From: Martin Mares Date: Tue, 18 Oct 2011 18:01:42 +0000 (+0200) Subject: Archivace starych kapitol, postupne vytvarim nove. Zatim 2-toky. X-Git-Url: http://mj.ucw.cz/gitweb/?a=commitdiff_plain;h=2d2944b3208696169024ccae79180605c4ab649b;p=ads2.git Archivace starych kapitol, postupne vytvarim nove. Zatim 2-toky. --- diff --git a/0-demo/0-demo.tex b/0-demo/0-demo.tex deleted file mode 100644 index bc5d7b7..0000000 --- a/0-demo/0-demo.tex +++ /dev/null @@ -1,37 +0,0 @@ -\input ../lecnotes.tex - -\prednaska{0}{Ukázka pou¾ití maker}{(zapsal Martin Mare¹)} - -% Takhle vypadá nadpis -\h{Pøedposlední kapitola} - -% \> zaøídí, aby se odstavec neodsadil. Pomocí \I se sází kurzíva, \foot vyrobí poznámku pod èarou. -\>Je svaèveèer. {\I Lysperní jezeleni} se vírnì vrtáèejí v~mokøavì.\foot{Viz Lewis Caroll: Jabberwocky.} - -\itemize\ibull -\:Takhle. -\:Vypadají. -\:Odrá¾ky. -\endlist - -\h{Druhá kapitola} - -\s{Vìta:} Paøez není strom. - -\proof -Strom je speciálním pøípadem lesa. Les je (podle definice) graf bez kru¾nic. -Paøez obsahuje alespoò jednu kru¾nici. Proto paøez nemù¾e být les, a~tedy ani -strom. -\qed - -\s{Algoritmus:} (tøídìní posloupnosti $a_1,\ldots,a_n$ pomocí {\sc Stupidsort}u) - -\algo -\:$\pi \leftarrow \hbox{identická permutace na~mno¾inì $\{1,\ldots,n\}$}$. -\:Opakuj: -\::Ovìø, zda je posloupnost $a_{\pi(1)},a_{\pi(2)},\ldots,a_{\pi(n)}$ uspoøádána vzestupnì. - Pokud je, vra» ji jako výsledek. -\::Nahraï $\pi$ jejím lexikografickým následníkem. -\endalgo - -\bye diff --git a/0-demo/Makefile b/0-demo/Makefile deleted file mode 100644 index 98def69..0000000 --- a/0-demo/Makefile +++ /dev/null @@ -1,3 +0,0 @@ -P=0-demo - -include ../Makerules diff --git a/1-toky/1-toky.tex b/1-toky/1-toky.tex deleted file mode 100644 index 126d81c..0000000 --- a/1-toky/1-toky.tex +++ /dev/null @@ -1,269 +0,0 @@ -\input lecnotes.tex - -\prednaska{1}{Toky v sítích}{(zapsala Markéta Popelová)} - -\s{První motivaèní úloha:} {\I Rozvod èajovodu do~v¹ech uèeben.} -\smallskip - -Pøedstavme si, ¾e~by v~budovì fakulty na~Malé Stranì existoval èajovod, který -by rozvádìl èaj do~ka¾dé uèebny. Znázornìme si to orientovaným grafem, kde by -jeden významný vrchol pøedstavoval èajovar a~druhý uèebnu, ve~které sedíme. -Hrany mezi vrcholy by pøedstavovaly vìtvící se trubky, které mají èaj rozvádìt. -Jak rozvést co nejefektivnìji dostatek èaje do~dané uèebny? - -\figure{toky01.eps}{Èajovod}{2in} - -\s{Druhá motivaèní úloha:} {\I Pøenos dat.} -\smallskip - -Jiným pøíkladem mù¾e být poèítaèová sí» na~pøenos dat, která se sestává z~pøenosových linek -spojených pomocí routerù. Data se sice obvykle pøená¹ejí po~paketech, ale to -mù¾eme pøi dne¹ních rychlostech pøenosu zanedbat a pova¾ovat data za spojitá. -Jak pøená¹et data mezi dvìma poèítaèi v~síti co nejrychleji? - -\s{Definice:} {\I Sí»} je uspoøádaná pìtice $(V,E,z,s,c)$, pro ní¾ platí: -\itemize\ibull -\:$(V,E)$ je orientovaný graf. -\:$c:E\to{\bb R}_{0}^{+}$ je {\I kapacita} hran. -\:$z,s \in V$ jsou dva vrcholy grafu, kterým øíkáme {\I zdroj} a~{\I stok} (spotøebiè). -\:Graf je symetrický, tedy $\forall u,v \in V: uv \in E \Leftrightarrow vu \in E$ (tuto podmínku si~mù¾eme zvolit bez~újmy na~obecnosti, nebo» v¾dy mù¾eme do~grafu pøidat hranu, která v~nìm je¹tì nebyla, a~dát jí nulovou kapacitu). -\endlist - -\figure{sit.eps}{Pøíklad sítì. Èísla pøedstavují kapacity jednotlivých hran.}{2.5in} - -\s{Definice:} {\I Tok} je funkce $f:E \to {\bb R}_{0}^{+}$ taková, ¾e~platí: -\numlist{\ndotted} -\:Tok po~ka¾dé hranì je omezen její kapacitou: $\forall e \in E : f(e)\le c(e)$. -\:Kirchhoffùv zákon: $$\forall v \in V \setminus \{z,s\}: \sum_{u: uv \in E}{f(uv)}=\sum_{u: vu \in E}{f(vu)}.$$ Neboli pro~ka¾dý vrchol kromì zdroje a~stoku platí, ¾e~to, co do~nìj pøitéká, je stejnì velké jako to, co z~nìj odtéká (\uv{sí» tìsní}). -\endlist - -\s{Definice:} Pro libovolnou funkci $f:E \to {\bb R}$ se nám bude hodit následující znaèení: -\itemize\ibull -\:$f^+(v) = \sum_{u: uv \in E}{f(uv)}$ (celkový pøítok do vrcholu) -\:$f^-(v) = \sum_{u: vu \in E}{f(vu)}$ (celkový odtok) -\:$f^\Delta(v) = f^+(v) - f^-(v)$ (pøebytek ve~vrcholu) -\endlist - -\>(Kirchhoffùv zákon pak øíká prostì to, ¾e $f^\Delta(v)=0$ pro v¹echna $v\ne z,s$.) - -\figure{tok.eps}{Pøíklad toku. Èísla pøedstavují toky po~hranách, v~závorkách jsou kapacity.}{4in} - -\s{Pozorování:} Nìjaký tok v¾dy existuje. V libovolné síti mù¾eme v¾dy zvolit -konstantnì nulovou funkci (po~¾ádné hranì nic nepoteèe). To je korektní tok, -ale sotva u¾iteèný. Budeme chtít najít tok, který pøepraví co nejvíce tekutiny -ze~zdroje do~spotøebièe. - -\s{Definice:} {\I Velikost toku} $f$ budeme znaèit $\vert f\vert$ a polo¾íme ji -rovnou rozdílu souètu velikostí toku na~hranách vedoucích do~$s$ a~souètu velikostí -toku na~hranách vedoucích z~$s$. Neboli $\vert f\vert:=f^\Delta(s).$ - -\s{Pozorování:} Jeliko¾ sí» tìsní, mìlo by být jedno, zda velikost toku mìøíme -u~spotøebièe nebo u~zdroje. Vskutku, krátkým výpoètem ovìøíme, ¾e tomu tak je: -$$ -f^\Delta(z) - f^\Delta(s) = \sum_v f^\Delta(v) = 0. -$$ -První rovnost platí proto, ¾e podle Kirchhoffova zákona jsou zdroj a spotøebiè jediné -dva vrcholy, jejich¾ pøebytek mù¾e být nenulový. Druhou rovnost získáme tak, ¾e si -uvìdomíme, ¾e tok na ka¾dé hranì pøispìje do celkové sumy jednou s~kladným znaménkem -a jednou se záporným. Zjistili jsme tedy, ¾e pøebytek zdroje a spotøebièe se li¹í -pouze znaménkem. -\qed - -\s{Poznámka:} Rádi bychom nalezli v~zadané síti tok, jeho¾ velikost je maximální. -Máme ale zaruèeno, ¾e maximum bude existovat? V¹ech mo¾ných tokù je nekoneènì mnoho -a v~nekoneèné mno¾inì se mù¾e snadno stát, ¾e aèkoliv existuje supremum, není maximem -(pøíklad: $\{1-1/n \mid n\in{\bb N}^+\}$). -Odpovìï nám poskytne matematická analýza: mno¾ina v¹ech tokù je kompaktní podmno¾inou -prostoru ${\bb R}^{\vert E\vert}$, velikost toku je spojitá (dokonce lineární) funkce -z~této mno¾iny do~$\bb R$, tak¾e musí nabývat minima i maxima. - -Nám ale bude staèít studovat sítì s~racionálními kapacitami, kde existence maximálního -toku bude zjevná u¾ z~toho, ze sestrojíme algoritmus, který takový tok najde. - -\s{První pokus:} Hledejme cestu $P$ ze~$z$ do~$s$ takovou, ¾e~$\forall e \in -P: f(e) < c(e)$ (po~v¹ech jejích hranách teèe ostøe ménì, ne¾ jim dovolují -jejich kapacity). Pak zjevnì mù¾eme tok upravit tak, aby se~jeho velikost -zvìt¹ila. Zvolme $$\varepsilon := \min_{e \in P} (c(e) - f(e)).$$ Nový tok $f'$ -pak definujme jako $f'(e):=f(e) + \varepsilon$. Kapacity nepøekroèíme ($\varepsilon$ -je nejvìt¹í mo¾ná hodnota, abychom tok zvìt¹ili, ale nepøekroèili kapacitu ani -jedné z~hran cesty $P$) a~Kirchhoffovy zákony zùstanou neporu¹eny, nebo» zdroj -a~stok neomezují a~ka¾dému jinému vrcholu na~cestì $P$ se~pøítok $f^+(v)$ -i~odtok $f^-(v)$ zvìt¹í pøesnì o~$\varepsilon$. - -Opakujme tento proces tak dlouho, dokud existují zlep¹ující cesty. A¾ se algoritmus -zastaví (co¾ by obecnì nemusel, ale nás je¹tì chvíli trápit nemusí), získáme maximální tok? -Pøekvapivì nemusíme. Napø. na~obrázku je vidìt, ¾e~kdy¾ najdeme nejdøíve cestu -pøes hranu s~kapacitou 1 (na obrázku tuènì) a~u¾ hodnotu toku na~této hranì -nesní¾íme, tak dosáhneme velikost toku nejvý¹e 19. Ale maximální tok této sítì -má velikost 20. - -\figure{toky02.eps}{Èísla pøedstavují kapacity jednotlivých hran.}{1.5in} - -Zde by ov¹em situaci zachránilo, kdybychom poslali tok velikosti 1 proti smìru -prostøední hrany -- to mù¾eme udìlat tøeba odeètením jednièky od toku po smìru -hrany. Roz¹íøíme tedy ná¹ algoritmus tak, aby umìl posílat tok i proti smìru -hran. O~kolik mù¾eme tok hranou zlep¹it (a» u¾ pøiètením po~smìru nebo odeètením -proti smìru) nám bude øíkat její {\I rezerva:} - -\s{Definice:} {\I Rezerva hrany} $uv$ je $r(uv):=c(uv) - f(uv) + f(vu).$ - -\smallskip -Algoritmus bude vypadat následovnì. Postupnì dok¾eme, ¾e je koneèný a ¾e v~ka¾dé -síti najde maximální tok. - -\s{Algoritmus (Fordùv-Fulkersonùv)} - -\algo -\:$f \leftarrow$ libovolný tok, napø. v¹ude nulový. -\:Dokud $\exists P$ cesta ze $z$ do $s$ taková, ¾e~$\forall e \in P: r(e) > 0$, opakujeme: -\::$\varepsilon \leftarrow \min \{r(e) \mid e \in P\}$. -\::Pro v¹echny hrany $uv \in P$: -\:::$\delta \leftarrow \min \{f(vu),\varepsilon\}$ -\:::$f(vu) \leftarrow f(vu) - \delta$ -\:::$f(uv) \leftarrow f(uv) + \varepsilon - \delta$ -\:Prohlásíme $f$ za~maximální tok. -\endalgo - -\s{Koneènost:} Zastaví se~Fordùv-Fulkersonùv algoritmus? - -\itemize\ibull - -\:Pro~celoèíselné kapacity se~v~ka¾dém kroku zvìt¹í velikost toku alespoò o~1. -Algoritmus se~tedy zastaví po~nejvíce tolika krocích, kolik je nìjaká horní -závora pro~velikost maximálního toku -- napø. souèet kapacit v¹ech hran -vedoucích do~stoku (tedy $c^+(s)$). - -\:Pro~racionální kapacity vyu¾ijeme jednoduchý trik. Nech» $M$ je nejmen¹í -spoleèný násobek jmenovatelù v¹ech kapacit. Spustíme-li algoritmus na sí» -s~kapacitami $c'(e) = c(e)\cdot M$, bude se rozhodovat stejnì jako v~pùvodní -síti, proto¾e bude stále platit $f'(e) = f(e)\cdot M$. Nová sí» je pøitom -celoèíselná, tak¾e se algoritmus jistì zastaví. - -\:Na~síti s~iracionálními kapacitami se~algoritmus chová mnohdy divoce, nemusí -se~zastavit, ba ani konvergovat ke~správnému výsledku. (Zkuste vymyslet pøíklad -takové sítì.) - -\endlist - -\s{Maximalita:} Kdy¾ se algoritmus zastaví, je tok~$f$ maximální? K~tomu se -bude hodit zavést øezy. - -\s{Definice:} {\I Øez} je uspoøádaná dvojice mno¾in vrcholù ($A,B$) taková, ¾e -$A$ a $B$ jsou disjunktní, pokrývají v¹echny vrcholy, $A$ obsahuje zdroj a $B$ -obsahuje stok. Neboli $A \cap B = \emptyset$, $A \cup B = V$, $z \in A$, $s \in B$. - -\>Ka¾dému øezu pøirozenì pøiøadíme mno¾iny hran: -\itemize\ibull -\:$E^+(A,B) = E \cap (A\times B)$ (hrany \uv{zleva doprava}) -\:$E^-(A,B) = E \cap (B\times A)$ (hrany \uv{zprava doleva}) -\:$E^\Delta(A,B) = E^+(A,B) \cup E^-(A,B)$ (v¹echny hrany øezu) -\endlist - -\>Také pro libovolnou funkci $f: E\rightarrow {\bb R}$ zavedeme: -\itemize\ibull -\:$f^+(A,B) = \sum_{e\in E^+(A,B)} f(e)$ (prùtok pøes øez zleva doprava) -\:$f^-(A,B) = \sum_{e\in E^-(A,B)} f(e)$ (prùtok zprava doleva) -\:$f^\Delta(A,B) = f^+(A,B) - f^-(A,B)$ (èistý prùtok) -\endlist - -\>{\I Kapacita øezu} budeme øíkat souètu kapacit hran zleva doprava, tedy $c+(A,B)$. - -\s{Poznámka:} Øezy se~dají definovat více zpùsoby, jedna z~definic je, ¾e~øez -je mno¾ina hran grafu takových, ¾e~po~jejich odebrání se~graf rozpadne na~více -komponent. Tuto vlastnost mají i na¹e øezy, ale opaènì to nemusí platit. - -\s{Lemma:} Pro ka¾dý øez $(A,B)$ a ka¾dý tok~$f$ platí, ¾e $f^\Delta(A,B) -= \vert f\vert$. (Jinými slovy velikost toku mù¾eme mìøit na libovolném øezu, -nejen na triviálních øezech kolem zdroje nebo kolem spotøebièe.) - -\proof -Opìt ¹ikovným seètením pøebytkù vrcholù: -$$ -f^\Delta(A,B) = \sum_{v\in B} f^\Delta(v) = f^\Delta(s). -$$ -První rovnost získáme poèítáním pøes hrany: ka¾dá hrana vedoucí z~vrcholu v~$B$ -do~jiného vrcholu v~$B$ pøispìje jednou kladnì a jednou zápornì; hrany le¾ící -celé mimo~$B$ nepøispìjí vùbec; hrany s~jedním koncem v~$B$ a druhým mimo pøispìjí -jednou, pøièem¾ znaménko se bude li¹it podle toho, který konec je v~$B$. Druhá -rovnost je snadná: v¹echny vrcholy v~$B$ mimo spotøebièe mají podle Kirchhoffova -zákona nulový pøebytek. -\qed - -\s{Dùsledek:} Pro ka¾dý tok~$f$ a ka¾dý øez $(A,B)$ platí $\vert f \vert \le c^+(A,B)$. -(Velikost ka¾dého toku je shora omezena kapacitou ka¾dého øezu.) - -\proof -$f^\Delta(A,B) = f^+(A,B) - f^-(A,B) \le f^+(A,B) \le c^+(A,B)$. -\qed - -\s{Dùsledek:} Pokud $\vert f\vert = c^+(A,B)$, pak je tok~$f$ maximální a øez~$(A,B)$ -minimální. Jinými slovy pokud najdeme dvojici tok a stejnì velký øez, mù¾eme øez pou¾ít -jako certifikát maximality toku. Následující vìta nám zaruèí, ¾e je to mo¾né v¾dy: - -\s{Vìta:} Pokud se~Fordùv-Fulkersonùv algoritmus zastaví, tak vydá maximální tok. - -\proof -Nech» se~Fordùv-Fulkersonùv algoritmus zastaví. Definujme mno¾inu vrcholù $A -:= \{v \in V \mid \hbox{existuje cesta ze~$z$ do~$v$ jdoucí po~hranách s~$r -> 0$}\}$ a~$B := V \setminus A$. - -Dvojice $(A,B)$ je øez, nebo» $z \in A$ (ze~$z$ do~$z$ existuje cesta délky 0) -a~$s \in B$ (kdyby $s \not\in B$, tak by musela existovat cesta ze~$z$ do~$s$ -s~kladnou rezervou, tudí¾ by algoritmus neskonèil, nýbr¾ tuto cestu vzal -a~stávající tok vylep¹il). - -Dále víme, ¾e~v¹echny hrany øezu mají nulovou rezervu, èili $\forall uv \in -E^+(A,B) : r(uv) = 0$ (kdyby mìla hrana $uv$ rezervu nenulovou, tedy kladnou, -tak by vrchol $v$ patøil do~$A$). Proto po~v¹ech hranách øezu vedoucích z~$A$ -do~$B$ teèe tolik, kolik jsou kapacity tìchto hran, a~po~hranách vedoucích -z~$B$ do~$A$ neteèe nic, tedy $f(uv) = c(uv)$ a $f(vu) = 0$. Máme øez $(A,B)$ -takový, ¾e~$f^\Delta(A,B) = c^+(A,B)$. To znamená, ¾e~jsme na¹li maximální tok -a~minimální øez. \qed - -Dokázali jsme tedy následující: - -\s{Vìta:} Pro~sí» s~racionálními kapacitami se~Fordùv-Fulkersonùv algoritmus -zastaví a~vydá maximální tok a~minimální øez. - -\s{Vìta:} Sí» s~celoèíselnými kapacitami má aspoò jeden z~maximálních tokù -celoèíselný a~Fordùv-Fulkersonùv algoritmus takový tok najde. - -\proof -Kdy¾ dostane Fordùv-Fulkersonùv algoritmus celoèíselnou sí», tak najde maximální tok a~ten bude zase celoèíselný (algoritmus nikde nedìlí). -\qed - -To, ¾e~umíme najít celoèíselné øe¹ení není úplnì samozøejmé. (U~jiných problémù takové ¹tìstí mít nebudeme.) Uka¾me si rovnou jednu aplikaci, která právì celoèíselný tok vyu¾ije. - -\s{Aplikace:} Hledání nejvìt¹ího párování v~bipartitních grafech. - -\s{Definice:} Mno¾ina hran $F \subseteq E$ se~nazývá {\I párování}, jestli¾e -¾ádné dvì hrany této mno¾iny nemají spoleèný ani jeden vrchol. Neboli $\forall -e,f \in F : e \cap f = \emptyset$. {\I Velikostí} párování myslíme poèet jeho -hran. - -\s{Øe¹ení:} -Mìjme bipartitní graf $G = (V,E)$. V~nìm hledáme nejvìt¹í párování. Sestrojme -si~sí» takovou, ¾e~vezmeme vrcholy $V$ grafu $G$ a~pøidáme k~nim dva speciální -vrcholy $z$ (zdroj) a~$s$ (stok) a~ze~zdroje pøidáme hrany do~v¹ech vrcholù -levé partity a~ze~v¹ech vrcholù pravé partity povedeme hrany do~stoku. V¹echny -kapacity nastavme na~1. Hrany bipartitního grafu zorientujme z levé partity do -pravé. Nyní staèí jen na~tuto sí» spustit Fordùv-Fulkersonùv algoritmus (nebo -libovolný jiný algoritmus, který najde maximální celoèíselný tok) a~a¾~dobìhne, -tak prohlásit hrany s~tokem 1 za~maximální párování. - -\figure{toky04.eps}{Hledání maximálního párování v~bipartitním grafu.}{2in} - -Existuje toti¾ bijekce mezi párováním a~celoèíselnými toky pøi~zachování -velikosti. Z ka¾dého celoèíselného toku na~vý¹e zmínìném grafu (viz obrázek) lze sestrojit -párování o~stejné velikosti (velikost toku zde odpovídá poètu hran bipartitního -grafu, po~kterých poteèe 1) a~naopak. Dùle¾ité je si uvìdomit, ¾e~definice toku -(omezení toku kapacitou a~Kirchhoffovy zákony) nám zaruèují, ¾e~hrany -s~nenulovým tokem (tedy jednièkovým) budou tvoøit párování (nestane se, ¾e~by -dvì hrany zaèínaly nebo konèily ve~stejném vrcholu, nebo» by se~nutnì poru¹ila -jedna ze~dvou podmínek definice toku). Potom i~maximální tok bude odpovídat -maximálnímu párování a~naopak. - -V~bipartitním grafu najdeme maximální párování v~èase $\O(n \cdot (m+n))$. Fordùv-Fulkersonùv algoritmus stráví jednou iterací èas $\O(m+n)$ (za~prohledání do~¹íøky) a~pøi~jednotkových kapacitách bude iterací nejvý¹e~$n$, proto¾e ka¾dou se~tok zvìt¹í alespoò o~1 a v¹echny toky jsou omezené øezem kolem zdroje, který má kapacitu nejvý¹e~$n$. Výsledná èasová slo¾tost hledání maximálního párování bude tedy $\O(n \cdot (m+n))$. - - -\bye diff --git a/1-toky/Makefile b/1-toky/Makefile deleted file mode 100644 index e82416b..0000000 --- a/1-toky/Makefile +++ /dev/null @@ -1,3 +0,0 @@ -P=1-toky - -include ../Makerules diff --git a/1-toky/sit.eps b/1-toky/sit.eps deleted file mode 100644 index f66f5e1..0000000 --- a/1-toky/sit.eps +++ /dev/null @@ -1,828 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Creator: Ipelib 60027 (Ipe 6.0 preview 27) -%%CreationDate: D:20071018214606 -%%LanguageLevel: 2 -%%BoundingBox: 107 284 460 489 -%%HiResBoundingBox: 107.459 284.641 459.645 488.218 -%%DocumentSuppliedResources: font OXRFMQ+CMR10 -%%+ font GKLBST+CMR12 -%%+ font OKRINM+CMMI12 -%%+ font PVGEOP+CMR17 -%%EndComments -%%BeginProlog -%%BeginResource: procset ipe 6.0 60027 -/ipe 40 dict def ipe begin -/np { newpath } def -/m { moveto } def -/l { lineto } def -/c { curveto } def -/h { closepath } def -/re { 4 2 roll moveto 1 index 0 rlineto 0 exch rlineto - neg 0 rlineto closepath } def -/d { setdash } def -/w { setlinewidth } def -/J { setlinecap } def -/j { setlinejoin } def -/cm { [ 7 1 roll ] concat } def -/q { gsave } def -/Q { grestore } def -/g { setgray } def -/G { setgray } def -/rg { setrgbcolor } def -/RG { setrgbcolor } def -/S { stroke } def -/f* { eofill } def -/f { fill } def -/ipeMakeFont { - exch findfont - dup length dict begin - { 1 index /FID ne { def } { pop pop } ifelse } forall - /Encoding exch def - currentdict - end - definefont pop -} def -/ipeFontSize 0 def -/Tf { dup /ipeFontSize exch store selectfont } def -/Td { translate } def -/BT { gsave } def -/ET { grestore } def -/TJ { 0 0 moveto { dup type /stringtype eq - { show } { ipeFontSize mul -0.001 mul 0 rmoveto } ifelse -} forall } def -end -%%EndResource -%%EndProlog -%%BeginSetup -ipe begin -%%BeginResource: font OXRFMQ+CMR10 -%!PS-AdobeFont-1.1: CMR10 1.00B -%%CreationDate: 1992 Feb 19 19:54:52 -% Copyright (C) 1997 American Mathematical Society. All Rights Reserved. -11 dict begin -/FontInfo 7 dict dup begin -/version (1.00B) readonly def -/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def -/FullName (CMR10) readonly def -/FamilyName (Computer Modern) readonly def -/Weight (Medium) readonly def -/ItalicAngle 0 def -/isFixedPitch false def -end readonly def -/FontName /OXRFMQ+CMR10 def -/PaintType 0 def -/FontType 1 def -/FontMatrix [0.001 0 0 0.001 0 0] readonly def -/Encoding 256 array -0 1 255 {1 index exch /.notdef put} for -dup 49 /one put -dup 50 /two put -readonly def -/FontBBox{-251 -250 1009 969}readonly def -currentdict end -currentfile eexec -d9d66f633b846a97b686a97e45a3d0aa052a014267b7904eb3c0d3bd0b83d891 -016ca6ca4b712adeb258faab9a130ee605e61f77fc1b738abc7c51cd46ef8171 -9098d5fee67660e69a7ab91b58f29a4d79e57022f783eb0fbbb6d4f4ec35014f -d2decba99459a4c59df0c6eba150284454e707dc2100c15b76b4c19b84363758 -469a6c558785b226332152109871a9883487dd7710949204ddcf837e6a8708b8 -2bdbf16fbc7512faa308a093fe5cf7158f1163bc1f3352e22a1452e73feca8a4 -87100fb1ffc4c8af409b2067537220e605da0852ca49839e1386af9d7a1a455f -d1f017ce45884d76ef2cb9bc5821fd25365ddea6e45f332b5f68a44ad8a530f0 -92a36fac8d27f9087afeea2096f839a2bc4b937f24e080ef7c0f9374a18d565c -295a05210db96a23175ac59a9bd0147a310ef49c551a417e0a22703f94ff7b75 -409a5d417da6730a69e310fa6a4229fc7e4f620b0fc4c63c50e99e179eb51e4c -4bc45217722f1e8e40f1e1428e792eafe05c5a50d38c52114dfcd24d54027cbf -2512dd116f0463de4052a7ad53b641a27e81e481947884ce35661b49153fa19e -0a2a860c7b61558671303de6ae06a80e4e450e17067676e6bbb42a9a24acbc3e -b0ca7b7a3bfea84fed39ccfb6d545bb2bcc49e5e16976407ab9d94556cd4f008 -24ef579b6800b6dc3aaf840b3fc6822872368e3b4274dd06ca36af8f6346c11b -43c772cc242f3b212c4bd7018d71a1a74c9a94ed0093a5fb6557f4e0751047af -d72098eca301b8ae68110f983796e581f106144951df5b750432a230fda3b575 -5a38b5e7972aabc12306a01a99fcf8189d71b8dbf49550baea9cf1b97cbfc7cc -96498ecc938b1a1710b670657de923a659db8757147b140a48067328e7e3f9c3 -7d1888b284904301450ce0bc15eeea00e48ccd6388f3fc3c8578ef9a20a0e06e -4f7addaf0e7d1e182d115bf1ad931977325ad391e72e2b13cc108e3726c11099 -e2000623188aaac9f3e233eb253bdd8b0a4759a66a113e066238b0086ac1b634 -5abff90e4b5ed3fa69c22541981b2bfc9710aef6b50a8bb53431c7b4d380d721 -639e005d6b4688ee16bff48443e7c9e5fb5bc5883e271cb034289232a0694cce -12a5a2637485fb47bc281a213666c9859e580ce59cfdec9be4b40398e2b84425 -3cf8a27af81a2fb17bb5f71213554c91fac93265ecfe7b38a3192e902d480d53 -bcaddee6127ff16199e331ce7be6f13812c0fa7ff243b5d0105f4f07d605d87d -a5aeaee835c37d3e277fef4d376966485d96fed9708999732b178ad19ddf12ec -ebdf4b6f176b85943e397161f9b5dcb8554cfbcb3e187957d49ca175a0b6244b -b5ffc53201730723ddaebc48f55be423b0fde425b23b9d210d2fc630f6fa7b11 -d96c138d9c418650741c1729297e7e09e8f4060310cb49400425b80c78083787 -5d5ce70d69604f6575a831f2d36e3b11788857535a045905b2357597063596e1 -47ba6cd16aadf911f004cda264527b6c3ede9edf6697eab1fcf6c5420256f322 -25d745196e5786eef2471c8425f7bfa9ebe568aefec6003ca2729657c245c5ef -03fcdd889be356ddd6bce8ac2e56ff6738102dec18300604bbecf042efc89cb2 -54d6fc32ba6c9437d0847bdd76b10e14e2464c45b11f09436a6b952cfa84e9fc -75b39f4455f0 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -cleartomark -%%EndResource -/F8 /OXRFMQ+CMR10 -[ /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - 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/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef ] -ipeMakeFont -%%BeginResource: font GKLBST+CMR12 -%!PS-AdobeFont-1.1: CMR12 1.0 -%%CreationDate: 1991 Aug 20 16:38:05 -% Copyright (C) 1997 American Mathematical Society. All Rights Reserved. -11 dict begin -/FontInfo 7 dict dup begin -/version (1.0) readonly def -/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def -/FullName (CMR12) readonly def -/FamilyName (Computer Modern) readonly def -/Weight (Medium) readonly def -/ItalicAngle 0 def -/isFixedPitch false def -end readonly def -/FontName /GKLBST+CMR12 def -/PaintType 0 def -/FontType 1 def -/FontMatrix [0.001 0 0 0.001 0 0] readonly def -/Encoding 256 array -0 1 255 {1 index exch /.notdef put} for -dup 50 /two put -dup 51 /three put -dup 52 /four put -dup 53 /five put -dup 54 /six put -dup 56 /eight put -dup 57 /nine put -readonly def -/FontBBox{-34 -251 988 750}readonly def -currentdict end -currentfile eexec -d9d66f633b846a97b686a97e45a3d0aa052a014267b7904eb3c0d3bd0b83d891 -016ca6ca4b712adeb258faab9a130ee605e61f77fc1b738abc7c51cd46ef8171 -9098d5fee67660e69a7ab91b58f29a4d79e57022f783eb0fbbb6d4f4ec35014f -d2decba99459a4c59df0c6eba150284454e707dc2100c15b76b4c19b84363758 -469a6c558785b226332152109871a9883487dd7710949204ddcf837e6a8708b8 -2bdbf16fbc7512faa308a093fe5cf4e9d2405b169cd5365d6eced5d768d66d6c 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Copyright (C) 1997 American Mathematical Society. All Rights Reserved. -11 dict begin -/FontInfo 7 dict dup begin -/version (1.100) readonly def -/Notice (Copyright (C) 1997 American Mathematical Society. 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All Rights Reserved. -11 dict begin -/FontInfo 7 dict dup begin -/version (1.0) readonly def -/Notice (Copyright (C) 1997 American Mathematical Society. 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All Rights Reserved. -11 dict begin -/FontInfo 7 dict dup begin -/version (1.100) readonly def -/Notice (Copyright (C) 1997 American Mathematical Society. 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/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef ] -ipeMakeFont -%%BeginResource: font QRHILV+CMSY10 -%!PS-AdobeFont-1.1: CMSY10 1.0 -%%CreationDate: 1991 Aug 15 07:20:57 -% Copyright (C) 1997 American Mathematical Society. All Rights Reserved. -11 dict begin -/FontInfo 7 dict dup begin -/version (1.0) readonly def -/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def -/FullName (CMSY10) readonly def -/FamilyName (Computer Modern) readonly def -/Weight (Medium) readonly def -/ItalicAngle -14.035 def -/isFixedPitch false def -end readonly def -/FontName /QRHILV+CMSY10 def -/PaintType 0 def -/FontType 1 def -/FontMatrix [0.001 0 0 0.001 0 0] readonly def -/Encoding 256 array -0 1 255 {1 index exch /.notdef put} for -dup 0 /minus put -dup 112 /radical put -readonly def -/FontBBox{-29 -960 1116 775}readonly def -currentdict end -currentfile eexec -d9d66f633b846a97b686a97e45a3d0aa052f09f9c8ade9d907c058b87e9b6964 -7d53359e51216774a4eaa1e2b58ec3176bd1184a633b951372b4198d4e8c5ef4 -a213acb58aa0a658908035bf2ed8531779838a960dfe2b27ea49c37156989c85 -e21b3abf72e39a89232cd9f4237fc80c9e64e8425aa3bef7ded60b122a52922a -221a37d9a807dd01161779dde7d31ff2b87f97c73d63eecdda4c49501773468a -27d1663e0b62f461f6e40a5d6676d1d12b51e641c1d4e8e2771864fc104f8cbf -5b78ec1d88228725f1c453a678f58a7e1b7bd7ca700717d288eb8da1f57c4f09 -0abf1d42c5ddd0c384c7e22f8f8047be1d4c1cc8e33368fb1ac82b4e96146730 -de3302b2e6b819cb6ae455b1af3187ffe8071aa57ef8a6616b9cb7941d44ec7a -71a7bb3df755178d7d2e4bb69859efa4bbc30bd6bb1531133fd4d9438ff99f09 -4ecc068a324d75b5f696b8688eeb2f17e5ed34ccd6d047a4e3806d000c199d7c -515db70a8d4f6146fe068dc1e5de8bc57036431151ec603c8bcfe359bbd953ad -5f3d9983b036d9202c8fcc4fa88af960e1e49914ec809263862931db14b61eee -6d37a389b488d0b64cfb7da527aaed80494f79a073d895aa287bb47bd5246090 -a76ce91680c1f37e75a8804089ca4d83dbc5044e1eb9714257808bdc7df9d0b5 -fe7274d571b4b44f2e2d98e11b5cf85379f57db353ad8bca58c7047bca299c87 -a55b0066590a3136d60aebcbaeada5b3d618b68c0be7234fe688e597b2706fc4 -2f825d94ca7fea7c6b2c7e3837f5dc05986f4a5c38123e3af71cd2c0332e5a6d -5b89f35f7573055fa88425ac6992079f19b24cc22876fb11fc33bfe08990ebfb -943dcba682e4327772ae01a440c60ba4edcbd5f51e248b34dca93fbd3c560ab6 -fb4110271e9cfc5424d2ee2541071e7946d71d00d7e7fdaffe2f5eff45fd84b0 -c5d3765aac65c022989bcff5621476459905003712cdf63941655009a7b5ed3d -707c641e3e63c7476e88cf3b3247 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -0000000000000000000000000000000000000000000000000000000000000000 -cleartomark -%%EndResource -/F17 /QRHILV+CMSY10 -[ /minus/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /radical/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef - /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef ] -ipeMakeFont -%%BeginResource: font TLUUTQ+CMR17 -%!PS-AdobeFont-1.1: CMR17 1.0 -%%CreationDate: 1991 Aug 20 16:38:24 -% Copyright (C) 1997 American Mathematical Society. All Rights Reserved. -11 dict begin -/FontInfo 7 dict dup begin -/version (1.0) readonly def -/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def -/FullName (CMR17) readonly def -/FamilyName (Computer Modern) readonly def -/Weight (Medium) readonly def -/ItalicAngle 0 def -/isFixedPitch false def -end readonly def -/FontName /TLUUTQ+CMR17 def -/PaintType 0 def -/FontType 1 def -/FontMatrix [0.001 0 0 0.001 0 0] readonly def -/Encoding 256 array -0 1 255 {1 index exch /.notdef put} for -dup 40 /parenleft put -dup 41 /parenright put -dup 43 /plus put -dup 61 /equal put -dup 115 /s put -dup 122 /z put -readonly def -/FontBBox{-33 -250 945 749}readonly def -currentdict end -currentfile eexec -d9d66f633b846a97b686a97e45a3d0aa052a014267b7904eb3c0d3bd0b83d891 -016ca6ca4b712adeb258faab9a130ee605e61f77fc1b738abc7c51cd46ef8171 -9098d5fee67660e69a7ab91b58f29a4d79e57022f783eb0fbbb6d4f4ec35014f -d2decba99459a4c59df0c6eba150284454e707dc2100c15b76b4c19b84363758 -469a6c558785b226332152109871a9883487dd7710949204ddcf837e6a8708b8 -2bdbf16fbc7512faa308a093fe5f075ea0a10a15b0ed05d5039da41b32b16e95 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cm 1 0 0 1 0 0 cm 1 0 0 1 0 3.5865 cm -0 g -0 G - -1 0 0 1 0 -781.2403 cm -BT -/F16 14.3462 Tf 0 781.2403 Td[(\031)]TJ/F15 14.3462 Tf 13.1655 0 Td[(\0502)]TJ/F16 14.3462 Tf 12.4863 0 Td[(\031)]TJ/F15 14.3462 Tf 8.4832 0 Td[(\051)]TJ -ET -Q -q 1 0 0 1 269.991 471.39 cm 1 0 0 1 0 0 cm 1 0 0 1 0 3.5865 cm -0 g -0 G - -1 0 0 1 0 -781.2403 cm -BT -/F16 14.3462 Tf 0 781.2403 Td[(\031)]TJ/F15 14.3462 Tf 13.1655 0 Td[(\050)]TJ/F16 14.3462 Tf 5.4628 0 Td[(\031)]TJ/F15 14.3462 Tf 8.4831 0 Td[(\051)]TJ -ET -Q -q 1 0 0 1 399.725 423.941 cm 1 0 0 1 0 0 cm 1 0 0 1 0 3.5865 cm -0 g -0 G - -1 0 0 1 0 -779.0944 cm -BT -/F16 14.3462 Tf 0 779.0944 Td[(\031)]TJ/F15 14.3462 Tf 11.6711 0 Td[(+)]TJ/F17 14.3462 Tf 14.1136 11.7579 Td[(p)]TJ -ET -1 0 0 1 37.7399 790.8523 cm -q -[]0 d -0 J -0.5738 w -0 0.2869 m -7.0236 0.2869 l -S -Q -1 0 0 1 -37.7399 -790.8523 cm -BT -/F15 14.3462 Tf 37.7399 779.0944 Td[(2)-326(\0506)1(\051)]TJ -ET -Q -q 1 0 0 1 250.464 417.533 cm 1 0 0 1 0 0 cm 1 0 0 1 0 3.5865 cm -0 g -0 G - 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9608efe..0000000 --- a/1-toky/toky04.svg +++ /dev/null @@ -1,349 +0,0 @@ - - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - - z - s - - - diff --git a/10-prevody/10-prevody.tex b/10-prevody/10-prevody.tex deleted file mode 100644 index 070d102..0000000 --- a/10-prevody/10-prevody.tex +++ /dev/null @@ -1,282 +0,0 @@ -\input lecnotes.tex - -\prednaska{10}{Pøevody problémù}{\vbox{\hbox{(zapsali Martin Chytil, Vladimír Kudelas,}\hbox{ Michal Kozák, Vojta Tùma)}}} - -\>Na této pøedná¹ce se budeme zabývat rozhodovacími problémy a jejich obtí¾ností. -Za jednoduché budeme trochu zjednodu¹enì pova¾ovat ty problémy, na~nì¾ známe algoritmus -pracující v~polynomiálním èase. - -\s{Definice:} {\I Rozhodovací problém} je takový problém, jeho¾ výstupem je v¾dy {\sc ano}, nebo {\sc ne}. -[Formálnì bychom se na~nìj mohli dívat jako na~mno¾inu $L$ vstupù, na~které je odpovìï {\sc ano}, -a místo $L(x)=\hbox{\sc ano}$ psát prostì $x\in L$.] - -Vstupy mìjme zakódované jen pomocí nul a jednièek (obecnì je jedno, jaký základ pro soustavu -kódování zvolíme, pøevody mezi soustavami o nìjakém základu $\neq$ 1 jsou co do velikosti -zápisu polynomiální). Rozhodovací problém je tedy -$f:\ \{0,1\}^{\ast} \to \{0,1\}$, to jest funkce z mno¾iny v¹ech øetìzcù jednièek a nul -do mno¾iny $\{1,0\}$, kde 1 na výstupu znamená {\sc ano}, 0 {\sc ne}. - -\s{Pøíklad:} Je dán bipartitní graf $G$ a $k \in {\bb N}$. Existuje v $G$ -párování, které obsahuje alespoò $k$ hran? - -To, co bychom ve~vìt¹inì pøípadù chtìli, je samozøejmì nejen zjistit, zda takové párování -existuje, ale také nìjaké konkrétní najít. V¹imnìme si ale, ¾e kdy¾ umíme rozhodovat -existenci párování v~polynomiálním èase, mù¾eme ho polynomiálnì rychle i najít: - -Mìjme èernou skøíòku (fungující v polynomiálním èase), která odpoví, zda daný -graf má nebo nemá párování o~$k$ hranách. Odebereme z~grafu libovolnou hranu -a zeptáme se, jestli i tento nový graf má párovaní velikosti~$k$. Kdy¾ má, pak tato -hrana nebyla pro existenci párování potøebná, a~tak ji odstraníme. Kdy¾ naopak -nemá (hrana patøí do ka¾dého párování po¾adované velikosti), tak si danou hranu -poznamenáme a odebereme nejen ji a její vrcholy, ale také hrany, které do tìchto -vrcholù vedly. Toto je korektní krok, proto¾e v pùvodním grafu tyto vrcholy -byly navzájem spárované, a tedy nemohou být spárované s~¾ádnými jinými vrcholy. -Na~nový graf aplikujeme znovu tentý¾ postup. Výsledkem je mno¾ina hran, které patøí -do hledaného párování. Hran, a tedy i iterací na¹eho algoritmu, je polynomiálnì -mnoho a skøíòka funguje v polynomiálním èase, tak¾e celý algoritmus je polynomiální. - -A~jak ná¹ rozhodovací problém øe¹it? Nejsnáze tak, ¾e ho pøevedeme -na~{jiný,\footnote{${}^{\dag}$}{vìrni matfyzáckým vtipùm}} který -u¾ vyøe¹it umíme. Tento postup jsme (právì u~hledání párování) u¾ pou¾ili -v~kapitole o~Dinicovì algoritmu. Vytvoøili jsme vhodnou sí», pro kterou -platilo, ¾e v~ní existuje tok velikosti~$k$ právì tehdy, kdy¾ -v~pùvodním grafu existuje párování velikosti~$k$. - -Takovéto pøevody mezi problémy mù¾eme definovat obecnì: - -\s{Definice:} Jsou-li $A$, $B$ rozhodovací problémy, pak øíkáme, ¾e $A$ lze {\I -redukovat} (neboli {\it pøevést}) na $B$ (pí¹eme $A \rightarrow B$) právì tehdy, -kdy¾ existuje funkce $f$ spoèitatelná v polynomiálním èase taková, ¾e pro $\forall -x: A(x) = B(f(x))$. V¹imnìme si, ¾e $f$ pracující v polynomiálním èase vstup -zvìt¹í nejvíce polynomiálnì. - -\s{Pozorování:} $A\rightarrow B$ také znamená, ¾e problém~$B$ je alespoò tak tì¾ký -jako problém~$A$ (tím myslíme, ¾e pokud lze $B$ øe¹it v~polynomiálním èase, -lze tak øe¹it i~$A$): Nech» problém~$B$ umíme øe¹it v~èase $\O(b^k)$, kde -$b$ je délka jeho vstupu. Nech» dále funkce $f$ pøevádìjící $A$ na $B$ pracuje -v~èase $\O(a^\ell)$ pro vstup délky~$a$. Spustíme-li tedy $B(f(x))$ na~nìjaký -vstup~$x$ problému~$A$, bude mít $f(x)$ délku $\O(a^\ell)$, kde $a=|q|$; tak¾e -$B(f(x))$ pobì¾í v~èase $\O(a^\ell + (a^\ell)^k) = \O(a^{k\ell})$, co¾ je -polynomiální v~délce vstupu~$a$. - - -\s{Pozorování:} Pøevoditelnost je -\itemize\ibull -\:reflexivní (úlohu mù¾eme pøevést na tu stejnou identickým zobrazením): $A \rightarrow A$, -\:tranzitivní: Je-li $A \rightarrow B$ funkcí $f$, $B \rightarrow C$ funkcí $g$, -pak $A \rightarrow C$ slo¾enou funkcí $g \circ f$ -(slo¾ení dvou polynomiálních funkcí je zase polynomiální funkce, jak u¾ jsme zpozorovali -v~pøedchozím odstavci). -\endlist -\>Takovýmto relacím øíkáme kvaziuspoøádání -- nesplòují obecnì antisymetrii, tedy mù¾e nastat -$A\rightarrow B$ a $B\rightarrow A$. Omezíme-li se v¹ak na tøídy navzájem pøevoditelných -problémù, dostáváme ji¾ (èásteèné) uspoøádání. Existují i navzájem nepøevoditelné problémy -- -napøíklad problém v¾dy odpovídající 1 a problém v¾dy odpovídající 0. -Nyní se ji¾ podíváme na nìjaké zajímavé problémy. Obecnì to budou problémy, na které -polynomiální algoritmus není znám, a vzájemnými pøevody zjistíme ¾e jsou stejnì tì¾ké. - -\h{1. problém: SAT} -\>Splnitelnost (satisfiability) logických formulí, tj. dosazení 1 èi -0 za promìnné v logické formuli tak, aby formule dala výsledek 1. - -\>Zamìøíme se na speciální formu zadání formulí, {\I konjunktivní normální formu} (CNF), - které splòují následující podmínky: -\itemize\ibull -\:{\I formule} je zadána pomocí {\I klauzulí}\footnote{${}^{\dag}$}{bez politických konotací} oddìlených $\land$, -\:ka¾dá {\I klauzule} je slo¾ená z {\I literálù} oddìlených $\lor$, -\:ka¾dý {\I literál} je buïto promìnná nebo její negace. -\endlist -\>Formule mají tedy tvar: -$$\psi = (\ldots\lor\ldots\lor\ldots\lor\ldots) \land (\ldots\lor\ldots\lor\ldots\lor\ldots) \land \ldots $$ - -\>{\I Vstup:} Formule $\psi$ v konjunktivní normální formì. - -\>{\I Výstup:} $\exists$ dosazení 1 a 0 za promìnné takové, ¾e hodnota formule $\psi(\ldots) = 1$. - - - -\>Pøevod nìjaké obecné formule $\psi$ na jí ekvivalentní $\chi$ v~CNF mù¾e -zpùsobit, ¾e $\chi$ je exponenciálnì velká vùèi $\psi$. -Pozdìji uká¾eme, ¾e lze podniknout pøevod na takovou formuli $\chi'$ v~CNF, která sice není -ekvivalentní s $\psi$ (pøibydou nám promìnné, a ne ka¾dý roz¹íøený model -$\psi$ je modelem $\chi'$), ale je splnitelná právì tehdy, kdy¾ je splnitelná $\psi$ -- co¾ nám -pøesnì staèí -- a je lineárnì velká vùèi $\psi$. - -\h{2. problém: 3-SAT} -\s{Definice:} 3-SAT je takový SAT, v nìm¾ ka¾dá klauzule obsahuje nejvý¹e tøi literály. - -\s{Pøevod 3-SAT na SAT:} -Vstup není potøeba nijak upravovat, 3-SAT splòuje vlastnosti SATu, proto 3-SAT -$\rightarrow$ SAT (SAT je alespoò tak tì¾ký jako 3-SAT) - -\s {Pøevod SAT na 3-SAT:} -Musíme formuli pøevést tak, abychom neporu¹ili splnitelnost. - -\>Trik pro dlouhé klauzule: Ka¾dou \uv{¹patnou} klauzuli -$$(\alpha \lor \beta) \hbox{, t¾. } \vert\alpha\vert + \vert\beta\vert \ge 4 -,\ \vert\alpha\vert \geq 2,\ \vert\beta\vert\geq 2$$ -pøepí¹eme na: $$(\alpha \lor x) \land (\beta \lor \lnot x),$$ -kde $x$ je nová promìnná (pøi ka¾dém dìlení klauzule {\it jiná} -nová promìnná). -%kterou nastavíme tak, abychom neovlivnili splnitelnost formule. - -\>Tento trik opakujeme tak dlouho, dokud je to tøeba -- formuli délky $k+l$ -roztrhneme na formule délky $k+1$ a $l+1$. Pokud klauzule pùlíme, dostaneme -polynomiální èas (strom rekurze má logaritmicky pater -- formule délky alespoò 6 -se nám pøi rozdìlení zmen¹í na dvì instance velikosti maximálnì $2/3$ pùvodní, krat¹í -formule nás netrápí; na ka¾dém patøe se vykoná tolik co na pøedchozím + $2^{hloubka}$ -za pøidané formule). Velikost výsledné formule je tím pádem polynomiální vùèi pùvodní: -v ka¾dém kroku se pøidají jen dva literály, tedy celkem {\it èas na pøevod}$\cdot -2$ nových. - -\>Platí-li: -\itemize\ibull -\:$\alpha \Rightarrow$ zvolíme $x = 0$ (zajistí splnìní druhé poloviny nové formule), -\:$\beta \Rightarrow$ zvolíme $x = 1$ (zajistí splnìní první poloviny nové formule), -\:$\alpha ,\beta / \lnot\alpha ,\lnot\beta \Rightarrow$ zvolíme $x = 0/1$ (je nám to - jedno, celkové øe¹ení nám to neovlivní). -\endlist - -Nabízí se otázka, proè mù¾eme pøidanou promìnnou $x$ nastavovat, jak se nám zlíbí. -Vysvìtlení je prosté -- promìnná $x$ nám pùvodní formuli nijak neovlivní, proto¾e -se v ní nevyskytuje, proto ji mù¾eme nastavit tak, jak chceme. - -\s{Poznámka:} U~3-SAT lze vynutit právì tøi literály, pro krátké klauzule -pou¾ijeme stejný trik: -$$(\alpha) \rightarrow (\alpha \vee \alpha) \rightarrow (\alpha \lor x) \land (\alpha \lor \lnot x).$$ - -\h{3. problém: Hledání nezávislé mno¾iny v grafu} - -\>Existuje nezávislá mno¾ina vrcholù z~$G$ velikosti alespoò $k$? - -\s{Definice:} {\I Nezávislá mno¾ina} (NzMna) budeme øíkat ka¾dé mno¾inì vrcholù grafu -takové, ¾e mezi nimi nevede ¾ádná hrana. - -\figure{nezmna.eps}{Pøíklad nezávislé mno¾iny}{1in} - -\>{\I Vstup:} Neorientovaný graf $G$, $k \in {\bb N}$. - -\>{\I Výstup:} $\exists A \subseteq V(G)$, $\vert A \vert \ge k$: $\forall u,v \in A \Rightarrow uv \not\in E(G)$? - -\s{Poznámka:} Ka¾dý graf má minimálnì jednu nezávislou mno¾inu, a tou je prázdná mno¾ina. Proto je potøeba zadat i minimální velikost hledané mno¾iny. - -\>Uká¾eme, jak na~tento probém pøevést 3-SAT. - -\s{Pøevod 3-SAT na NzMna:} Z ka¾dé klauzule vybereme jeden literál, jeho¾ nastavením se klauzuli -rozhodneme splnit. Samozøejmì tak, abychom v~rùzných klauzulích nevybírali -konfliktnì, tj.~$x$ a~$\lnot x$. - -\s{Pøíklad:} -$(x \lor y \lor z) \land (x \lor \lnot y \lor \lnot z) \land (\lnot x \lor \lnot y \lor p) $. - -\>Pro ka¾dou klauzuli sestrojíme graf (trojúhelník) a pøidáme \uv{konfliktní} -hrany, tj. $x$ a $\lnot x$. Poèet vrcholù grafu odpovídá poètu literálù ve formuli, -poèet hran je maximálnì kvadratický a pøevod je tedy polynomiální. - -Existuje-li v grafu nezávislá mno¾ina velikosti $k$, pak z~ka¾dého z~$k$ trojúhelníkù -vybere právì jeden vrchol, a pøitom ¾ádné dva vrcholy nebudou odpovídat literálu a -jeho negaci -- tedy dostaneme ohodnocení promìnných splòujících alespoò $k$ klauzulí. -Na druhou stranu, existuje-li ohodnocení $k$ klauzulí, pak pøímo odpovídá nezávislé -mno¾inì velikosti $k$ (v ka¾dém trojúhelníku zvolíme právì jednu z ohodnocených -promìnných, nemù¾e se stát ¾e zvolíme vrcholy konfliktní hrany). Ptáme-li se tedy -na nezávislou mno¾inu velikosti odpovídající -poètu klauzulí, dostaneme odpovìï {\sc ano} právì tehdy, kdy¾ je formule splnitelná. - -Jsou-li ve formuli i klauzule krat¹í ne¾ 3, mù¾eme je buïto prodlou¾it metodou vý¹e -popsanou; nebo si v grafu necháme dvoj- a jedno-úhelníky, které ¾ádné z na¹ich úvah -vadit nebudou. - -\figure{nezmna_graf.eps}{Ukázka pøevodu 3-SAT na nezávislou mno¾inu}{3in} - -\s{Pøevod NzMna na SAT:} - -\itemize\ibull -\:Poøídíme si promìnné $v_1, \ldots, v_n$ odpovídající vrcholùm grafu. Promìnná $v_i$ bude - indikovat, zda se $i$-tý vrchol vyskytuje v~nezávislé mno¾inì (tedy pøíslu¹né ohodnocení - promìnných bude vlastnì charakteristická funkce nezávislé mno¾iny). -\:Pro ka¾dou hranu $ij \in E(G)$ pøidáme klauzuli $(\lnot v_i \lor \lnot v_j)$. Tyto klauzule - nám ohlídají, ¾e vybraná mno¾ina je vskutku nezávislá. -\:Je¹tì potøebujeme zkontrolovat, ¾e je mno¾ina dostateènì velká, tak¾e si její prvky - oèíslujeme èísly od~1 do~$k$. Oèíslování popí¹eme maticí promìnných $x_{ij}$, pøièem¾ - $x_{ij}$ bude pravdivá právì tehdy, kdy¾ v~poøadí $i$-tý prvek nezávislé mno¾iny je vrchol~$v_j$ --- pøidáme tedy klauzule, které nám øeknou, ¾e vybrané do nezávislé mno¾iny jsou právì - ty vrcholy, které jsou touto maticí oèíslované: $\forall i,j$, $x_{ij} \Rightarrow v_j$ - (jen dodejme, ¾e $a\Rightarrow b$ je definované jako $\neg a\vee b$). -\:Je¹tì potøebujeme zajistit, aby byla v~ka¾dém øádku i sloupci nejvý¹e jedna jednièka: - $\forall j,i,i^{'}, i\ne i^{'} : x_{ij} \Rightarrow \lnot x_{i^{'}j}$ a - $\forall i,j,j^{'}, j\ne j^{'} : x_{ij} \Rightarrow \lnot x_{ij^{'}}$. -\:A~nakonec si ohlídáme, aby v~ka¾dém øádku byla alespoò jedna jednièka, klauzulí $\forall i : - x_{i1} \lor x_{i2} \lor \ldots \lor x_{in}$. -\endlist -Tímto vynutíme NzMnu $\geq k$, co¾ jsme pøesnì chtìli. Takovýto pøevod je zøejmì polynomiální. - -\s{Pøíklad matice:} Jako pøíklad pou¾ijeme nezávislou mno¾inu z ukázky nezávislé mno¾iny. -Nech» jsou vrcholy grafu oèíslované zleva a zeshora. Hledáme nezávislou mno¾inu velikosti $2$. -Matice pak bude vypadat následovnì: -$$ \pmatrix{1&0&0&0&0 \cr 0&0&0&1&0}$$ -\s{Vysvìtlení:} Jako první vrchol mno¾iny bude vybrán vrchol $v_1$, proto v prvním -øádku a v prvním sloupci bude $1$. Jako druhý vrchol mno¾iny bude vybrán -vrchol $v_4$, proto na druhém øádku a ve ètvrtém sloupci bude $1$. Na ostatních místech bude $0$. - -\h{4. problém: Klika} - -\>{\I Vstup:} Graf $G, k \in N$. - -\>{\I Výstup:} $\exists$ úplný podgraf grafu $G$ na $k$ vrcholech? -\figure{klika.eps}{Pøíklad kliky}{2in} - -\s{Pøevod:} Prohodíme v grafu $G$ hrany a nehrany $\Rightarrow$ (hledání nezávislé mno¾iny $\leftrightarrow$ hledání kliky). - -\s{Dùvod:} Pokud existuje úplný graf na $k$ vrcholech, tak v~komplementárním grafu tyto vrcholy nejsou spojeny hranou, tj. tvoøí nezávislou mno¾inu, - a naopak. - -\figure{doplnek_nm.eps}{Prohození hran a nehran}{2in} - - -\h{5. problém: 3,3-SAT} -\s{Definice:} 3,3-SAT je speciální pøípad 3-SATu, kde ka¾dá promìnná se vyskytuje v~maximálnì tøech literálech. - -\s{Pøevod 3-SAT na 3,3-SAT:} -Pokud se promìnná $x$ vyskytuje v~$k > 3$ literálech, tak nahradíme výskyty novými promìnnými $x_1, \ldots , x_k$ a pøidáme klauzule: -$$ -(\lnot x_1 \lor x_2), -(\lnot x_2 \lor x_3), -(\lnot x_3 \lor x_4), -\ldots, -(\lnot x_{k-1} \lor x_k), -(\lnot x_k \lor x_1), -$$ - -co¾ odpovídá: - -$$ -(x_1 \Rightarrow x_2), -(x_2 \Rightarrow x_3), -(x_3 \Rightarrow x_4), -\ldots, -(x_{k-1} \Rightarrow x_k), -(x_k \Rightarrow x_1). -$$ - -Tímto zaruèíme, ¾e v¹echny nové promìnné budou mít stejnou hodnotu. - -Mimochodem, mù¾eme rovnou zaøídit, ¾e ka¾dý literál se vyskytuje nejvíce dvakrát (tedy ¾e -ka¾dá promìnná se vyskytuje alespoò jednou pozitivnì a alespoò jednou negativnì). Pokud by -se nìjaká promìnná objevila ve~tøech stejných literálech, mù¾eme na~ni -také pou¾ít ná¹ trik a nahradit ji tøemi promìnnými. V~nových klauzulích se pak bude -vyskytovat jak pozitivnì, tak negativnì. - -\h{6. problém: 3D párování (3D matching)} - -\>{\I Vstup:} Tøi mno¾iny, napø. $K$ (kluci), $H$ (holky), $Z$ (zvíøátka) a mno¾ina kompatibilních trojic (tìch, kteøí se spolu snesou). - -\>{\I Výstup:} Perfektní podmno¾ina trojic -- tj. taková podmno¾ina trojic, která obsahuje v¹echna $K$, $H$ a $Z$. - -\>Uká¾eme, jak na tento problém pøevést 3,3-SAT (ov¹em to a¾ na dal¹í pøedná¹ce). - -\figure{3d_parovani.eps}{Ukázka 3D párování}{3in} - -\s{Závìr:} Obrázek ukazuje problémy, jimi¾ jsme se dnes zabývali, a vztahy mezi tìmito problémy. -\figure{prevody.eps}{Pøevody mezi problémy}{3in} - -\bye diff --git a/10-prevody/3d_parovani.eps b/10-prevody/3d_parovani.eps deleted file mode 100644 index 22c2f66..0000000 --- a/10-prevody/3d_parovani.eps +++ /dev/null @@ -1,2166 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Creator: 0.45pre1 -%%Pages: 1 -%%Orientation: Portrait -%%BoundingBox: 0 0 504 241 -%%HiResBoundingBox: 4e-007 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setlinecap -newpath -5.77 0 moveto --2.88 5 lineto --2.88 -5 lineto -5.77 0 lineto -closepath -stroke -grestore -grestore -grestore -showpage -%%EOF diff --git a/10-prevody/prevody.svg b/10-prevody/prevody.svg deleted file mode 100644 index 248553f..0000000 --- a/10-prevody/prevody.svg +++ /dev/null @@ -1,264 +0,0 @@ - - - - - - - - - - - - - - - - image/svg+xml - - - - - - - - - SAT - - - Nezávislámnožina - - - 3 SAT - - - 3,3 SAT - - - Klika - - - 3d párování - - - - - - - - diff --git a/11-np/11-np.tex b/11-np/11-np.tex deleted file mode 100644 index c81d913..0000000 --- a/11-np/11-np.tex +++ /dev/null @@ -1,334 +0,0 @@ -\input lecnotes.tex - -\prednaska{11}{NP-úplné problémy}{\vbox{\hbox{(zapsali F. Kaèmarik, R. Krivák, D. Remi¹} - \hbox{ Michal Kozák, Vojta Tùma)}}} - -Dosud jsme zkoumali problémy, které se nás ptaly na to, jestli nìco existuje. -Napøíklad jsme dostali formuli a problém splnitelnosti se nás ptal, zda -existuje ohodnocení promìnných takové, ¾e formule platí. Nebo v~pøípadì -nezávislých mno¾in jsme dostali graf a èíslo $k$ a ptali jsme se, jestli -v~grafu existuje nezávislá mno¾ina, která obsahuje alespoò~$k$ vrcholù. -Tyto otázky mìly spoleèné to, ¾e kdy¾ nám nìkdo napovìdìl nìjaký objekt, umìli -jsme efektivnì øíci, zda je to ten, který hledáme. Napøíklad pokud dostaneme -ohodnocení promìnných logické formule, staèí jen dosadit a spoèítat, kde -formule dá \ nebo \. Zjistit, ¾e nìjaký objekt je ten, který -hledáme, umíme efektivnì. Tì¾ké na tom je takový objekt najít. Co¾ vede -k~definici obecných vyhledávacích problémù, kterým se øíká tøída problémù NP. -Nadefinujeme si ji poøádnì, ale nejdøíve zaèneme tro¹ièku jednodu¹¹í tøídou. - -\s{Definice:} P je {\I tøída rozhodovacích problémù}, které jsou øe¹itelné -v~polynomiálním èase. Jinak øeèeno, problém -$L \in {\rm P} \Leftrightarrow \exists $ polynom $f$ a~$\exists$ algoritmus~$A$ -takový, ¾e $\forall x: L(x)=A(x)$ a $A(x)$ dobìhne v~èase $\O(f(x))$. - -Tøída P tedy odpovídá tomu, o èem jsme se shodli, ¾e umíme efektivnì øe¹it. -Nadefinujme nyní tøídu NP: - -\s{Definice:} NP je {\I tøída rozhodovacích problémù} takových, ¾e $L \in {\rm NP}$ právì tehdy, kdy¾ $\exists $ problém -$K\in{\rm P}$ a $\exists$ polynom $g$ takový, ¾e pro -$\forall x$ platí $L(x)=1 \Leftrightarrow \exists $ nápovìda $ y: \vert y \vert \leq g(\vert x \vert)$ a souèasnì $K(x,y)=1$. - -\s{Pozorování:} Splnitelnost logických formulí je v~NP. Staèí si toti¾ nechat napovìdìt, jak -ohodnotit jednotlivé promìnné a pak ovìøit, jestli je formule splnìna. Nápovìda je polynomiálnì -velká (dokonce lineárnì), splnìní zkontrolujeme také v~lineárním èase. Odpovíme tedy ano právì -tehdy, existuje-li nápovìda, která nás pøesvìdèí, èili pokud je formule splnitelná. - -\s{Pozorování:} Tøída P le¾í uvnitø NP. -V~podstatì øíkáme, ¾e kdy¾ máme problém, který umíme øe¹it v~polynomiálním èase -bez nápovìdy, tak to zvládneme v~polynomiálním èase i s~nápovìdou. - -Problémy z minulé pøedná¹ky jsou v¹echny v NP (napø. pro nezávislou -mno¾inu je onou nápovìdou pøímo mno¾ina vrcholù deklarující nezávislost), -o jejich pøíslu¹nosti do P ale nevíme nic. -Brzy uká¾eme, ¾e to jsou v jistém smyslu nejtì¾¹í problémy v~NP. -Nadefinujme si: - -\s{Definice:} Problém $L$ je NP-{\I tì¾ký} právì tehdy, kdy¾ je na~nìj pøevoditelný -ka¾dý problém z~NP (viz definici pøevodù z minulé pøedná¹ky). - -Rozmyslete si, ¾e pokud umíme øe¹it nìjaký NP-tì¾ký problém v~polynomiálním èase, -pak umíme vyøe¹it v~polynomiálním èase v¹e v~NP, a tedy ${\rm P}={\rm NP}$. - -My se budeme zabývat problémy, které jsou NP-tì¾ké a samotné jsou v~NP. Takovým problémùm se øíká NP-úplné. - -\s{Definice:} Problém $L$ je NP-{\I úplný} právì tehdy, kdy¾ $L$ je NP-tì¾ký a $L \in {\rm NP}$. - -NP-úplné problémy jsou tedy ve~své podstatì nejtì¾¹í problémy, které le¾í v~NP. -Kdybychom umìli vyøe¹it nìjaký NP-úplný problém v~polynomiálním èase, pak -v¹echno v~NP je øe¹itelné v~polynomiálním èase. Bohu¾el to, jestli nìjaký -NP-úplný problém lze øe¹it v~polynomiálním èase, se neví. Otázka, jestli -${\rm P}={\rm NP}$, je asi nejznámìj¹í otevøený problém v~celé teoretické -informatice. - -Kde ale nìjaký NP-úplný problém vzít? K~tomu se nám bude velice hodit následující vìta: - -\s{Vìta (Cookova):} SAT je NP-úplný. - -\>Dùkaz je znaènì technický, pøibli¾nì ho naznaèíme pozdìji. Pøímým dùsledkem -Cookovy vìty je, ¾e cokoli v~NP je pøevoditelné na SAT. -K dokazování NP-úplnosti dal¹ích problémù pou¾ijeme následující vìtièku: - -\s{Vìtièka:} Pokud problém $L$ je NP-úplný a $L$ se dá pøevést na nìjaký problém $M\in{\rm NP}$, pak $M$ je také NP-úplný. - -\proof -Tuto vìtièku staèí dokázat pro NP-tì¾kost, NP-úplnost plyne okam¾itì z~toho, ¾e -problémy jsou NP-tì¾ké a le¾í v~NP (podle pøedpokladu). - -Víme, ¾e $L$ se dá pøevést na~$M$ nìjakou funkcí~$f$. Jeliko¾ $L$ je NP-úplný, -pak pro ka¾dý problém $Q\in{\rm NP}$ existuje nìjaká funkce~$g$, která pøevede -$Q$ na~$L$. Staèí tedy slo¾it funkci~$f$ s~funkcí~$g$, èím¾ získáme funkci pracující -opìt v~polynomiálním èase, která pøevede~$Q$ na~$M$. Ka¾dý problém z~NP se tedy -dá pøevést na problém~$M$. -\qed - -\s{Dùsledek:} Cokoliv, na co jsme umìli pøevést SAT, je také NP-úplné. -Napøíklad nezávislá mno¾ina, rùzné varianty SATu, klika v~grafu~\dots - -Jak taková tøída NP vypadá? Pøedstavme si v¹echny problémy tøídy NP, jakoby seøazené -shora dolu podle obtí¾nosti problémù (tedy navzdor gravitaci), kde porovnání dvou -problémù urèuje pøevoditelnost (viz obrázek). - -\figure{p-np.eps}{Struktura tøídy NP}{2.5cm} - -Obecnì mohou nastat dvì situace, proto¾e nevíme, jestli ${\rm P}={\rm NP}$. -Jestli ano, pak v¹echno je jedna a ta samá tøída. To by bylo v nìkterých -pøípadech nepraktické, napø. ka¾dá ¹ifra by byla jednodu¹e rozlu¹titelná. -\foot{Poznámka o ¹ifrách -- libovolnou funkci vyèíslitelnou v polynomiálním -èase bychom umìli v polynomiálním èase také invertovat.} -Jestli ne, NP-úplné problémy urèitì nele¾í v P, tak¾e P a NP-úplné problémy -jsou dvì disjunktní èásti NP. Také se dá dokázat (to dìlat nebudeme, ale je -dobré to vìdìt), ¾e je¹tì nìco le¾í mezi nimi, tedy ¾e existuje problém, který -je v~NP, není v~P a není NP-úplný (dokonce je takových problémù nekoneènì mnoho, -v nekoneènì tøídách). - -\s{Katalog NP-úplných problémù} - -Uká¾eme si nìkolik základních NP-úplných problémù. O~nìkterých jsme to dokázali -na~minulé pøedná¹ce, o~dal¹ích si to doká¾eme nyní, zbylým se na~zoubek podíváme -na~cvièeních. - -\itemize\ibull -\:{\I logické:} - \itemize\ibull - \:SAT (splnitelnost logických formulí v~CNF) - \:3-SAT (ka¾dá klauzule obsahuje max.~3 literály) - \:3,3-SAT (a navíc ka¾dá promìnná se vyskytuje nejvý¹e tøikrát) - \:SAT pro obecné formule (nejen CNF) - \:Obvodový SAT (není to formule, ale obvod) - \endlist -\:{\I grafové:} - \itemize\ibull - \:Nezávislá mno¾ina (existuje mno¾ina alespoò~$k$ vrcholù taková, ¾e ¾ádné dva nejsou propojeny hranou?) - \:Klika (existuje úplný podgraf na~$k$ vrcholech?) - \:3D párování (tøi mno¾iny se zadanými trojicemi, existuje taková mno¾ina disjunktních trojic, ve~které jsou v¹echny prvky?) - \:Barvení grafu (lze obarvit vrcholy $k$~barvami tak, aby vrcholy stejné barvy nebyly nikdy spojeny hranou? NP-úplné u¾ pro~$k=3$) - \:Hamiltonovská cesta (cesta obsahující v¹echny vrcholy [právì jednou]) - \:Hamiltonovská kru¾nice (kru¾nice, která nav¹tíví v¹echny vrcholy [právì jednou]) - \endlist -\:{\I èíselné:} - \itemize\ibull - \:Batoh (nejjednodu¹¹í verze: dána mno¾ina èísel, zjistit, zda existuje podmno¾ina se zadaným souètem) - \:Batoh -- optimalizace (podobnì jako u pøedchozího problému, ale místo mno¾iny èísel máme mno¾inu - pøedmìtù s vahami a cenami, chceme co nejdra¾¹í podmno¾inu její¾ váha nepøesáhne zadanou kapacitu - batohu) - \:Loupe¾níci (lze rozdìlit danou mno¾inu èísel na~dvì podmno¾iny se stejným souètem) - \:$Ax=b$ (soustava celoèíslených lineárních rovnic; $x_i$ mohou být pouze 0 nebo 1; NP-úplné i pokud $A_{ij}\in\{0,1\}$ a $b_i\in\{0,1\}$) - \:Celoèíselné lineární programování (existuje vektor nezáporných celoèísených $x$ takový, ¾e $Ax \leq b$ ?) - \endlist -\endlist - -Nyní si uká¾eme, jak pøevést SAT na nìjaký problém. Kdy¾ chceme ukázat, ¾e na -nìco se dá pøevést SAT, potøebujeme obvykle dvì vìci: -konstrukci, která bude simulovat promìnné, tedy nìco, co nabývá dvou stavù -\/\; a nìco, co bude reprezentovat klauzule a umí zaøídit, aby -ka¾dá klauzule byla splnìna alespoò jednou promìnnou. -Roz¹iøme tedy ná¹ katalog problémù o následující: -\h{3D párování (3D matching)} - -\>{\I Vstup:} Tøi mno¾iny, napø. $K$ (kluci), $H$ (holky), $Z$ (zvíøátka) a -mno¾ina $T$ kompatibilních trojic (tìch, kteøí se spolu snesou), - tj. $T \subseteq K\times H\times Z$. - -\>{\I Výstup:} Perfektní podmno¾ina trojic $P\subseteq K\times H \times Z$ -- - tj. taková podmno¾ina trojic, ¾e $(\forall k\in K\ \exists !p\in P: k\in p) - \wedge(\forall h\in H\ \exists !p\in P: h\in p) - \wedge(\forall z\in Z\ \exists !p\in P: z\in p)$ -- tedy ka¾dý byl vybrán - právì jednou. - - -\h{ Uká¾eme, jak na 3D-párování pøevést 3,3-SAT } - -Uva¾ujme takovouto konfiguraci: - -\fig{3d.eps}{4cm} - -\>4 zvíøátka, 2 kluci, 2 dívky a 4 trojice, které oznaèíme $A, B, C, D$. -Je¹tì pøedpokládáme, ¾e zvíøátka - se mohou úèastnit nìjakých jiných trojic, ale -tito ètyøi lidé se vyskytují pouze v~tìchto ètyøech trojicích a~nikde jinde. -V¹imneme si, ¾e existují právì dvì mo¾nosti, jak tento obrázek spárovat. -Abychom spárovali kluka $k_1$, tak si musíme vybrat $A$ nebo $B$. Kdy¾ si -vybereme $A$, $k_1$ i $d_2$ u¾ jsou spárovaní tak¾e si nesmíme vybrat $B$ ani -$D$. Pak jediná mo¾nost, jak spárovat $d_1$ a~$k_2$ je $C$. Jedna mo¾nost je -tedy vybrat si $A$ a $C$ a jeliko¾ je obrázek symetrický, tak kdy¾ vybereme -místo $A$ trojici $B$, dostaneme $B$ a~$D$. V¾dy si tedy vybereme dvì protìj¹í -trojice v~obrázku. - -Tyto konfigurace budeme pou¾ívat k~reprezentaci promìnných. Pro ka¾dou -promìnnou si nakreslíme takový obrázek a~to, ¾e $A$ bude spárované s~$C$, bude -odpovídat tomu, ¾e $x=1$, a~spárování $B$ a~$D$ bude odpovídat - $x=0$. Pokud jsme -pou¾ili $A$ a~$C$, zvíøata se sudými èísly, tj. $z_2$ a~$z_4$, horní a~dolní, -jsou nespárovaná a~pokud jsme pou¾ili $B$ a~$D$, zvíøátka $z_1$ a~$z_3$ zùstala -nespárovaná. Pøes tato nespárovaná zvíøátka mù¾eme pøedávat informaci, jestli -promìnná $x$ má hodnotu \ nebo \ do dal¹ích èástí vstupu. - -Zbývá vymyslet, jak reprezentovat klauzule. Klauzule jsou trojice popø. dvojice -literálù, napø. $\kappa = (x \lor y \lor \lnot r)$, kde -potøebujeme zajistit, aby $x$ bylo nastavené na $1$ nebo $y$ bylo nastavené -na~$1$ nebo $r$ na $0$. - -\fig{klauzule.eps}{4cm} - -\>Pro takovouto klauzuli si poøídíme dvojici kluk-dívka, kteøí budou figurovat -ve tøech trojicích se tøemi rùznými zvíøátky, co¾ mají být volná zvíøátka -z~obrázkù pro pøíslu¹né promìnné (podle toho, má-li se promìnná vyskytnout -s negací nebo ne). A~zaøídíme to tak, aby ka¾dé zvíøátko bylo -pou¾ité maximálnì v~jedné takové trojici, co¾ jde proto, ¾e ka¾dý literál se -vyskytuje maximálnì dvakrát a~pro ka¾dý literál máme dvì volná zvíøátka, -z~èeho¾ plyne, ¾e zvíøátek je dost pro v¹echny klauzule. Pro dvojice se postupuje -obdobnì. - -Je¹tì nám ale urèitì zbude $2p-k$ zvíøátek, kde $p$ je poèet promìnných, $k$ -poèet klauzulí -- ka¾dá promìnná vyrobí 4 zvíøátka, klauzule zba¹tí jedno -a samotné ohodnocení 2 zvíøátka -- tak pøidáme je¹tì $2p-k$ párù -kluk-dìvèe, kteøí milují -v¹echna zvíøátka, a~ti vytvoøí zbývající trojice. - -Pokud formule byla splnitelná, pak ze splòujícího ohodnocení mù¾eme vyrobit -párování s~na¹í konstrukcí. Obrázek pro ka¾dou promìnnou spárujeme podle -ohodnocení, tj. promìnná je $0$ nebo $1$ a~pro ka¾dou klauzuli si vybereme -nìkterou z~promìnných, kterými je ta klauzule splnìna. Funguje to ale -i~opaènì: Kdy¾ nám nìkdo dá párovaní v~na¹í konstrukci, pak z nìho doká¾eme -vyrobit splòující ohodnocení dané formule. Podíváme se, v~jakém stavu je -promìnná, a~to je v¹echno. Z~toho, ¾e jsou správnì spárované klauzule, u¾ -okam¾itì víme, ¾e jsou v¹echny splnìné. - -Zbývá ovìøit, ¾e na¹e redukce funguje v~polynomiálním èase. Pro ka¾dou klauzuli -spotøebujeme konstantnì mnoho èasu, $2p-k$ je také polynomiálnì mnoho a~kdy¾ v¹e -seèteme, máme polynomiální èas vzhledem k~velikosti vstupní formule. Tím je -pøevod hotový a~mù¾eme 3D-párování zaøadit mezi NP-úplné problémy. - - -%RK - - -\h{Náznak dùkazu Cookovy vìty} - -Abychom mohli budovat teorii NP-úplnosti, potøebujeme alespoò jeden problém, -o kterém doká¾eme, ¾e je NP-úplný, z definice. Cookova vìta øíká o NP-úplnosti -SAT-u, ale nám se to hodí dokázat o tro¹ku jiném problému -- {\I obvodovém SAT-u}. - -\>{\I Obvodový SAT} je splnitelnost, která nepracuje s~formulemi, ale s~booleovskými -obvody (hradlovými sítìmi). Ka¾dá formule se dá pøepsat do booleovského obvodu, -který ji poèítá, tak¾e dává smysl zavést splnitelnost i pro obvody. Na¹e obvody -budou mít nìjaké vstupy a~jenom jeden výstup. Budeme se ptát, jestli se vstupy -tohoto obvodu dají nastavit tak, abychom na výstupu dostali \. - -\>Nejprve doká¾eme NP-úplnost {\I obvodového SAT-u} a~pak uká¾eme, ¾e se dá -pøevést na obyèejný SAT v~CNF. Tím bude dùkaz Cookovy vìty hotový. Zaènìme -s pomocným lemmatem. - -\s{Lemma:} Nech» $L$ je problém v P. Potom existuje polynom $p$ a algoritmus, -který pro $\forall n \ge 0$ spoète v èase $p(n)$ hradlovou sí» $Bn$ s $n$ vstupy -a 1 výstupem takovou, ¾e $\forall x \in \{ 0, 1 \}^n : Bn(x) = L(x)$. - -\proof -Náznakem. Na základì zku¹eností z Principù poèítaèù intuitivnì chápeme poèítaèe -jako nìjaké slo¾ité booleovské obvody, jejich¾ stav se mìní v~èase. Uva¾me tedy -nìjaký problém $L \in {\rm P}$ a polynomiální algoritmus, který ho øe¹í. Pro vstup -velikosti~$n$ dobìhne v~èase~$T$ polynomiálním v~$n$ a spotøebuje $\O(T)$ bunìk pamìti. -Staèí nám tedy \uv{poèítaè s~pamìtí velkou $\O(T)$}, co¾ je nìjaký booleovský obvod -velikosti polynomiální v~$T$, a~tedy i v~$n$. Vývoj v~èase o¹etøíme tak, ¾e sestrojíme~$T$ -kopií tohoto obvodu, ka¾dá z~nich bude odpovídat jednomu kroku výpoètu a bude -propojena s~\uv{minulou} a \uv{budoucí} kopií. Tím sestrojíme booleovský obvod, -který bude øe¹it problém~$L$ pro vstupy velikosti~$n$ a bude polynomiálnì velký -vzhledem k~$n$. - -\s{Poznámka:} -Pro dùkaz následující vìty si dovolíme drobnou úpravu v~definici tøídy NP. -Budeme chtít, aby nápovìda -mìla pevnou velikost, závislou pouze na~velikosti vstupu (tedy: $\vert y \vert -= g(\vert x \vert)$ namísto $\vert y \vert \le g(\vert x \vert)$). Proè je taková -úprava BÚNO? Jistì si dovedete pøedstavit, -¾e pùvodní nápovìdu doplníme na po¾adovanou délku nìjakými \uv{mezerami}, které -program ignoruje. (Tedy upravíme program tak, aby mu nevadilo, ¾e dostane na -konci nápovìdy nìjak kódované mezery.) - -\s{Vìta:} Obvodový SAT je NP-úplný. - -\proof -Máme tedy nìjaký problém $L$ z~NP a~chceme dokázat, ¾e $L$ se dá pøevést -na~obvodový SAT (tj. NP-tì¾kost). Kdy¾ nám nìkdo pøedlo¾í nìjaký vstup $x$ -(chápeme jako posloupnost $x_1, x_2, \ldots, x_n$), -spoèítáme velikost nápovìdy $g(n)$. Víme, ¾e kontrolní -algoritmus~$K$ (který kontroluje, zda nápovìda je správnì) je v~P. Vyu¾ijeme -pøedchozí lemma, abychom získali obvod, který pro konkrétní velikost vstupu -$x$ poèítá to, co kontrolní algoritmus $K$. Na vstupu tohoto obvodu bude $x$ -(vstup problému $L$) a~nápovìda~$y$. Na výstupu nám øekne, jestli je nápovìda -správná. Velikost vstupu tohoto obvodu bude tedy $p(g(n))$, co¾ je také polynom. - -\fig{kobvod.eps}{2.3cm} - -\>V tomto obvodu zafixujeme vstup $x$ (na místa vstupu dosadíme konkrétní hodnoty z $x$). Tím získáme obvod, jeho¾ vstup je jen $y$ a~ptáme se, zda za $y$ mù¾eme dosadit nìjaké hodnoty tak, aby na výstupu bylo \. Jinými slovy, ptáme se, zda je tento obvod splnitelný. - -\>Pro libovolný problém z~NP tak doká¾eme sestrojit funkci, která pro ka¾dý vstup~$x$ v~polynomiálním èase vytvoøí obvod, který je splnitelný pravì tehdy, kdy¾ odpovìï tohoto problému na vstup $x$ má být \. Tedy libovolný problém z~NP se dá -v~polynomiálním èase pøevést na obvodový SAT. - -\>Obvodový SAT je v NP triviálnì -- staèí si nechat poradit vstup, sí» -topologicky setøídit a v~tomto poøadí poèítat hodnoty hradel. -\qed - -\s{Lemma:} Obvodový SAT se dá pøevést na 3-SAT. - -\proof -Budeme postupnì budovat formuli v~konjunktivní normální formì. Ka¾dý booleovský obvod se dá pøevést v~polynomiálním èase na~ekvivalentní obvod, ve~kterém se vyskytují jen hradla {\sc and} a {\sc not}, tak¾e staèí najít klauzule odpovídající tìmto hradlùm. Pro ka¾dé hradlo v~obvodu zavedeme novou promìnnou popisující jeho výstup. Pøidáme klauzule, které nám kontrolují, ¾e toto hradlo máme ohodnocené konzistentnì. - -\>{\I Pøevod hradla \sc not}: na vstupu hradla budeme mít nìjakou promìnnou $x$ (která pøi¹la buïto pøímo ze~vstupu toho celého obvodu nebo je to promìnná, která vznikla na výstupu nìjakého hradla) a na výstupu promìnnou $y$. Pøidáme klauzule, které nám zaruèí, ¾e jedna promìnná bude negací té druhé: -$$\matrix{ (x \lor y), \cr - (\neg{x} \lor \neg{y}). \cr } - \hskip 0.2\hsize -\vcenter{\hbox{\epsfxsize=0.7cm\epsfbox{not.eps}}} -$$ - -\>{\I Pøevod hradla \sc and}: Hradlo má vstupy $x, y$ a~výstup $z$. Potøebujeme pøidat klauzule, které nám popisují, jak se má hradlo {\sc and} chovat. Tyto vztahy pøepí¹eme do~konjunktivní normální formy: -$$ -\left. \matrix{ - x\ \&\ y \Rightarrow z \cr - \neg{x} \Rightarrow \neg{z} \cr - \neg{y} \Rightarrow \neg{z} \cr -} -\ \quad - \right\} -\quad -\matrix{ - (z \lor \neg{x} \lor \neg{y}) \cr - (\neg{z} \lor x) \cr - (\neg{z} \lor y) \cr - } - \hskip 0.1\hsize -\vcenter{\hbox{\epsfxsize=0.7cm\epsfbox{and.eps}}} -$$ - -\>Kdy¾ chceme pøevádìt obvodový SAT na 3-SAT, obvod nejdøíve pøelo¾íme na takový, ve~kterém jsou jen hradla {\sc and} a~{\sc not}, a~pak hradla tohoto obvodu pøelo¾íme na klauzule. Formule vzniklá z~takovýchto klauzulí je splnitelná pravì tehdy, kdy¾ je splnitelný daný obvod. Pøevod pracuje v polynomiálním èase. -\qed - -\s{Poznámka:} -Kdy¾ jsme zavádìli SAT, omezili jsme se jen na formule, které jsou -v~konjunktivní normální formì (CNF). Teï u¾ víme, ¾e splnitelnost obecné -booleovské formule doká¾eme pøevést na obvodovou splnitelnost a tu pak -pøevést na 3-SAT. Opaèný pøevod je samozøejmì triviální, tak¾e obecný SAT -je ve~skuteènosti ekvivalentní s~na¹ím \uv{standardním} SATem pro CNF. - -\bye - diff --git a/11-np/3d.eps b/11-np/3d.eps deleted file mode 100644 index 755fbcc..0000000 --- a/11-np/3d.eps +++ /dev/null @@ -1,641 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Creator: 0.45.1 -%%Pages: 1 -%%Orientation: Portrait -%%BoundingBox: 1 1 234 233 -%%HiResBoundingBox: 1.5714774 1.6 233.87693 232.675 -%%EndComments -%%Page: 1 1 -0 842 translate -0.8 -0.8 scale -0 0 0 setrgbcolor -[] 0 setdash -1 setlinewidth -0 setlinejoin -0 setlinecap -gsave [1 0 0 1 0 0] concat -gsave -0 0 0 setrgbcolor -newpath -147.56738 996.36218 moveto -142.78136 988.61727 lineto -137.5 980.14292 lineto -147.03962 980.12535 lineto -157.5 980.0699 lineto -152.7464 987.83238 lineto -147.56738 996.36218 lineto -closepath -fill -grestore -gsave -0 0 0 setrgbcolor -newpath -147.43262 812.0699 moveto -152.21864 819.81481 lineto -157.5 828.28916 lineto -147.96038 828.30673 lineto 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lecnotes.tex -\prednaska{12}{Aproximaèní algoritmy}{\vbox{\hbox{(F. Ha¹ko, J. Menda, M. Mare¹,} - \hbox{ Michal Kozák, Vojta Tùma)}}} - -\>Na~minulých pøedná¹kách jsme se zabývali rùznými tì¾kými rozhodovacími -problémy. Tato se zabývá postupy, jak se v~praxi vypoøádat s~øe¹ením tìchto -problémù. - -\h{Co dìlat, kdy¾ potkáme NP-úplný problém} -\algo -\:Nepanikaøit. -\:Spokojit se s~málem. -\:Rozmyslet, jestli opravdu potøebujeme obecný algoritmus. Mnohdy potøebujeme pouze -speciálnìj¹í pøípady, které mohou být øe¹itelné v~polynomiálním èase. -\:Spokojit se s~pøibli¾ným øe¹ením, (pou¾ít aproximaèní algoritmus). -\:Pou¾ít heuristiku -- napøíklad genetické algoritmy nebo randomizované algoritmy. -Velmi pomoci mù¾e i jen výhodnìj¹í poøadí pøi~prohledávání èi oøezávání nìkterých -napohled nesmyslných vìtví výpoètu. -\endalgo - -\h{První zpùsob: Speciální pøípad} - -\>Èasto si vystaèíme s~vyøe¹ením speciálního pøípadu NP-úplného problému, který -le¾í v~$P$. Napøíklad pøi øe¹ení grafové úlohy nám mù¾e staèit øe¹ení -pro~speciální druh grafù (stromy, bipartitní grafy, \dots). Barvení grafu je lehké -napø. pro~dvì barvy èi pro intervalové grafy. 2-SAT, jako speciální pøípad SATu, -se dá øe¹it v~lineárním èase. - -\>Uká¾eme si dva takové pøípady (budeme øe¹ení hledat, nejen rozhodovat, zda existuje) - -\s{Problém: Maximální nezávislá mno¾ina ve~stromì (ne rozhodovací)} - -\>{\I Vstup:} Zakoøenìný strom~$T$. - -\>{\I Výstup:} Maximální (co do~poètu vrcholù) nezávislá mno¾ina vrcholù~$M$~v~$T$. - -\>BÚNO mù¾eme pøedpokládat, ¾e v~$M$ jsou v¹echny listy $T$. Pokud by nìkterý -list $l$ v~$M$ nebyl, tak se podíváme na~jeho otce: -\itemize\ibull -\:Pokud otec není v~$M$, tak list $l$ pøidáme do~$M$, èím¾ se nezávislost -mno¾iny zachovala a velikost stoupla o~1. -\:Pokud tam otec je, tak ho z~$M$ vyjmeme a na~místo nìho vlo¾íme $l$. -Nezávislost ani velikost $M$ se nezmìnily. -\endlist -\>Nyní listy spolu s~jejich otci z~$T$ odebereme a postup opakujeme. $T$ se -mù¾e rozpadnout na~les, ale to nevadí $\to$ tentý¾ postup aplikujeme na~v¹echny stromy v~lese. - -\s{Algoritmus:} -\>MaxNz$(T)$ -\algo -\:Polo¾íme $L$:=$\{$listy stromu $T\}$. -\:Polo¾íme $O$:=$\{$otcové vrcholù z~$L\}$. -\:Vrátíme $L \cup$ MaxNz$(T\setminus(O \cup L))$. -\endalgo -\>{\I Poznámka:} Toto doká¾eme naprogramovat v~$\O(n)$ (udr¾ujeme si frontu listù). - -\s{Problém: Batoh} - -\>Je daná mno¾ina $n$~pøedmìtù s~hmotnostmi $h_1,\ldots,h_n$ -a cenami $c_1,\ldots,c_n$ a~batoh, který unese hmostnost~$H$. Najdìte takovou -podmno¾inu pøedmìtù, jejich¾ celková hmotnost je maximálnì $H$ a celková cena -je maximální mo¾ná. - -\>Tento problém je zobecnìním problému batohu z~minulé pøedná¹ky dvìma smìry: -Jednak místo rozhodovacího problému øe¹íme optimalizaèní, jednak pøedmìty -mají ceny (pøedchozí verze odpovídala tomu, ¾e ceny jsou rovny hmotnostem). -Uká¾eme si algoritmus pro øe¹ení tohoto obecného problému, jeho¾ èasová -slo¾itost bude polynomiální v~poètu pøedmìtù~$n$ a souètu v¹ech cen~$C=\sum_i -c_i$. - -\>Pou¾ijeme dynamické programování. Pøedstavme si problém omezený na~prvních~$k$ -pøedmìtù. Oznaème si $A_k(c)$ (kde $0\le c\le C$) minimální hmotnost -podmno¾iny, její¾ cena je právì~$c$. Tato $A_k$ spoèteme indukcí podle~$k$: -Pro $k=0$ je urèitì $A_0(0)=0$, $A_0(c)=infty$ pro $c>0$. Pokud ji¾ známe -$A_{k-1}$, spoèítáme $A_k$ následovnì: $A_k(c)$ odpovídá nìjaké podmno¾inì -pøedmìtù z~$1,\ldots,k$. V~této podmno¾inì jsme buïto $k$-tý pøedmìt nepou¾ili -(a pak je $A_k(c)=A_{k-1}(c)$), nebo pou¾ili a tehdy bude $A_k(c) = -A_{k-1}(c-c_k) + h_k$ (to samozøejmì jen pokud $c\ge c_k$). Z~tìchto dvou -mo¾ností si vybereme tu, která dává mno¾inu s~men¹í hmotností. Tedy: -$$ -A_k(c) = \min (A_{k-1}(c), A_{k-1}(c-c_k) + h_k). -$$ -Tímto zpùsobem v~èase $\O(C)$ spoèteme $A_k(c)$ pro fixní $k$ a v¹echna $c$, -v~èase $\O(nC)$ pak v¹echny $A_k(c)$. - -\>Podle $A_n$ snadno nalezneme maximální cenu mno¾iny, která se vejde do~batohu. -To bude nejvìt¹í~$c^*$, pro nì¾ je $A_n(c^*) \le H$. Jeho nalezení nás stojí -èas $\O(C)$. - -\>A~jak zjistit, které pøedmìty do~nalezené mno¾iny patøí? Upravíme algoritmus, -aby si pro ka¾dé $A_k(c)$ pamatoval $B_k(c)$, co¾ bude index posledního pøedmìtu, -který jsme do~pøíslu¹né mno¾iny pøidali. Pro nalezené $c^*$ tedy bude $i=B_n(c^*)$ -poslední pøedmìt v~nalezené mno¾inì, $i'=B_{i-1}(c^*-c_i)$ ten pøedposlední -a tak dále. Takto v~èase $\O(n)$ rekonstruujeme celou mno¾inu od~posledního -prvku k~prvnímu. - -\>Ukázali jsme tedy algoritmus s~èasovou slo¾itostí $\O(nC)$, který vyøe¹í -problém batohu. Jeho slo¾itost není polynomem ve~velikosti vstupu ($C$~mù¾e -být a¾ exponenciálnì velké vzhledem k~velikosti vstupu), ale pouze ve~velikosti -èísel na~vstupu. Takovým algoritmùm se øíká {\I pseudopolynomiální.} Ani takové -algoritmy ale nejsou k dispozici pro v¹echny problémy (napø. u problému obchodního -cestujícího nám vùbec nepomù¾e, ¾e váhy hran budou malá èísla). - -\s{Verze bez cen:} Na verzi s~cenami rovnými hmotnostem se dá pou¾ít -i jiný algoritmus zalo¾ený na~dynamickém programování: poèítáme mno¾iny -$Z_k$ obsahující v¹echny hmotnosti men¹í ne¾~$H$, kterých nabývá -nìjaká podmno¾ina prvních~$k$ prvkù. Pøitom $Z_0=\{0\}$, $Z_k$ -spoèteme ze~$Z_{k-1}$ --- udr¾ujme si $Z_{k-1}$ jako setøídìný spojový seznam, -výpoèet dal¹ího seznamu udìláme slitím dvou seznamù $Z_{k-1}$ a $Z_{k-1}$ se -v¹emi prvky zvý¹enými o hmotnost $k$ zahazujíce duplicitní a pøíli¹ velké hodnoty --- -a ze~$Z_n$ vyèteme výsledek. V¹echny tyto mno¾iny -mají nejvý¹e $H$ prvkù, tak¾e celková èasová slo¾itost algoritmu je~$\O(nH)$. - -\h{Druhý zpùsob: Aproximace} - -\>V pøedcházejících problémech jsme se zamìøili na~speciální pøípady. Obèas v¹ak -takové ¹tìstí nemáme a musíme vyøe¹it celý NP-úplný problém. Mù¾eme si v¹ak -pomoct tím, ¾e se ho nebudeme sna¾it vyøe¹it optimálnì -- namísto optimálního -øe¹ení najdeme nìjaké, které je nejvý¹e $c$-krát hor¹í pro nìjakou konstantu $c$. - -\s{Problém: Obchodní cestující} - -\>{\I Vstup:} Neorientovaný graf~$G$, ka¾dá hrana -je ohodnocená funkcí $w: E(G)\rightarrow {\bb R }^+_0$. - -\>{\I Výstup:} Hamiltonovská kru¾nice (obsahující v¹echny vrcholy grafu), a~to ta nejkrat¹í -(podle ohodnocení). - -\>Tento problém je hned na~první pohled nároèný -- u¾ sama existence -hamiltonovské kru¾nice je NP-úplná. Najdeme aproximaèní algoritmus nejprve za pøedpokladu, -¾e vrcholy splòují trojúhelníkovou nerovnost (tj. $\forall x,y,z \in V: w(xz)\le -w(xy)+w(yz)$), potom uká¾eme, ¾e v úplnì obecném pøípadé by samotná existence -aproximaèního algoritmu implikovala ${\rm P=NP }$. - -\>{\I a) trojúhelníková nerovnost:} - -Existuje pìkný algoritmus, který najde hamiltonovskou kru¾nici o délce $\leq -2\cdot opt$, kde $opt$ je délka nejkrat¹í hamiltonovské kru¾nice. -Vedle pøedpokladu trojúhelníkové -nerovnosti budeme potøebovat, aby ná¹ graf byl úplný. Souhrnnì mù¾eme -pøedpokládat, ¾e úlohu øe¹íme v nìjakém metrickém protoru, ve kterém jsou obì -podmínky podle definice splnìny. - -Najdeme nejmen¹í kostru grafu a obchodnímu cestujícímu poradíme, a» jde po~ní -- kostru -zakoøeníme a projdeme jako strom do hloubky, pøièem¾ se zastavíme a¾ v koøeni po projití -v¹ech vrcholù. Problém v¹ak je, ¾e prùchod po kostøe obsahuje -nìkteré vrcholy i hrany vícekrát, a proto musíme nahradit nepovolené vracení se. -Máme-li na nìjaký vrchol vstoupit podruhé, prostì ho ignorujeme a pøesuneme se -rovnou na dal¹í nenav¹tívený -- dovolit si to mù¾eme, graf je úplný a obsahuje -hrany mezi v¹emi dvojicemi vrcholù -(jinak øeèeno, poøadí vrcholù kru¾nice bude preorder výpis prùchodem do hloubky). -Pokud platí trojúhelníková nerovnost, tak si tìmito zkratkami neu¹kodíme. -Nech» minimální kostra má váhu~$T$. Pokud bychom pro¹li celou kostru, bude mít -sled váhu~$2T$ (ka¾dou hranou kostry jsme ¹li tam a zpátky), a pøeskakování -vrcholù celkovou váhu nezvìt¹uje (pøi pøeskoku -nahradíme cestu $xyz$ jedinou hranou $xz$, pøièem¾ z trojúhelníkové nerovnosti -máme $xz \leq xy + xz$), tak¾e váha nalezené -hamiltonovské kru¾nice bude také nanejvý¹ $2T$. - -Kdy¾ máme hamiltonovskou kru¾nici $C$ a z~ní vy¹krtneme hranu, dostaneme kostru -grafu~$G$ s~váhou men¹í ne¾ $C$ -- ale ka¾dá kostra je alespoò tak tì¾ká -jako minimální kostra $T$. Tedy optimální hamiltonovská kru¾nice je urèitì tì¾¹í -ne¾ minimální kostra $T$. Kdy¾ tyto dvì nerovnosti slo¾íme -dohromady, algoritmus nám vrátí hamiltonovskou kru¾nici $T'$ s~váhou nanejvý¹ -dvojnásobnou vzhledem k optimální hamiltonovské kru¾nici ($T' \leq 2T < 2C$). Takovéto -algoritmy se nazývají {\I 2-aproximaèní}, kdy¾ øe¹ení je maximálnì dvojnásobné -od~optimálního.\foot{Hezkým trikem se v obecných metrických prostorech umí -$1{,}5$-aproximace. Ve~nìkterých metrických prostorech (tøeba v euklidovské -rovinì) se aproximaèní pomìr dá dokonce srazit na -libovolnì blízko k 1. Zaplatíme ale na èase -- èím pøesnìj¹í výsledek -po algoritmu chceme, tím déle to bude trvat.} - -\>{\I b) bez~trojúhelníkové nerovnosti:} - -Zde se budeme naopak sna¾it ukázat, ¾e ¾ádný polynomiální aproximaèní -algoritmus neexistuje. - -\s{Vìta:} Pokud pro~libovolné~$\varepsilon>0$ existuje polynomiální -$(1+\varepsilon)$-aproximaèní algoritmus pro~problém obchodního cestujícího bez~trojúhelníkové nerovnosti, tak ${\rm P = NP }$. - -\proof Uká¾eme, ¾e v~takovém pøípadì doká¾eme v~polynomiálním èase zjistit, -zda v grafu existuje hamiltonovská kru¾nice. - -\>Dostali jsme graf~$G$, ve~kterém hledáme hamiltonovskou kru¾nici. Doplníme -$G$ na~úplný graf~$G'$ a~váhy hran~$G'$ nastavíme takto: -\itemize\ibull -\: $w(e) = 1$, kdy¾ $e \in E(G)$ -\: $w(e) = c \gg 1$, kdy¾ $e \not\in E(G)$ -\endlist -\>Konstantu $c$ potøebujeme zvolit tak velkou, abychom jasnì poznali, jestli -je ka¾dá hrana z nalezené hamiltonovské kru¾nice hranou grafu $G$ (pokud by -nebyla, bude kru¾nice obsahovat aspoò jednu hranu s váhou $c$, která vy¾ene -souèet poznatelnì vysoko). Pokud existuje hamiltonovská kru¾nice v~$G'$ slo¾ená jen -z~hran, které byly -pùvodnì v~$G$, pak optimální øe¹ení bude mít váhu~$n$, jinak bude urèitì -minimálnì $n-1+c$. Kdy¾ máme aproximaèní algoritmus s~pomìrem~$1+\varepsilon$, -musí tedy být -$$ -\eqalign{ -(1+\varepsilon)\cdot n &< n-1+c \cr -\varepsilon n+1 &< c -} -$$ -\>Kdyby takový algoritmus existoval, máme polynomiální algoritmus -na~hamiltonovskou kru¾nici. -\qed - -\s{Poznámka:} O existenci pseudopolynomiálního algoritmu -platí analogická vìta, a doká¾e se analogicky -- existující hrany budou -mít váhu 1, neexistující váhu 2. - -\h{Aproximaèní schéma pro problém batohu} - -Ji¾ víme, jak optimalizaèní verzi problému batohu vyøe¹it v~èase $\O(nC)$, -pokud jsou hmotnosti i ceny na~vstupu pøirozená èísla a $C$ je souèet v¹ech cen. -Jak si poradit, pokud je~$C$ obrovské? Kdybychom mìli ¹tìstí a v¹echny -ceny byly dìlitelné nìjakým èíslem~$p$, mohli bychom je tímto èíslem -vydìlit. Tím bychom dostali zadání s~men¹ími èísly, jeho¾ øe¹ením by byla -stejná mno¾ina pøedmìtù jako u~zadání pùvodního. - -Kdy¾ nám ¹tìstí pøát nebude, mù¾eme pøesto zkusit ceny vydìlit a výsledky -nìjak zaokrouhlit. Øe¹ení nové úlohy pak sice nebude pøesnì odpovídat optimálnímu -øe¹ení té pùvodní, ale kdy¾ nastavíme parametry správnì, bude alespoò jeho dobrou aproximací. - -\s{Základní my¹lenka:} - -Oznaèíme si $c_{max}$ maximum z~cen~$c_i$. Zvolíme si nìjaké pøirozené èíslo~$M < c_{max}$ -a zobrazíme interval cen $[0, c_{max}]$ na $[0,M]$ (tedy ka¾dou cenu znásobíme -$M/c_{max}$). -Jak jsme tím zkreslili výsledek? V¹imnìme si, ¾e efekt je stejný, jako kdybychom jednotlivé -ceny zaokrouhlili na~násobky èísla $c_{max}/M$ (prvky z intervalu -$[i\cdot c_{max}/M,(i+1)\cdot c_{max}/M)$ se zobrazí na stejný prvek). Ka¾dé $c_i$ jsme tím -tedy zmìnili o~nejvý¹e $c_{max}/M$, celkovou cenu libovolné podmno¾iny pøedmìtù pak -nejvý¹e o~$n\cdot c_{max}/M$. Teï si je¹tì v¹imnìme, ¾e pokud ze~zadání odstraníme -pøedmìty, které se samy nevejdou do~batohu, má optimální øe¹ení pùvodní úlohy cenu $OPT\ge c_{max}$, -tak¾e chyba v~souètu je nejvý¹e $n\cdot OPT/M$. Má-li tato chyba být shora omezena -$\varepsilon\cdot OPT$, musíme zvolit $M\ge n/\varepsilon$.\foot{Pøipomìòme, ¾e toto je¹tì není dùkaz, nebo» velkoryse pøehlí¾íme chyby dané zaokrouhlováním. Dùkaz provedeme ní¾e.} - -\s{Algoritmus:} -\algo -\:Odstraníme ze~vstupu v¹echny pøedmìty tì¾¹í ne¾~$H$. -\:Spoèítáme $c_{max}=\max_i c_i$ a zvolíme $M=\lceil n/\varepsilon\rceil$. -\:Kvantujeme ceny: $\forall i: \hat{c}_i \leftarrow \lfloor c_i \cdot M/c_{max} \rfloor$. -\:Vyøe¹íme dynamickým programováním problém batohu pro upravené ceny $\hat{c}_1, \ldots, \hat{c}_n$ -a pùvodní hmotnosti i kapacitu batohu. -\:Vybereme stejné pøedmìty, jaké pou¾ilo optimální øe¹ení kvantovaného zadání. -\endalgo - -\>Kroky 1--3 a 5 jistì zvládneme v~èase $\O(n)$. Krok~4 øe¹í problém batohu -se souètem cen $\hat{C}\le nM = \O(n^2/\varepsilon)$, co¾ stihne v~èase $\O(n\hat{C})=\O(n^3/\varepsilon)$. -Zbývá dokázat, ¾e výsledek na¹eho algoritmu má opravdu relativní chybu nejvý¹e~$\varepsilon$. - -Nejprve si rozmyslíme, jakou cenu budou mít pøedmìty které daly optimální øe¹ení -v pùvodním zadání (tedy mají v pùvodním zadání dohromady cenu $OPT$), -kdy¾ jejich ceny nakvantujeme (mno¾inu indexù tìchto pøedmìtù si oznaèíme~$Y$): -$$ -\eqalign{ -\widehat{OPT} &= \sum_{i\in Y} \hat{c}_i = -\sum_i \left\lfloor c_i\cdot {M\over c_{max}} \right\rfloor \ge -\sum_i \left( c_i\cdot {M\over c_{max}} - 1 \right) \ge \cr -&\ge -\biggl(\sum_i c_i \cdot {M\over c_{max}}\biggr) - n = -OPT \cdot {M\over c_{max}} - n. -} -$$ -Nyní spoèítejme, jak dopadne optimální øe¹ení~$Q$ nakvantovaného problému pøi pøepoètu -na~pùvodní ceny (to je výsledek na¹eho algoritmu): -$$ -\eqalign{ -ALG &= \sum_{i\in Q} c_i \ge -\sum_i \hat{c}_i \cdot {c_{max}\over M} = -\biggl(\sum_i \hat{c}_i\biggr) \cdot {c_{max}\over M} \ge^* -\widehat{OPT} \cdot {c_{max}\over M}. -} -$$ -Nerovnost $\ge^*$ platí proto, ¾e $\sum_{i\in Q} \hat{c}_i$ je optimální øe¹ení -kvantované úlohy, zatímco $\sum_{i\in Y} \hat{c}_i$ je nìjaké dal¹í øe¹ení té¾e úlohy, -které nemù¾e být lep¹í. Teï u¾ staèí slo¾it obì nerovnosti a dosadit za~$M$: -$$ -\eqalign{ -ALG &\ge \biggl( { OPT \cdot M\over c_{max}} - n\biggr) \cdot {c_{max}\over M} \ge -OPT - {n\cdot c_{max}\over n / \varepsilon} \ge OPT - \varepsilon c_{max} \ge \cr -&\ge OPT - \varepsilon OPT = (1-\varepsilon)\cdot OPT. -} -$$ -Algoritmus tedy v¾dy vydá øe¹ení, které je nejvý¹e $(1-\varepsilon)$-krát hor¹í ne¾ optimum, -a~doká¾e to pro libovolné~$\varepsilon$ v~èase polynomiálním v~$n$. Takovému algoritmu øíkáme -{\I polynomiální aproximaèní schéma} (jinak té¾ PTAS\foot{Polynomial-Time Approximation Scheme}). -V~na¹em pøípadì je dokonce slo¾itost polynomiální i v~závislosti na~$1/\varepsilon$, tak¾e -schéma je {\I plnì polynomiální} (øeèené té¾ FPTAS\foot{Fully Polynomial-Time Approximation -Scheme}). U nìkterých problémù se stává, ¾e aproximaèní schéma závisí na -$1/\varepsilon$ exponenciálnì, co¾ tak pøíjemné není. Shròme, co jsme zjistili, do následující vìty: - -\s{Vìta:} -Existuje algoritmus, který pro ka¾dé $\varepsilon > 0$ nalezne -{\I $(1 - \varepsilon)$-aproximaci} problému batohu s $n$ pøedmìty v èase -$\O(n^3/\varepsilon)$. - -\bye diff --git a/12-apx/Makefile b/12-apx/Makefile deleted file mode 100644 index 11f9d14..0000000 --- a/12-apx/Makefile +++ /dev/null @@ -1,3 +0,0 @@ -P=12-apx - -include ../Makerules diff --git a/2-dinic/2-dinic.tex b/2-dinic/2-dinic.tex deleted file mode 100644 index 244427c..0000000 --- a/2-dinic/2-dinic.tex +++ /dev/null @@ -1,223 +0,0 @@ -\input lecnotes.tex - -\prednaska{2}{Dinicùv algoritmus}{(zapsala Markéta Popelová)} - - -Na~minulé pøedná¹ce jsme si~ukázali Fordùv-Fulkersonùv algoritmus. Tento algoritmus hledal maximální tok tak, ¾e zaèal s~tokem nulovým a~postupnì ho zvìt¹oval. Pro~ka¾dé zvìt¹ení potøeboval v~síti najít cestu, na~které mají v¹echny hrany kladnou rezervu (po takovéto cestì mù¾eme poslat více, ne¾ po~ní aktuálnì teèe). Ukázali jsme, ¾e pokud takováto cesta existuje, jde tok vylep¹it (zvìt¹it). Zároveò pokud tok jde vylep¹it, pak takováto cesta existuje. Dokázali jsme, ¾e pro~racionální kapacity je algoritmus koneèný a~najde maximální tok. - -Fordùv-Fulkersonùv algoritmus má ov¹em znaèné nevýhody. Funguje pouze pro~racionální kapacity a~je pomìrnì pomalý. Nyní si~uká¾eme jiný algoritmus, který nevylep¹uje tok pomocí cest, ale pomocí tokù\dots Budeme k~tomu potøebovat sí» rezerv. - -\s{Definice:} {\I Sí» rezerv} k~toku~$f$ v~síti $S=(V,E,z,s,c)$ je sí» $R=(S,f)=(V,E,z,s,r)$, kde~$r(e)$ je rezerva hrany~$e$ v~toku~$f$. - -\s{Konvence:} Pro~hranu~$e$ znaèí~$\overleftarrow{e}$ hranu opaènou. Napø. pokud $e=uv$, tak $\overleftarrow{e}=vu$. - -Je dùle¾ité si~uvìdomit, ¾e sí» rezerv je závislá jak na~pùvodní síti~$S$, tak na~nìjakém toku~$f$ v~síti~$S$. Sí» rezerv~$R$ se~pak od sítì~$S$ li¹í pouze kapacitami -- sí»~$R$ má jako kapacitu hrany rezervu hrany v pùvodní síti. Pro~pøipomenutí: rezervu hrany~$e$ v~síti $S=(V,E,z,s,c)$ s~tokem~$f$ jsme si~definovali jako $r(e)=c(e) - f(e) + f(\overleftarrow{e})$. - -Ne¾ si~uká¾eme samotný algoritmus, doká¾eme si~následující lemma. - -\s{Lemma:} Pro~ka¾dý tok~$f$ v~síti~$S$ a~pro~ka¾dý tok~$g$ v~síti $R=(S,f)$ lze v~èase $\O(m)$ nalézt tok~$f'$ v~síti~$S$ takový, ¾e $\vert f' \vert = \vert f \vert + \vert g \vert$. - -\proof - -Dùkaz rozdìlíme do~tøí krokù. V~prvním kroku si~uká¾eme, jak budeme tok~$f'$ v~síti~$S$ konstruovat. V~druhém kroku doká¾eme, ¾e takto zkonstruované~$f'$ je opravdu tok. A~nakonec uká¾eme, ¾e splòuje po¾adovanou vlastnost, tedy ¾e jeho velikost je souèet velikostí tokù~$f$ a~$g$. - -\>{\it 1. konstrukce~$f'$} - -\noindent -Pro ka¾dou dvojici hran~$e, \overleftarrow{e}$ urèíme~$f'(e)$ a~$f'(\overleftarrow{e})$ následovnì: - -\itemize\ibull -\:Pokud~$g(e) = g(\overleftarrow{e}) = 0$, pak nastavme: - \itemize\ibull - \:$f'(e) := f(e)$, - \:$f'(\overleftarrow{e}) := f(\overleftarrow{e})$. - \endlist - -\:Pokud~$g(e) > 0$ a~$g(\overleftarrow{e}) = 0$, pak polo¾me: - \itemize\ibull - \:$\varepsilon := \min (g(e), f(\overleftarrow{e}))$, - \:$f'(e) := f(e) + g(e) - \varepsilon$, - \:$f'(\overleftarrow{e}) := f(\overleftarrow{e}) - \varepsilon$. - \endlist - -\:Pøípad~$g(e) = 0$ a~$g(\overleftarrow{e}) > 0$ vyøe¹íme obdobnì. - -\:Pokud~$g(e) > 0$ a~$g(\overleftarrow{e}) > 0$, pak odeèteme od toku~$g$ cirkulaci po cyklu tvoøeném hranami~$e$ a $\overleftarrow{e}$: - \itemize\ibull - \:$\delta := \min (g(e),g(\overleftarrow{e}))$, - \:$g'(e) := g(e) - \delta$, - \:$g'(\overleftarrow{e}) := g(\overleftarrow{e}) - \delta$. - \endlist - - Tok $g'$ nyní spadá pod nìkterý z~pøedchozích pøípadù, které u¾ umíme vyøe¹it. - -\endlist - -\>{\it 2. $f'$ je tok} - -\numlist{\ndotted} - -\:Nejdøíve ovìøme první podmínku: $\forall e \in E: 0 \leq f(e) \leq c(e)$. Vezmìme libovolnou hranu~$e \in E$. Podle toho, co teèe po~hranách~$e$ a~$\overleftarrow{e}$ v~toku~$g$, jsme rozdìlili konstrukci toku na~tøi pøípady: - - \numlist{\ndotted} - - \:Pokud po~hranách~$e$ a~$\overleftarrow{e}$ netekl ¾ádný tok~$g$, pak jsme nastavili $f'(e) := f(e)$ a~$f'(\overleftarrow{e}) := f(\overleftarrow{e})$. Tedy pokud~$f$ dodr¾oval kapacity, tak pro~$f'$ musí platit to samé. - - - \:Pokud po~hranì~$e$ tekl tok~$g$ nenulový a~po opaèné nulový, tak jsme zvolili: $f'(e) := f(e) + g(e) - \varepsilon$. Víme, ¾e jsme si~$\varepsilon$ vybrali tak, ¾e $\varepsilon \leq g(e)$. Proto $f'(e) \geq 0$. - - Teï ovìøme, ¾e $f'(e) \leq c(e)$. V~pøípadì, ¾e $\varepsilon = g(e)$, tak $f'(e) = f(e) \leq c(e)$. V~opaèném pøípadì platí, ¾e $\varepsilon = f(\overleftarrow{e})$. Pak ov¹em - $$f'(e) = f(e) + g(e) - f(\overleftarrow{e}) \leq $$ - $$\leq f(e) + \left[ c(e) - f(e) + f(\overleftarrow{e}) \right] - f(\overleftarrow{e}) = c(e).$$ - Vyu¾ili jsme, ¾e~$g$ je tok v~síti rezerv, tedy $g(e) \leq c(e) - f(e) + f(\overleftarrow{e})$. - - Pro tok $f'(\overleftarrow{e})$ platí, ¾e $\varepsilon \leq f(\overleftarrow{e})$. Proto $f'(\overleftarrow{e}) = f(\overleftarrow{e}) - \varepsilon \geq 0$. Zároveò $f'(\overleftarrow{e}) \leq f(\overleftarrow{e}) \leq c(\overleftarrow{e})$. - - Tím jsme dokázali, ¾e~$f'(e)$ i~$f'(\overleftarrow{e})$ dodr¾ují kapacity. - - \:V posledním pøípadì tekl po~obou hranách kladný tok~$g$. Men¹í tok z~$g(e)$ a~$g(\overleftarrow{e})$ jsme vynulovali a~od vìt¹ího odeèetli ten men¹í. Tok~$g'(e)$ a~$g'(\overleftarrow{e})$ tedy zùstal korektní a~tok~$f'$ u¾ konstruujeme podle pøedchozího pøípadu. - - \endlist - -\:Teï musíme je¹tì dokázat, ¾e nový tok neporu¹uje Kirchhoffovy zákony: $$\forall v~\in V \setminus \{z,s\}: f'^\Delta(v)=0.$$ - % neboli $$\forall v~\in V \setminus \{z,s\}: \sum_{u: uv \in E}{f'(uv)}=\sum_{u: vu \in E}{f'(vu)}.$$ - - Vezmìme si~libovolnou hranu~$e = uv \in E$. Uvìdomme si, ¾e pøi~pøechodu z~$f(e)$ na~$f'(e)$ a~z~$f(\overleftarrow{e})$ na~$f'(\overleftarrow{e})$ bylo: - \itemize\idot - \:$f^\Delta(u)$ sní¾eno o~$g(e)$ - \:$f^\Delta(v)$ zvý¹eno o~$g(e)$. - \endlist - Seèteme-li úpravy na v¹ech hranách, dostaneme: $$f'^\Delta(v) = f^\Delta(v) + \sum_{u:uv \in E} g(uv) - \sum_{u:vu \in E} g(vu) =$$ $$= f^\Delta(v) + g^+(v) - g^-(v) = f^\Delta(v) + g^\Delta(v).$$ - - Jeliko¾~$f$ byl tok, tak $f^\Delta(v) = 0$ a jeliko¾~$g$ byl tok, tak $g^\Delta(v) = 0$. Proto $f'^\Delta(v) = f^\Delta(v) + g^\Delta(v) = 0$. - -\endlist - -Tím jsme dokázali, ¾e~$f'$ je tok v~síti~$S$. - -\>{\it 3. $\vert f' \vert = \vert f \vert + \vert g \vert$} - -Pou¾ijme vztah pro souèet pøebytkù z pøedchozího kroku: -$$\vert f' \vert = f'^\Delta(s) = f^\Delta(s) + g^\Delta(s) = \vert f \vert + \vert g \vert.$$ -\qed - - -Pro algoritmus budeme potøebovat vybírat kvalitní toky~$g$ v~síti rezerv. Pokud se~nám to bude daøit, bude se~tok~$f'$ rychle zvìt¹ovat, a¾ bychom mohli dojít k~maximálnímu toku. Nejlépe by se~nám hodily co nejvìt¹í toky v~síti rezerv. Kdybychom si~dali za cíl najít v¾dy maximální tok v~síti rezerv, výsledek by byl sice krásný (dostali bychom tak rovnou i~maximální tok v~pùvodní síti), ale problém hledání maximálního toku bychom pouze pøenesli na~jinou sí». Na¹e po¾adavky na~tento tok budou tedy takové, aby byl dostateènì velký, ale abychom bìhem jeho hledání nestrávili moc èasu. Podívejme se, jak se~s~tímto problémem vyrovná {\I Dinicùv algoritmus}. Nejdøíve si~ale zadefinujme nìkolik pojmù. - -\s{Definice:} Tok~$f$ je {\I blokující}, jestli¾e pro~ka¾dou orientovanou cestu~$P$ ze~$z$ do~$s$ existuje hrana~$e \in P$ taková, ¾e $f(e) = c(e)$. - -\s{Definice:} Sí» je {\I vrstevnatá (proèi¹tìná)}, kdy¾ v¹echny vrcholy a~hrany le¾í na~nejkrat¹ích cestách ze~$z$ do~$s$. - -Dinicùv algoritmus zaèíná s~nulovým tokem. Potom v¾dy podle toku~$f$ sestrojí sí» rezerv a~v~ní vyma¾e hrany s~nulovou rezervou. Pokud v~této promazané síti rezerv neexistuje cesta ze~zdroje do~stoku, tak skonèí a~prohlásí tok~$f$ za maximální. Jinak proèistí sí» rezerv tak, aby se~z ní stala vrstevnatá sí» (rozdìlí vrcholy do~vrstev podle vzdálenosti od zdroje a~odstraní pøebyteèné hrany). Ve~vrstevnaté síti najde blokující tok, pomocí nìho¾ zlep¹í tok~$f$. Pak opìt pokraèuje sestrojením sítì rezerv na~tomto vylep¹eném toku~$f$ atd. - -\figure{dinic-cistasit.eps}{Proèi¹tìná sí» rozdìlená do~vrstev}{0.4\hsize} - -\s{Algoritmus (Dinicùv)} - -\algo -\:$f \leftarrow$ nulový tok. -\:Sestrojíme sí» rezerv~$R$ a~sma¾eme $e: r(e) = 0$. -\:$l \leftarrow$ délka nejkrat¹í cesty ze~$z$ do~$s$ v~$R$. -\:Pokud $l = \infty$, zastavíme se~a vrátíme~$f$. -\:Proèistíme sí» $R \rightarrow$ sí»~$C$. -\:$g \leftarrow$ blokující tok v~$C$. -\:Zlep¹íme tok~$f$ pomocí~$g$. -\:GOTO 2. -\endalgo - -\s{Pozorování:} Pokud se~algoritmus zastaví, vydá maximální tok. - -\proof -Víme, ¾e~$f$ je stále korektní tok (jediné, jak ho mìníme je pøièítání toku~$g$, co¾ je, jak jsme si~dokázali, \uv{ne¹kodná operace}). Jakmile neexistuje cesta ze~$z$ do~$s$ v~$R$, tak je $f$ maximální tok, nebo» v~tuto dobu by se~zastavil (a vydal maximální tok) i~Fordùv-Fulkersonùv algoritmus, který je korektní. -\qed - -A teï je¹tì musíme ujasnit, jak budeme èistit sí» rezerv a~vybírat blokující tok~$g$. - -\s{Algoritmus proèi¹tìní sítì rezerv} - -\algo -\:Rozdìlíme vrcholy do~vrstev podle vzdálenosti od~$z$. -\:Odstraníme vrstvy za~$s$ (tedy vrcholy, které jsou od~$z$ vzdálenìj¹í ne¾~$s$), hrany do~minulých vrstev a~hrany uvnitø vrstev. -\:Odstraníme \uv{slepé ulièky}, tedy vrcholy s~$\deg^{out}(v) = 0$, a~to opakovanì pomocí fronty. (Nejdøíve zaøadíme do~fronty v¹echny vrcholy s~ $\deg^{out}(v) = 0$. Pak dokud není fronta prázdná, v¾dy vybereme vrchol~$v$ z~fronty, odstraníme~$v$ a~v¹echny hrany~$uv$. Pro~ka¾dý takový vrchol~$u$ zkontrolujeme, zda se~tím nesní¾il výstupní stupeò vrcholu~$u$ na~nulu ($\deg^{out}(u) = 0$). Pokud sní¾il, tak ho zaøadíme do~fronty.) -\endalgo - -\figure{dinic-neprocistenasit.eps}{Neproèi¹tìná sí». Obsahuje zpìtné hrany, hrany uvnitø vrstvy a~slepé ulièky.}{0.45\hsize} - -Hledání blokujícího toku zaèneme s~tokem nulovým. Pak vezmeme v¾dy orientovanou cestu ze~zdroje do~stoku v~síti~$C$. V~této cestì najdeme hranu s~nejni¾¹í hodnotou výrazu $r(e) - g(e)$ (neboli $c(e) - f(e)$ v~pùvodní síti). Tuto hodnotu oznaèíme~$\varepsilon$. Pak ke~ v¹em hranám na~této cestì pøièteme~$\varepsilon$. Pokud tok~$g$ na~nìjaké hranì dosáhne kapacity hrany, co¾ je zde~$r(e)$, tak hranu vyma¾eme. Následnì sí» doèistíme, aby splòovala podmínky vrstevnaté sítì. A~pokud je¹tì existuje nìjaká orientovaná cesta ze~zdroje do~stoku, tak opìt pokraèujeme s~touto cestou. - -\s{Algoritmus hledání blokujícího toku} - -\algo -\:$g \leftarrow$ nulový tok. -\:Dokud existuje orientovaná cesta~$P$ ze~$z$ do~$s$ v~$C$, opakuj: -\::$\varepsilon \leftarrow \min_{e \in P} (r(e) - g(e))$. -\::Pro~$\forall e \in P: g(e) \leftarrow g(e) + \varepsilon$. -\:::Pokud $g(e) = r(e)$, sma¾eme~$e$ z~$C$. -\::Doèistíme sí» zase pomocí fronty. -\endalgo - -\s{Èasová slo¾itost} Rozeberme si~jednotlivé kroky algoritmu. - -\numlist{\ndotted} -\:Inicializace toku~$f$ \dots $\O(m)$. -\:Sestrojení sítì rezerv a~smazání hran s~nulovou rezervou \dots $\O(m + n)$. -\:Najití nejkrat¹í cesty (prohledáváním do~¹íøky) \dots $\O(m + n)$. -\:Zkontrolování délky nejkrat¹í cesty \dots $\O(1)$. -\:Proèi¹tìní sítì \dots $\O(m + n)$. - \numlist{\ndotted} - \:Rozdìlení vrcholù do~vrstev -- provedlo ji¾ prohledávání do~¹íøky \dots $\O(1)$. - \:Odstranìní nìkterých hran \dots $\O(m + n)$. - \:Odstranìní \uv{slepých ulièek} pomocí fronty -- ka¾dou hranu odstraníme nejvý¹e jedenkrát, ka¾dý vrchol se~dostane do~fronty nejvý¹e jedenkrát \dots $\O(m + n)$. - \endlist -\:Najití blokujícího toku~$g$ \dots $\O(m \cdot n)$. - \numlist{\ndotted} - \:Inicializace toku~$g$ \dots $\O(m)$. - \:Najití orientované cesty v~proèi¹tìné síti rezerv (staèí vzít libovolnou cestu ze~zdroje, nebo» ka¾dá z~nich v~této síti vede do~stoku) \dots $\O(n)$. - \:Výbìr minima z~výrazu $r(e) - g(e)$ pøes v¹echny hrany cesty -- ta mù¾e být dlouhá nejvý¹e~$n$ \dots $\O(n)$. - \:Pøepoèítání v¹ech hran cesty \dots $\O(n)$. - \:Smazání hran cesty, jejich¾ tok~$g(e)$ se~zvý¹il na~hodnotu~$r(e)$ \dots $\O(n)$. - \:Doèi¹»ování vyøe¹me zvlá¹». - \endlist - - Vnitøní cyklus (kroky 2 a¾ 6) provedeme nejvý¹e~$m$ krát, nebo» pøi~ka¾dém prùchodu vyma¾eme alespoò jednu hranu (tak jsme si~volili~$\varepsilon$). - - Èi¹tìní bìhem celého hledání blokujícího toku~$g$ v~proèi¹tìné síti rezerv trvá dohromady $\O(m + n)$, nebo» ka¾dou hranu a~vrchol sma¾eme nejvý¹e jedenkrát. - - Najití blokujícího toku bude tedy trvat $\O(m \cdot n + (m + n)) = \O(m \cdot n)$. - -\:Zlep¹ení toku~$f$ pomocí toku~$g$ \dots $\O(m)$. -\:Skok na~2. krok \dots $\O(1)$. -\endlist - -Zbývá nám jen urèit, kolikrát projdeme vnìj¹ím cyklem (fází). Doká¾eme si~lemma, ¾e hodnota~$l$ vzroste mezi prùchody vnìj¹ím cyklem (fázemi) alespoò o~1. Z~toho plyne, ¾e vnìj¹ím cyklem mù¾eme projít nejvý¹e $n$-krát, nebo» cesta v síti na~$n$ vrcholech mù¾e být dlouhá nejvý¹e $n$. - -Uvìdomme si, ¾e uvnitø vnìj¹ího cyklu pøevládá èlen $\O(m \cdot n)$, tak¾e celková èasová slo¾itost bude $\O(n^2 \cdot m)$. - -\s{Lemma:} Hodnota~$l$ (délka nejkrat¹í cesty ze~$z$ do~$s$ v~proèi¹tìné síti) vzroste mezi fázemi alespoò o~1. - -\proof - -Podíváme se~na~prùbìh jednoho prùchodu vnìj¹ím cyklem. Délku aktuálnì nejkrat¹í cesty ze~zdroje do~stoku oznaème~$l$. V¹echny pùvodní cesty délky~$l$ se~bìhem prùchodu zaruèenì nasytí, proto¾e tok~$g$ je blokující. Musíme v¹ak dokázat, ¾e nemohou vzniknout ¾ádné nové cesty délky~$l$ nebo men¹í. V~síti rezerv toti¾ mohou hrany nejen -ubývat, ale i~pøibývat: pokud po¹leme tok po~hranì, po~které je¹tì nic neteklo, tak v~protismìru z~dosud nulové rezervy vyrobíme nenulovou. Rozmysleme si~tedy, jaké hrany mohou pøibývat. - -Hrany mohou pøibývat jen tehdy, kdy¾ jsme po~opaèné hranì nìco poslali. Ale my nìco posíláme po~hranách pouze z~vrstvy do~té následující. Hrany tedy pøibývají do~minulé vrstvy. - -Vznikem nových hran by proto mohly vzniknout nové cesty ze~zdroje do~stoku, které pou¾ívají zpìtné hrany. Jen¾e cesta ze~zdroje do~stoku, která pou¾ije zpìtnou hranu, musí alespoò jednou skoèit o~vrstvu zpìt a~nikdy nemù¾e skoèit o~více ne¾ jednu vrstvu dopøedu, a~proto je její délka alespoò $l+2$. Pokud cesta novou zpìtnou hranu nepou¾ije, má buï délku~$> l$, co¾ je v~poøádku, nebo má délku~$= l$, pak je zablokovaná. - -Tím je lemma dokázáno. -\qed - -\figure{dinic-cestashranouzpet.eps}{Cesta u¾ívající novou zpìtnou hranu}{0.4\hsize} - -V¹echna dokázaná tvrzení mù¾eme shrnout do~následující vìty: - -\s{Vìta:} Dinicùv algoritmus najde maximální tok v~èase $\O(n^2\cdot m)$. - -\s{Poznámka:} Algoritmus se~chová hezky na~sítích s~malými celoèíselnými kapacitami, ale kupodivu i~na~rùzných jiných sítích. Èasto se~pou¾ívá, nebo» se~chová efektivnì. A~je mnoho zpùsobù, jak ho je¹tì vylep¹ovat, èi odhadovat ni¾¹í slo¾itost na~speciálních sítích. - -\s{Poznámka:} Algoritmus nevy¾aduje racionální kapacity! 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a/2-toky/2-toky.tex b/2-toky/2-toky.tex new file mode 100644 index 0000000..126d81c --- /dev/null +++ b/2-toky/2-toky.tex @@ -0,0 +1,269 @@ +\input lecnotes.tex + +\prednaska{1}{Toky v sítích}{(zapsala Markéta Popelová)} + +\s{První motivaèní úloha:} {\I Rozvod èajovodu do~v¹ech uèeben.} +\smallskip + +Pøedstavme si, ¾e~by v~budovì fakulty na~Malé Stranì existoval èajovod, který +by rozvádìl èaj do~ka¾dé uèebny. Znázornìme si to orientovaným grafem, kde by +jeden významný vrchol pøedstavoval èajovar a~druhý uèebnu, ve~které sedíme. +Hrany mezi vrcholy by pøedstavovaly vìtvící se trubky, které mají èaj rozvádìt. +Jak rozvést co nejefektivnìji dostatek èaje do~dané uèebny? + +\figure{toky01.eps}{Èajovod}{2in} + +\s{Druhá motivaèní úloha:} {\I Pøenos dat.} +\smallskip + +Jiným pøíkladem mù¾e být poèítaèová sí» na~pøenos dat, která se sestává z~pøenosových linek +spojených pomocí routerù. Data se sice obvykle pøená¹ejí po~paketech, ale to +mù¾eme pøi dne¹ních rychlostech pøenosu zanedbat a pova¾ovat data za spojitá. +Jak pøená¹et data mezi dvìma poèítaèi v~síti co nejrychleji? + +\s{Definice:} {\I Sí»} je uspoøádaná pìtice $(V,E,z,s,c)$, pro ní¾ platí: +\itemize\ibull +\:$(V,E)$ je orientovaný graf. +\:$c:E\to{\bb R}_{0}^{+}$ je {\I kapacita} hran. +\:$z,s \in V$ jsou dva vrcholy grafu, kterým øíkáme {\I zdroj} a~{\I stok} (spotøebiè). +\:Graf je symetrický, tedy $\forall u,v \in V: uv \in E \Leftrightarrow vu \in E$ (tuto podmínku si~mù¾eme zvolit bez~újmy na~obecnosti, nebo» v¾dy mù¾eme do~grafu pøidat hranu, která v~nìm je¹tì nebyla, a~dát jí nulovou kapacitu). +\endlist + +\figure{sit.eps}{Pøíklad sítì. Èísla pøedstavují kapacity jednotlivých hran.}{2.5in} + +\s{Definice:} {\I Tok} je funkce $f:E \to {\bb R}_{0}^{+}$ taková, ¾e~platí: +\numlist{\ndotted} +\:Tok po~ka¾dé hranì je omezen její kapacitou: $\forall e \in E : f(e)\le c(e)$. +\:Kirchhoffùv zákon: $$\forall v \in V \setminus \{z,s\}: \sum_{u: uv \in E}{f(uv)}=\sum_{u: vu \in E}{f(vu)}.$$ Neboli pro~ka¾dý vrchol kromì zdroje a~stoku platí, ¾e~to, co do~nìj pøitéká, je stejnì velké jako to, co z~nìj odtéká (\uv{sí» tìsní}). +\endlist + +\s{Definice:} Pro libovolnou funkci $f:E \to {\bb R}$ se nám bude hodit následující znaèení: +\itemize\ibull +\:$f^+(v) = \sum_{u: uv \in E}{f(uv)}$ (celkový pøítok do vrcholu) +\:$f^-(v) = \sum_{u: vu \in E}{f(vu)}$ (celkový odtok) +\:$f^\Delta(v) = f^+(v) - f^-(v)$ (pøebytek ve~vrcholu) +\endlist + +\>(Kirchhoffùv zákon pak øíká prostì to, ¾e $f^\Delta(v)=0$ pro v¹echna $v\ne z,s$.) + +\figure{tok.eps}{Pøíklad toku. Èísla pøedstavují toky po~hranách, v~závorkách jsou kapacity.}{4in} + +\s{Pozorování:} Nìjaký tok v¾dy existuje. V libovolné síti mù¾eme v¾dy zvolit +konstantnì nulovou funkci (po~¾ádné hranì nic nepoteèe). To je korektní tok, +ale sotva u¾iteèný. Budeme chtít najít tok, který pøepraví co nejvíce tekutiny +ze~zdroje do~spotøebièe. + +\s{Definice:} {\I Velikost toku} $f$ budeme znaèit $\vert f\vert$ a polo¾íme ji +rovnou rozdílu souètu velikostí toku na~hranách vedoucích do~$s$ a~souètu velikostí +toku na~hranách vedoucích z~$s$. Neboli $\vert f\vert:=f^\Delta(s).$ + +\s{Pozorování:} Jeliko¾ sí» tìsní, mìlo by být jedno, zda velikost toku mìøíme +u~spotøebièe nebo u~zdroje. Vskutku, krátkým výpoètem ovìøíme, ¾e tomu tak je: +$$ +f^\Delta(z) - f^\Delta(s) = \sum_v f^\Delta(v) = 0. +$$ +První rovnost platí proto, ¾e podle Kirchhoffova zákona jsou zdroj a spotøebiè jediné +dva vrcholy, jejich¾ pøebytek mù¾e být nenulový. Druhou rovnost získáme tak, ¾e si +uvìdomíme, ¾e tok na ka¾dé hranì pøispìje do celkové sumy jednou s~kladným znaménkem +a jednou se záporným. Zjistili jsme tedy, ¾e pøebytek zdroje a spotøebièe se li¹í +pouze znaménkem. +\qed + +\s{Poznámka:} Rádi bychom nalezli v~zadané síti tok, jeho¾ velikost je maximální. +Máme ale zaruèeno, ¾e maximum bude existovat? V¹ech mo¾ných tokù je nekoneènì mnoho +a v~nekoneèné mno¾inì se mù¾e snadno stát, ¾e aèkoliv existuje supremum, není maximem +(pøíklad: $\{1-1/n \mid n\in{\bb N}^+\}$). +Odpovìï nám poskytne matematická analýza: mno¾ina v¹ech tokù je kompaktní podmno¾inou +prostoru ${\bb R}^{\vert E\vert}$, velikost toku je spojitá (dokonce lineární) funkce +z~této mno¾iny do~$\bb R$, tak¾e musí nabývat minima i maxima. + +Nám ale bude staèít studovat sítì s~racionálními kapacitami, kde existence maximálního +toku bude zjevná u¾ z~toho, ze sestrojíme algoritmus, který takový tok najde. + +\s{První pokus:} Hledejme cestu $P$ ze~$z$ do~$s$ takovou, ¾e~$\forall e \in +P: f(e) < c(e)$ (po~v¹ech jejích hranách teèe ostøe ménì, ne¾ jim dovolují +jejich kapacity). Pak zjevnì mù¾eme tok upravit tak, aby se~jeho velikost +zvìt¹ila. Zvolme $$\varepsilon := \min_{e \in P} (c(e) - f(e)).$$ Nový tok $f'$ +pak definujme jako $f'(e):=f(e) + \varepsilon$. Kapacity nepøekroèíme ($\varepsilon$ +je nejvìt¹í mo¾ná hodnota, abychom tok zvìt¹ili, ale nepøekroèili kapacitu ani +jedné z~hran cesty $P$) a~Kirchhoffovy zákony zùstanou neporu¹eny, nebo» zdroj +a~stok neomezují a~ka¾dému jinému vrcholu na~cestì $P$ se~pøítok $f^+(v)$ +i~odtok $f^-(v)$ zvìt¹í pøesnì o~$\varepsilon$. + +Opakujme tento proces tak dlouho, dokud existují zlep¹ující cesty. A¾ se algoritmus +zastaví (co¾ by obecnì nemusel, ale nás je¹tì chvíli trápit nemusí), získáme maximální tok? +Pøekvapivì nemusíme. Napø. na~obrázku je vidìt, ¾e~kdy¾ najdeme nejdøíve cestu +pøes hranu s~kapacitou 1 (na obrázku tuènì) a~u¾ hodnotu toku na~této hranì +nesní¾íme, tak dosáhneme velikost toku nejvý¹e 19. Ale maximální tok této sítì +má velikost 20. + +\figure{toky02.eps}{Èísla pøedstavují kapacity jednotlivých hran.}{1.5in} + +Zde by ov¹em situaci zachránilo, kdybychom poslali tok velikosti 1 proti smìru +prostøední hrany -- to mù¾eme udìlat tøeba odeètením jednièky od toku po smìru +hrany. Roz¹íøíme tedy ná¹ algoritmus tak, aby umìl posílat tok i proti smìru +hran. O~kolik mù¾eme tok hranou zlep¹it (a» u¾ pøiètením po~smìru nebo odeètením +proti smìru) nám bude øíkat její {\I rezerva:} + +\s{Definice:} {\I Rezerva hrany} $uv$ je $r(uv):=c(uv) - f(uv) + f(vu).$ + +\smallskip +Algoritmus bude vypadat následovnì. Postupnì dok¾eme, ¾e je koneèný a ¾e v~ka¾dé +síti najde maximální tok. + +\s{Algoritmus (Fordùv-Fulkersonùv)} + +\algo +\:$f \leftarrow$ libovolný tok, napø. v¹ude nulový. +\:Dokud $\exists P$ cesta ze $z$ do $s$ taková, ¾e~$\forall e \in P: r(e) > 0$, opakujeme: +\::$\varepsilon \leftarrow \min \{r(e) \mid e \in P\}$. +\::Pro v¹echny hrany $uv \in P$: +\:::$\delta \leftarrow \min \{f(vu),\varepsilon\}$ +\:::$f(vu) \leftarrow f(vu) - \delta$ +\:::$f(uv) \leftarrow f(uv) + \varepsilon - \delta$ +\:Prohlásíme $f$ za~maximální tok. +\endalgo + +\s{Koneènost:} Zastaví se~Fordùv-Fulkersonùv algoritmus? + +\itemize\ibull + +\:Pro~celoèíselné kapacity se~v~ka¾dém kroku zvìt¹í velikost toku alespoò o~1. +Algoritmus se~tedy zastaví po~nejvíce tolika krocích, kolik je nìjaká horní +závora pro~velikost maximálního toku -- napø. souèet kapacit v¹ech hran +vedoucích do~stoku (tedy $c^+(s)$). + +\:Pro~racionální kapacity vyu¾ijeme jednoduchý trik. Nech» $M$ je nejmen¹í +spoleèný násobek jmenovatelù v¹ech kapacit. Spustíme-li algoritmus na sí» +s~kapacitami $c'(e) = c(e)\cdot M$, bude se rozhodovat stejnì jako v~pùvodní +síti, proto¾e bude stále platit $f'(e) = f(e)\cdot M$. Nová sí» je pøitom +celoèíselná, tak¾e se algoritmus jistì zastaví. + +\:Na~síti s~iracionálními kapacitami se~algoritmus chová mnohdy divoce, nemusí +se~zastavit, ba ani konvergovat ke~správnému výsledku. (Zkuste vymyslet pøíklad +takové sítì.) + +\endlist + +\s{Maximalita:} Kdy¾ se algoritmus zastaví, je tok~$f$ maximální? K~tomu se +bude hodit zavést øezy. + +\s{Definice:} {\I Øez} je uspoøádaná dvojice mno¾in vrcholù ($A,B$) taková, ¾e +$A$ a $B$ jsou disjunktní, pokrývají v¹echny vrcholy, $A$ obsahuje zdroj a $B$ +obsahuje stok. Neboli $A \cap B = \emptyset$, $A \cup B = V$, $z \in A$, $s \in B$. + +\>Ka¾dému øezu pøirozenì pøiøadíme mno¾iny hran: +\itemize\ibull +\:$E^+(A,B) = E \cap (A\times B)$ (hrany \uv{zleva doprava}) +\:$E^-(A,B) = E \cap (B\times A)$ (hrany \uv{zprava doleva}) +\:$E^\Delta(A,B) = E^+(A,B) \cup E^-(A,B)$ (v¹echny hrany øezu) +\endlist + +\>Také pro libovolnou funkci $f: E\rightarrow {\bb R}$ zavedeme: +\itemize\ibull +\:$f^+(A,B) = \sum_{e\in E^+(A,B)} f(e)$ (prùtok pøes øez zleva doprava) +\:$f^-(A,B) = \sum_{e\in E^-(A,B)} f(e)$ (prùtok zprava doleva) +\:$f^\Delta(A,B) = f^+(A,B) - f^-(A,B)$ (èistý prùtok) +\endlist + +\>{\I Kapacita øezu} budeme øíkat souètu kapacit hran zleva doprava, tedy $c+(A,B)$. + +\s{Poznámka:} Øezy se~dají definovat více zpùsoby, jedna z~definic je, ¾e~øez +je mno¾ina hran grafu takových, ¾e~po~jejich odebrání se~graf rozpadne na~více +komponent. Tuto vlastnost mají i na¹e øezy, ale opaènì to nemusí platit. + +\s{Lemma:} Pro ka¾dý øez $(A,B)$ a ka¾dý tok~$f$ platí, ¾e $f^\Delta(A,B) += \vert f\vert$. (Jinými slovy velikost toku mù¾eme mìøit na libovolném øezu, +nejen na triviálních øezech kolem zdroje nebo kolem spotøebièe.) + +\proof +Opìt ¹ikovným seètením pøebytkù vrcholù: +$$ +f^\Delta(A,B) = \sum_{v\in B} f^\Delta(v) = f^\Delta(s). +$$ +První rovnost získáme poèítáním pøes hrany: ka¾dá hrana vedoucí z~vrcholu v~$B$ +do~jiného vrcholu v~$B$ pøispìje jednou kladnì a jednou zápornì; hrany le¾ící +celé mimo~$B$ nepøispìjí vùbec; hrany s~jedním koncem v~$B$ a druhým mimo pøispìjí +jednou, pøièem¾ znaménko se bude li¹it podle toho, který konec je v~$B$. Druhá +rovnost je snadná: v¹echny vrcholy v~$B$ mimo spotøebièe mají podle Kirchhoffova +zákona nulový pøebytek. +\qed + +\s{Dùsledek:} Pro ka¾dý tok~$f$ a ka¾dý øez $(A,B)$ platí $\vert f \vert \le c^+(A,B)$. +(Velikost ka¾dého toku je shora omezena kapacitou ka¾dého øezu.) + +\proof +$f^\Delta(A,B) = f^+(A,B) - f^-(A,B) \le f^+(A,B) \le c^+(A,B)$. +\qed + +\s{Dùsledek:} Pokud $\vert f\vert = c^+(A,B)$, pak je tok~$f$ maximální a øez~$(A,B)$ +minimální. Jinými slovy pokud najdeme dvojici tok a stejnì velký øez, mù¾eme øez pou¾ít +jako certifikát maximality toku. Následující vìta nám zaruèí, ¾e je to mo¾né v¾dy: + +\s{Vìta:} Pokud se~Fordùv-Fulkersonùv algoritmus zastaví, tak vydá maximální tok. + +\proof +Nech» se~Fordùv-Fulkersonùv algoritmus zastaví. Definujme mno¾inu vrcholù $A +:= \{v \in V \mid \hbox{existuje cesta ze~$z$ do~$v$ jdoucí po~hranách s~$r +> 0$}\}$ a~$B := V \setminus A$. + +Dvojice $(A,B)$ je øez, nebo» $z \in A$ (ze~$z$ do~$z$ existuje cesta délky 0) +a~$s \in B$ (kdyby $s \not\in B$, tak by musela existovat cesta ze~$z$ do~$s$ +s~kladnou rezervou, tudí¾ by algoritmus neskonèil, nýbr¾ tuto cestu vzal +a~stávající tok vylep¹il). + +Dále víme, ¾e~v¹echny hrany øezu mají nulovou rezervu, èili $\forall uv \in +E^+(A,B) : r(uv) = 0$ (kdyby mìla hrana $uv$ rezervu nenulovou, tedy kladnou, +tak by vrchol $v$ patøil do~$A$). Proto po~v¹ech hranách øezu vedoucích z~$A$ +do~$B$ teèe tolik, kolik jsou kapacity tìchto hran, a~po~hranách vedoucích +z~$B$ do~$A$ neteèe nic, tedy $f(uv) = c(uv)$ a $f(vu) = 0$. Máme øez $(A,B)$ +takový, ¾e~$f^\Delta(A,B) = c^+(A,B)$. To znamená, ¾e~jsme na¹li maximální tok +a~minimální øez. \qed + +Dokázali jsme tedy následující: + +\s{Vìta:} Pro~sí» s~racionálními kapacitami se~Fordùv-Fulkersonùv algoritmus +zastaví a~vydá maximální tok a~minimální øez. + +\s{Vìta:} Sí» s~celoèíselnými kapacitami má aspoò jeden z~maximálních tokù +celoèíselný a~Fordùv-Fulkersonùv algoritmus takový tok najde. + +\proof +Kdy¾ dostane Fordùv-Fulkersonùv algoritmus celoèíselnou sí», tak najde maximální tok a~ten bude zase celoèíselný (algoritmus nikde nedìlí). +\qed + +To, ¾e~umíme najít celoèíselné øe¹ení není úplnì samozøejmé. (U~jiných problémù takové ¹tìstí mít nebudeme.) Uka¾me si rovnou jednu aplikaci, která právì celoèíselný tok vyu¾ije. + +\s{Aplikace:} Hledání nejvìt¹ího párování v~bipartitních grafech. + +\s{Definice:} Mno¾ina hran $F \subseteq E$ se~nazývá {\I párování}, jestli¾e +¾ádné dvì hrany této mno¾iny nemají spoleèný ani jeden vrchol. Neboli $\forall +e,f \in F : e \cap f = \emptyset$. {\I Velikostí} párování myslíme poèet jeho +hran. + +\s{Øe¹ení:} +Mìjme bipartitní graf $G = (V,E)$. V~nìm hledáme nejvìt¹í párování. Sestrojme +si~sí» takovou, ¾e~vezmeme vrcholy $V$ grafu $G$ a~pøidáme k~nim dva speciální +vrcholy $z$ (zdroj) a~$s$ (stok) a~ze~zdroje pøidáme hrany do~v¹ech vrcholù +levé partity a~ze~v¹ech vrcholù pravé partity povedeme hrany do~stoku. V¹echny +kapacity nastavme na~1. Hrany bipartitního grafu zorientujme z levé partity do +pravé. Nyní staèí jen na~tuto sí» spustit Fordùv-Fulkersonùv algoritmus (nebo +libovolný jiný algoritmus, který najde maximální celoèíselný tok) a~a¾~dobìhne, +tak prohlásit hrany s~tokem 1 za~maximální párování. + +\figure{toky04.eps}{Hledání maximálního párování v~bipartitním grafu.}{2in} + +Existuje toti¾ bijekce mezi párováním a~celoèíselnými toky pøi~zachování +velikosti. Z ka¾dého celoèíselného toku na~vý¹e zmínìném grafu (viz obrázek) lze sestrojit +párování o~stejné velikosti (velikost toku zde odpovídá poètu hran bipartitního +grafu, po~kterých poteèe 1) a~naopak. Dùle¾ité je si uvìdomit, ¾e~definice toku +(omezení toku kapacitou a~Kirchhoffovy zákony) nám zaruèují, ¾e~hrany +s~nenulovým tokem (tedy jednièkovým) budou tvoøit párování (nestane se, ¾e~by +dvì hrany zaèínaly nebo konèily ve~stejném vrcholu, nebo» by se~nutnì poru¹ila +jedna ze~dvou podmínek definice toku). Potom i~maximální tok bude odpovídat +maximálnímu párování a~naopak. + +V~bipartitním grafu najdeme maximální párování v~èase $\O(n \cdot (m+n))$. Fordùv-Fulkersonùv algoritmus stráví jednou iterací èas $\O(m+n)$ (za~prohledání do~¹íøky) a~pøi~jednotkových kapacitách bude iterací nejvý¹e~$n$, proto¾e ka¾dou se~tok zvìt¹í alespoò o~1 a v¹echny toky jsou omezené øezem kolem zdroje, který má kapacitu nejvý¹e~$n$. Výsledná èasová slo¾tost hledání maximálního párování bude tedy $\O(n \cdot (m+n))$. + + +\bye diff --git a/2-toky/Makefile b/2-toky/Makefile new file mode 100644 index 0000000..e4e9173 --- /dev/null +++ b/2-toky/Makefile @@ -0,0 +1,3 @@ +P=2-toky + +include ../Makerules diff --git a/2-toky/sit.eps b/2-toky/sit.eps new file mode 100644 index 0000000..f66f5e1 --- /dev/null +++ b/2-toky/sit.eps @@ -0,0 +1,828 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: Ipelib 60027 (Ipe 6.0 preview 27) +%%CreationDate: D:20071018214606 +%%LanguageLevel: 2 +%%BoundingBox: 107 284 460 489 +%%HiResBoundingBox: 107.459 284.641 459.645 488.218 +%%DocumentSuppliedResources: font OXRFMQ+CMR10 +%%+ font GKLBST+CMR12 +%%+ font OKRINM+CMMI12 +%%+ font PVGEOP+CMR17 +%%EndComments +%%BeginProlog +%%BeginResource: procset ipe 6.0 60027 +/ipe 40 dict def ipe begin +/np { newpath } def +/m { moveto } def +/l { lineto } def +/c { curveto } def +/h { closepath } def +/re { 4 2 roll moveto 1 index 0 rlineto 0 exch rlineto + neg 0 rlineto closepath } def +/d { setdash } def +/w { setlinewidth } def +/J { setlinecap } def +/j { setlinejoin } def +/cm { [ 7 1 roll ] concat } def +/q { gsave } def +/Q { grestore } def +/g { setgray } def +/G { setgray } def +/rg { setrgbcolor } def +/RG { setrgbcolor } def +/S { stroke } def +/f* { eofill } def +/f { fill } def +/ipeMakeFont { + exch findfont + dup length dict begin + { 1 index /FID ne { def } { pop pop } ifelse } forall + /Encoding exch def + currentdict + end + definefont pop +} def +/ipeFontSize 0 def +/Tf { dup /ipeFontSize exch store selectfont } def +/Td { translate } def +/BT { gsave } def +/ET { grestore } def +/TJ { 0 0 moveto { dup type /stringtype eq + { show } { ipeFontSize mul -0.001 mul 0 rmoveto } ifelse +} forall } def +end +%%EndResource +%%EndProlog +%%BeginSetup +ipe begin +%%BeginResource: font OXRFMQ+CMR10 +%!PS-AdobeFont-1.1: CMR10 1.00B +%%CreationDate: 1992 Feb 19 19:54:52 +% Copyright (C) 1997 American Mathematical Society. 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/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/two/three/four/five/six/.notdef + /eight/nine/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef ] +ipeMakeFont +%%BeginResource: font OKRINM+CMMI12 +%!PS-AdobeFont-1.1: CMMI12 1.100 +%%CreationDate: 1996 Jul 27 08:57:55 +% Copyright (C) 1997 American Mathematical Society. All Rights Reserved. +11 dict begin +/FontInfo 7 dict dup begin +/version (1.100) readonly def +/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def +/FullName (CMMI12) readonly def +/FamilyName (Computer Modern) readonly def +/Weight (Medium) readonly def +/ItalicAngle -14.04 def +/isFixedPitch false def +end readonly def +/FontName /OKRINM+CMMI12 def +/PaintType 0 def +/FontType 1 def +/FontMatrix [0.001 0 0 0.001 0 0] readonly def +/Encoding 256 array +0 1 255 {1 index exch /.notdef put} for +dup 25 /pi put +readonly def +/FontBBox{-30 -250 1026 750}readonly def +currentdict end +currentfile eexec +d9d66f633b846a97b686a97e45a3d0aa0529731c99a784ccbe85b4993b2eebde +3b12d472b7cf54651ef21185116a69ab1096ed4bad2f646635e019b6417cc77b +532f85d811c70d1429a19a5307ef63eb5c5e02c89fc6c20f6d9d89e7d91fe470 +b72befda23f5df76be05af4ce93137a219ed8a04a9d7d6fdf37e6b7fcde0d90b +986423e5960a5d9fbb4c956556e8df90cbfaec476fa36fd9a5c8175c9af513fe +d919c2ddd26bdc0d99398b9f4d03d6a8f05b47af95ef28a9c561dbdc98c47cf5 +5250011d19e9366eb6fd153d3a100caa6212e3d5d93990737f8d326d347b7edc 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All Rights Reserved. +11 dict begin +/FontInfo 7 dict dup begin +/version (1.0) readonly def +/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def +/FullName (CMR17) readonly def +/FamilyName (Computer Modern) readonly def +/Weight (Medium) readonly def +/ItalicAngle 0 def +/isFixedPitch false def +end readonly def +/FontName /PVGEOP+CMR17 def +/PaintType 0 def +/FontType 1 def +/FontMatrix [0.001 0 0 0.001 0 0] readonly def +/Encoding 256 array +0 1 255 {1 index exch /.notdef put} for +dup 115 /s put +dup 122 /z put +readonly def +/FontBBox{-33 -250 945 749}readonly def +currentdict end +currentfile eexec +d9d66f633b846a97b686a97e45a3d0aa052a014267b7904eb3c0d3bd0b83d891 +016ca6ca4b712adeb258faab9a130ee605e61f77fc1b738abc7c51cd46ef8171 +9098d5fee67660e69a7ab91b58f29a4d79e57022f783eb0fbbb6d4f4ec35014f +d2decba99459a4c59df0c6eba150284454e707dc2100c15b76b4c19b84363758 +469a6c558785b226332152109871a9883487dd7710949204ddcf837e6a8708b8 +2bdbf16fbc7512faa308a093fe5f075ea0a10a15b0ed05d5039da41b32b16e95 +a3ce9725a429b35bad796912fc328e3a28f96fcada20a598e247755e7e7ff801 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All Rights Reserved. +11 dict begin +/FontInfo 7 dict dup begin +/version (1.0) readonly def +/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def +/FullName (CMR12) readonly def +/FamilyName (Computer Modern) readonly def +/Weight (Medium) readonly def +/ItalicAngle 0 def +/isFixedPitch false def +end readonly def +/FontName /OLBANC+CMR12 def +/PaintType 0 def +/FontType 1 def +/FontMatrix [0.001 0 0 0.001 0 0] readonly def +/Encoding 256 array +0 1 255 {1 index exch /.notdef put} for +dup 40 /parenleft put +dup 41 /parenright put +dup 43 /plus put +dup 49 /one put +dup 50 /two put +dup 51 /three put +dup 52 /four put +dup 53 /five put +dup 54 /six put +dup 56 /eight put +dup 57 /nine put +readonly def +/FontBBox{-34 -251 988 750}readonly def +currentdict end +currentfile eexec +d9d66f633b846a97b686a97e45a3d0aa052a014267b7904eb3c0d3bd0b83d891 +016ca6ca4b712adeb258faab9a130ee605e61f77fc1b738abc7c51cd46ef8171 +9098d5fee67660e69a7ab91b58f29a4d79e57022f783eb0fbbb6d4f4ec35014f +d2decba99459a4c59df0c6eba150284454e707dc2100c15b76b4c19b84363758 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/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /parenleft/parenright/.notdef/plus/.notdef/.notdef/.notdef/.notdef + /.notdef/one/two/three/four/five/six/.notdef + /eight/nine/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + 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/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef ] +ipeMakeFont +%%BeginResource: font BYMWUK+CMMI12 +%!PS-AdobeFont-1.1: CMMI12 1.100 +%%CreationDate: 1996 Jul 27 08:57:55 +% Copyright (C) 1997 American Mathematical Society. All Rights Reserved. +11 dict begin +/FontInfo 7 dict dup begin +/version (1.100) readonly def +/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def +/FullName (CMMI12) readonly def +/FamilyName (Computer Modern) readonly def +/Weight (Medium) readonly def +/ItalicAngle -14.04 def +/isFixedPitch false def +end readonly def +/FontName /BYMWUK+CMMI12 def +/PaintType 0 def +/FontType 1 def +/FontMatrix [0.001 0 0 0.001 0 0] readonly def +/Encoding 256 array +0 1 255 {1 index exch /.notdef put} for +dup 25 /pi put +dup 102 /f put +dup 119 /w put +readonly def +/FontBBox{-30 -250 1026 750}readonly def +currentdict end +currentfile eexec +d9d66f633b846a97b686a97e45a3d0aa0529731c99a784ccbe85b4993b2eebde +3b12d472b7cf54651ef21185116a69ab1096ed4bad2f646635e019b6417cc77b +532f85d811c70d1429a19a5307ef63eb5c5e02c89fc6c20f6d9d89e7d91fe470 +b72befda23f5df76be05af4ce93137a219ed8a04a9d7d6fdf37e6b7fcde0d90b +986423e5960a5d9fbb4c956556e8df90cbfaec476fa36fd9a5c8175c9af513fe +d919c2ddd26bdc0d99398b9f4d03d6a8f05b47af95ef28a9c561dbdc98c47cf5 +5250011d19e9366eb6fd153d3a100caa6212e3d5d93990737f8d326d347b7edc 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All Rights Reserved. +11 dict begin +/FontInfo 7 dict dup begin +/version (1.0) readonly def +/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def +/FullName (CMSY10) readonly def +/FamilyName (Computer Modern) readonly def +/Weight (Medium) readonly def +/ItalicAngle -14.035 def +/isFixedPitch false def +end readonly def +/FontName /QRHILV+CMSY10 def +/PaintType 0 def +/FontType 1 def +/FontMatrix [0.001 0 0 0.001 0 0] readonly def +/Encoding 256 array +0 1 255 {1 index exch /.notdef put} for +dup 0 /minus put +dup 112 /radical put +readonly def +/FontBBox{-29 -960 1116 775}readonly def +currentdict end +currentfile eexec +d9d66f633b846a97b686a97e45a3d0aa052f09f9c8ade9d907c058b87e9b6964 +7d53359e51216774a4eaa1e2b58ec3176bd1184a633b951372b4198d4e8c5ef4 +a213acb58aa0a658908035bf2ed8531779838a960dfe2b27ea49c37156989c85 +e21b3abf72e39a89232cd9f4237fc80c9e64e8425aa3bef7ded60b122a52922a +221a37d9a807dd01161779dde7d31ff2b87f97c73d63eecdda4c49501773468a +27d1663e0b62f461f6e40a5d6676d1d12b51e641c1d4e8e2771864fc104f8cbf +5b78ec1d88228725f1c453a678f58a7e1b7bd7ca700717d288eb8da1f57c4f09 +0abf1d42c5ddd0c384c7e22f8f8047be1d4c1cc8e33368fb1ac82b4e96146730 +de3302b2e6b819cb6ae455b1af3187ffe8071aa57ef8a6616b9cb7941d44ec7a +71a7bb3df755178d7d2e4bb69859efa4bbc30bd6bb1531133fd4d9438ff99f09 +4ecc068a324d75b5f696b8688eeb2f17e5ed34ccd6d047a4e3806d000c199d7c +515db70a8d4f6146fe068dc1e5de8bc57036431151ec603c8bcfe359bbd953ad +5f3d9983b036d9202c8fcc4fa88af960e1e49914ec809263862931db14b61eee +6d37a389b488d0b64cfb7da527aaed80494f79a073d895aa287bb47bd5246090 +a76ce91680c1f37e75a8804089ca4d83dbc5044e1eb9714257808bdc7df9d0b5 +fe7274d571b4b44f2e2d98e11b5cf85379f57db353ad8bca58c7047bca299c87 +a55b0066590a3136d60aebcbaeada5b3d618b68c0be7234fe688e597b2706fc4 +2f825d94ca7fea7c6b2c7e3837f5dc05986f4a5c38123e3af71cd2c0332e5a6d +5b89f35f7573055fa88425ac6992079f19b24cc22876fb11fc33bfe08990ebfb +943dcba682e4327772ae01a440c60ba4edcbd5f51e248b34dca93fbd3c560ab6 +fb4110271e9cfc5424d2ee2541071e7946d71d00d7e7fdaffe2f5eff45fd84b0 +c5d3765aac65c022989bcff5621476459905003712cdf63941655009a7b5ed3d +707c641e3e63c7476e88cf3b3247 +0000000000000000000000000000000000000000000000000000000000000000 +0000000000000000000000000000000000000000000000000000000000000000 +0000000000000000000000000000000000000000000000000000000000000000 +0000000000000000000000000000000000000000000000000000000000000000 +0000000000000000000000000000000000000000000000000000000000000000 +0000000000000000000000000000000000000000000000000000000000000000 +0000000000000000000000000000000000000000000000000000000000000000 +0000000000000000000000000000000000000000000000000000000000000000 +cleartomark +%%EndResource +/F17 /QRHILV+CMSY10 +[ /minus/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /radical/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef + /.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef/.notdef ] +ipeMakeFont +%%BeginResource: font TLUUTQ+CMR17 +%!PS-AdobeFont-1.1: CMR17 1.0 +%%CreationDate: 1991 Aug 20 16:38:24 +% Copyright (C) 1997 American Mathematical Society. All Rights Reserved. +11 dict begin +/FontInfo 7 dict dup begin +/version (1.0) readonly def +/Notice (Copyright (C) 1997 American Mathematical Society. All Rights Reserved) readonly def +/FullName (CMR17) readonly def +/FamilyName (Computer Modern) readonly def +/Weight (Medium) readonly def +/ItalicAngle 0 def +/isFixedPitch false def +end readonly def +/FontName /TLUUTQ+CMR17 def +/PaintType 0 def +/FontType 1 def +/FontMatrix [0.001 0 0 0.001 0 0] readonly def +/Encoding 256 array +0 1 255 {1 index exch /.notdef put} for +dup 40 /parenleft put +dup 41 /parenright put +dup 43 /plus put +dup 61 /equal put +dup 115 /s put +dup 122 /z put +readonly def +/FontBBox{-33 -250 945 749}readonly def +currentdict end +currentfile eexec +d9d66f633b846a97b686a97e45a3d0aa052a014267b7904eb3c0d3bd0b83d891 +016ca6ca4b712adeb258faab9a130ee605e61f77fc1b738abc7c51cd46ef8171 +9098d5fee67660e69a7ab91b58f29a4d79e57022f783eb0fbbb6d4f4ec35014f +d2decba99459a4c59df0c6eba150284454e707dc2100c15b76b4c19b84363758 +469a6c558785b226332152109871a9883487dd7710949204ddcf837e6a8708b8 +2bdbf16fbc7512faa308a093fe5f075ea0a10a15b0ed05d5039da41b32b16e95 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-0,0 +1,349 @@ + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + z + s + + + diff --git a/3-goldberg/3-goldberg.tex b/3-goldberg/3-goldberg.tex deleted file mode 100644 index 70544d4..0000000 --- a/3-goldberg/3-goldberg.tex +++ /dev/null @@ -1,283 +0,0 @@ -\input lecnotes.tex - -\prednaska{3}{Goldbergùv algoritmus}{(zapsala Markéta Popelová)} - -Pøedstavíme si~nový algoritmus pro~hledání maximálního toku v~síti, který se~uká¾e být stejnì dobrý jako {\I Dinicùv algoritmus} ($\O(MN^{2})$) a~po~nìkolika vylep¹eních bude i~lep¹í. Nejdøíve si~pøipomeòme definice, které budeme potøebovat: - -\s{Definice:} Mìjme sí» $S=(V,E,z,s,c)$, tok~$f$ a~libovolný vrchol~$v$. Pak $f^{\Delta}(v)$ nazýváme {\I pøebytek} ve~vrcholu~$v$ a~definujeme ho takto: $$f^{\Delta}(v):=\sum_{uv \in E}{f(uv)} - \sum_{vu \in E}{f(vu)}.$$ Pøebytek ve~vrcholu~$v$ je tedy souèet v¹eho, co do~vrcholu~$v$ pøiteèe, minus souèet v¹eho, co z~$v$ odteèe. - -\s{Definice:} Dále pro~libovolnou hranu~$uv \in E$ definujeme její {\I rezervu} následovnì: -$$r(uv) = c(uv) - f(uv) + f(vu).$$ Rezerva hrany znaèí, co je¹tì je mo¾no po~této hranì poslat. - -\s{Poznámka:} Dále budeme oznaèovat písmenem~$N$ poèet vrcholù a~$M$ poèet hran, tedy~$N = \vert V \vert$ a~$M = \vert E \vert$. - -Goldbergùv algoritmus na~rozdíl od~Dinicova algoritmu zaèíná s~ohodnocením hran, které pravdìpodobnì není tokem (budeme ho nazývat {\I vlna}), a~postupnì ho zmen¹uje a¾ na~korektní tok. - -\s{Definice:} Funkce $f:E \rightarrow {\bb R}_{0}^{+}$ je {\I vlna} v~síti~$(V, E, z, s, c)$ tehdy, kdy¾ jsou splnìny následující dvì podmínky: - \numlist\ndotted - \:$\forall e \in E : f(e) \leq c(e)$ (vlna na hranì nepøekroèí kapacitu hrany) - \:$ \forall v \in V \setminus \{z, s\} : f^{\Delta}(v) \geq 0$ (pøebytek ve vrcholu je nezáporný). - \endlist - -\s{Pozorování:} Ka¾dý tok~$f$ je také vlna, ale opaènì to obvykle platit nemusí. - -\s{Operace:} {\I Pøevedení pøebytku} - -Algoritmus bude potøebovat pøevádìt pøebytky z~vrcholu~$u$ na~sousední vrchol~$v$. Mìjme hranu~$uv$ s~kladnou rezervou $r(uv) > 0$ a~kladným pøebytkem ve~vrcholu~$u$: $f^\Delta(u) > 0$. Èást pøebytku budeme chtít poslat z~vrcholu~$u$ do~vrcholu~$v$. Vezmeme $\delta := \min (f^\Delta(u), r(uv))$ a~po~hranì~$uv$ po¹leme tok o velikosti~$\delta$. Výsledná situace bude vypadat následovnì: - \itemize\ibull - \:$f'^\Delta(u) = f^\Delta(u) - \delta$. - \:$f'^\Delta(v) = f^\Delta(v) + \delta$. - \:$r'(uv) = r(uv) - \delta$. - \:$r'(vu) = r(vu) + \delta$. - \endlist - -Kdybychom ov¹em nepøidali ¾ádnou jinou podmínku, ná¹ algoritmus by se~mohl krásnì zacyklit (napø. posílat pøebytek z~$u$ do~$v$ a~zase zpátky). Abychom se~tomu vyhnuli, zavedeme {\I vý¹ku vrcholu} $h: V \to {\bb N}$ a~dovolíme pøevádìt pøebytek pouze z~vy¹¹ího vrcholu~$u$ na~ni¾¹í $v$: $h(u) > h(v)$. - -\s{Shrnutí:} Podmínky pro~pøevedení pøebytku po~hranì $uv \in E$: - \numlist\ndotted - \:Ve~vrcholu~$u$ je nenulový pøebytek: $f^{\Delta}(u) > 0$. - \:Vrchol~$u$ je vý¹ ne¾ vrchol~$v$: $h(u) > h(v)$. - \:Hrana~$uv$ má nenulovou rezervu: $r(uv)>0$. - \endlist - - -\s{Operace:} Pro~vrchol~$u \in V$ definujme {\I zvednutí vrcholu}: -Pokud bìhem výpoètu narazíme ve~vrcholu~$u$ na~pøebytek, který nelze nikam pøevést, zvìt¹íme vý¹ku vrcholu~$u$ o~jednièku, tj. $h(u) \leftarrow h(u)+1$. - - -\s{Algoritmus (Goldbergùv)} - -\algo -\:$\forall v \in V: h(v)\leftarrow 0$ (v¹em vrcholùm nastavíme poèáteèní vý¹ku nula) a~$h(z)\leftarrow N$ (zdroj zvedneme do~vý¹ky~$N$). -\:$\forall e \in E: f(e)\leftarrow 0$ (po~hranách nejdøíve nenecháme protékat nic) a~$\forall zu \in E : f(zu)\leftarrow c(zu)$ (ze~zdroje pustíme maximální mo¾nou vlnu). -\:Dokud $\exists u \in V \setminus \{z,s\}: f^{\Delta}(u)>0$: -\::Pokud $\exists v \in V: uv \in E,~r(uv)>0$ a~$h(u)>h(v)$, pak pøevedeme pøebytek po~hranì z~$u$ do~$v$. -\::V~opaèném pøípadì zvedneme $u$:~$h(u) \leftarrow h(u) + 1$. -\:Vrátíme tok~$f$ jako výsledek. -\endalgo - -\noindent -Nyní bude následovat nìkolik lemmat a~invariantù, jimi¾ doká¾eme správnost a~èasovou slo¾itost Goldbergova algoritmu. - -\s{Invariant A (základní):} - \numlist \ndotted - \:Funkce~$f$ je v~ka¾dém kroku algoritmu vlna. - \:$h(v)$ nikdy neklesá pro~¾ádné~$v$. - \:$h(z)=N$ a~$h(s)=0$ po~celou dobu bìhu algoritmu. - \endlist - -\proof Indukcí dle poètu prùchodù cyklem (3. -- 5. krok algoritmu). - -Na zaèátku je v¹e v~poøádku ($f$ je nulová funkce, pøebytky v¹ech vrcholù jsou nezáporné, tedy~$f$ je vlna, $h(z)=N$ a~$h(s)=0$). V~prùbìhu se~tyto hodnoty mìní pouze pøi: - \itemize\ibull - \:Pøevedení po~hranì~$uv$: Po hranì~$uv$ se~nepo¹le více ne¾ její rezerva. Pøebytek~$u$ se~sní¾í, ale nejménì na~nulu. Pøebytek~$v$ se~zvý¹í. Tedy~$f$ zùstává vlnou. Vý¹ky se~nemìní. - \:Zvednutí vrcholu~$u$: Mìní pouze vý¹ky -- a~to vrcholù rùzných od zdroje èi stoku -- a~pouze se zvy¹ují. - \qeditem - \endlist - -\s{Invariant S (o~Spádu):} Neexistuje hrana $uv \in E: r(uv)>0$ \& $h(u) > h(v)+1$ (s~kladnou rezervou a~spádem vìt¹ím ne¾ jedna). - -\proof Indukcí dle bìhu algoritmu. - -Na zaèátku mají v¹echny hrany ze~zdroje rezervu nulovou a~v¹echny ostatní vedou mezi vrcholy s~vý¹kou 0. V~prùbìhu by se~tento invariant mohl pokazit pouze dvìma zpùsoby: - \itemize\ibull - \:Zvednutím vrcholu~$u$, ze~kterého vede hrana~$uv$ s~kladnou rezervou a~spádem 1. Tento pøípad nemù¾e nastat, nebo» hranu zvedáme pouze tehdy, kdy¾ neexistuje vrchol~$v$ takový, ¾e hrana~$uv$ má kladnou rezervu a~spád alespoò 1. Takový vrchol v~na¹em pøípadì existuje, proto se~místo zvednutí vrcholu~$u$ po¹le pøebytek po~hranì~$uv$. - \:Zvìt¹ením rezervy hrany se~spádem vìt¹ím ne¾ 1. Toto také nemù¾e nastat, nebo» rezervu bychom mohli zvìt¹it jedinì tak, ¾e bychom poslali nìco v~protismìru -- a~to nesmíme, jeliko¾ bychom poslali pøebytek z~ni¾¹ího vrcholu na~vy¹¹í. - \qeditem - \endlist - -\s{Definice:} Cestu~$P$ nazveme {\I nenasycenou}, pokud v¹echny její hrany mají kladnou rezervu. Neboli $\forall e \in P: r(e) > 0$. - -\s{Lemma K (o~Korektnosti):} Kdy¾ se~algoritmus zastaví, je~$f$ maximální tok. - -\proof Dùkaz rozlo¾me do~dvou krokù. Nejdøíve uká¾eme, ¾e~$f$ je tok, a~pak jeho maximalitu. - - \numlist\ndotted - \:Nech» se~algoritmus zastavil. Pak nemohl existovat ¾ádný vrchol~$v$ (kromì zdroje a~stoku) s~kladným pøebytkem. Tedy $\forall v \in V~\setminus \{z,s\}: f^\Delta(v) = 0$. (Víme ji¾, ¾e~$f$ je po~celou dobu vlna, tak¾e pøebytek nemù¾e být nikdy záporný.) V~tom pøípadì splòuje~$f$ podmínky toku. - \:Pro spor pøedpokládejme, ¾e tok~$f$ není maximální. Pak existuje nenasycená cesta ze~zdroje do~stoku. Vezmìme si~libovolnou takovou cestu. Zdroj je stále ve~vý¹ce~$N$ a~spotøebiè ve~vý¹ce 0 (viz invariant A). Tato cesta tedy pøekonává vý¹ku~$N$, ale mù¾e mít nejvý¹e~$N-1$ hran. Proto existuje alespoò jedna hrana se~spádem alespoò 2. Tato hrana tedy nemù¾e mít kladnou rezervu (viz invariant S). Tato cesta proto nemù¾e být zlep¹ující, co¾ je spor. Tím jsme dokázali, ¾e~$f$ je nutnì maximální tok. - \qeditem - \endlist - -\s{Lemma C (Cesta):} Mìjme vrchol $v \in V$. Pokud $f^{\Delta}(v) > 0$, pak existuje nenasycená cesta z~vrcholu~$v$ do~zdroje. - -\proof -Pro vrchol~$v \in V$ s $f^{\Delta}(v) > 0$ definujme mno¾inu $A := \{ u \in V : \exists$ nenasycená cesta z~$v$ do~$u \}$. - -Seètìme pøebytky ve~v¹ech vrcholech mno¾iny~$A$. Pøebytek ka¾dého vrcholu se~spoèítá jako souèet tokù do~nìj vstupujících minus souèet tokù z~nìj vystupujících. V¹echny hrany, jejich¾ oba vrcholy le¾í v~$A$, se~jednou pøiètou a~jednou odeètou. Proto nás budou zajímat pouze hrany mezi~$A$ a~$V \setminus A$. - - $$\sum_{u \in A}f^{\Delta}(u) = \underbrace{ \sum_{ab \in E \cap ( (V \setminus A) \times A )} f(ab) }\limits_{=0} - \underbrace{ \sum_{ab \in E \cap ( A \times (V \setminus A) )} f(ab) }\limits_{\geq 0}~\leq~0.$$ - -Uka¾me si, proè je první svorka rovna nule. Mìjme vrcholy $a \in V \setminus A$ a~$b \in A$ takové, ¾e $ab\in E$. O~nich víme, ¾e $r(ba) = 0$ (jinak by~$a$ patøilo do~$A$) $\Rightarrow f(ba) = c(ba) \Rightarrow f(ab)=0$. Proto do~$A$ nic nepøitéká. - -\figure{Goldberg01.eps}{Obrázek k dùkazu lemmatu C}{0.2\hsize} - -Proè je druhá svorka nezáporná, je zøejmé, nebo» tok na~hranì je v¾dy nezáporný a~souèet nezáporných èísel je nezáporné èíslo. - -Proto $\sum_{u \in A}{f^\Delta(u) \le 0}$. Zároveò v¹ak v~$A$ je aspoò jeden vrchol s~kladným pøebytkem, toti¾~$v$, proto v~$A$ musí být také vrchol se~záporným pøebytkem -- a~jediný takový je zdroj. Tím je dokázáno, ¾e $z \in A$, tedy ¾e vede nenasycená cesta z~vrcholu~$v$ do~zdroje. -\qed - -\s{Invariant V (Vý¹ka):} $\forall v \in V$ platí $h(v)\leq 2N$. - -\proof -Kdyby existoval vrchol~$v$ s~vý¹kou $h(v) > 2N$, tak by musel být nìkdy zvednut z~vý¹ky~$2N$. Tehdy musel mít kladný pøebytek $f^\Delta(v)>0$ (jinak by nemohl být zvednut). Dle lemmatu C musela existovat nenasycená cesta z~$v$ do~zdroje. Tato cesta mìla spád alespoò~$N$, ale mohla mít nejvý¹e~$N-1$ hran (jinak by to nebyla cesta v~síti na~$N$ vrcholech). Tudí¾ musela na~této cestì existovat hrana se~spádem alespoò 2, co¾ je spor s~invariantem S (nebo» v¹echny hrany této cesty mají z~definice nenasycené cesty kladné rezervy). -\qed - -\s{Lemma Z (poèet Zvednutí):} Poèet v¹ech zvednutí je maximálnì~$2N^{2}$. - -\proof -Staèí si~uvìdomit, ¾e ka¾dý vrchol mù¾eme zvednout maximálnì~$2N$-krát a~vrcholù je~$N$. -\qed - -Teï nám je¹tì zbývá urèit poèet provedených pøevedení. Bude se~nám hodit, kdy¾ pøevedení rozdìlíme na~dva druhy: - -\s{Definice:} Øekneme, ¾e pøevedení je {\I nasycené}, pokud po~pøevodu rezerva na~hranì~$uv$ klesla na~nulu, tedy $r(uv)=0$. V~opaèném pøípadì je {\I nenasycené}, a~tehdy urèitì klesne pøebytek ve~vrcholu~$u$ na~nulu, tedy $f^{\Delta}(u) = 0$ (pøi~nasyceném pøevedení se~to~ale mù¾e stát také). - -\s{Lemma S (naSycená pøevedení):} Poèet v¹ech nasycených pøevedení je nejvý¹~$NM$. - -\proof -Pro ka¾dou hranu~$uv$ spoèítejme poèet nasycených pøevedení (tedy takových pøevedení, ¾e po~nich klesne rezerva hrany na~nulu). Abychom dvakrát nasycenì pøevedli pøebytek (nebo jeho èást) z~vrcholu~$u$ do~vrcholu~$v$, tak jsme museli~$u$ mezitím alespoò dvakrát zvednout: - -Po~prvním nasyceném pøevedení z~vrcholu~$u$ do~vrcholu~$v$ se~vynulovala rezerva hrany~$uv$. Uvìdomme si, ¾e pøi~této operaci muselo být~$u$ vý¹e ne¾~$v$, a~dokonce víme, ¾e bylo vý¹e pøesnì o~1 (viz lemma~S). Po~této hranì tedy nemù¾eme u¾~nic více pøevést. Aby do¹lo k~druhému nasycenému pøevedení z~$u$ do~$v$, musíme nejprve opìt zvý¹it rezervu hrany~$uv$. Jediný zpùsob, jak toho lze dosáhnout, je pøevést èást pøebytku z~$v$ zpátky do~$u$. K~tomu se~musí~$v$ dostat (alespoò o~1) vý¹e ne¾~$u$. Po~pøelití bude rezerva~$uv$ opìt kladná. A~abychom provedli nasycené pøevedení znovu ve~smìru z~$u$ do~$v$, musíme zase~$u$ dostat (alespoò o~1) vý¹e ne¾~$v$. Proto musíme~$u$ alespoò o~2 zvednout -- nejprve na~úroveò~$v$ a~pak je¹tì o~1 vý¹e. - - -Ukázali jsme si~tedy, ¾e mezi ka¾dými dvìma nasycenými pøevedeními jsme vrchol~$u$ zvedli alespoò dvakrát. Nicménì libovolnou hranu mù¾eme zvednout nejvý¹e~$2N$-krát (viz invariant V). V¹ech hran je~$M$, tudí¾ poèet v¹ech nasycených pøevedení je nejvý¹e~$NM$. -\qed - -\s{Lemma N (Nenasycená pøevedení):} Poèet v¹ech nenasycených pøevedení je~$\O(N^2M)$. - -\proof -Dùkaz provedeme pomocí potenciálové metody -- nadefinujme si~následující funkci jako potenciál: - $$ \Phi := \sum_{\scriptstyle{v: f^{\Delta}(v) > 0} \atop \scriptstyle{v \ne z,s}} h(v). $$ -Nyní se~podívejme, jak se~ná¹ potenciál bìhem algoritmu vyvíjí a~jaké má vlastnosti: - - \itemize\ibull - \:Na poèátku je $ \Phi = 0 $. - \:Bìhem celého algoritmu je $ \Phi \ge 0 $, nebo» je souètem nezáporných èlenù. - \:Zvednutí vrcholu zvý¹í $\Phi$ o~jednièku (Aby byl vrchol zvednut, musel mít kladný pøebytek $\Rightarrow$ vrchol do~sumy ji¾ pøispíval, teï jen pøispìje èíslem o 1 vy¹¹ím.). Ji¾ víme, ¾e za~celý prùbìh algoritmu je v¹ech zvednutí maximálnì~$2N^2$, proto zvedáním vrcholù zvý¹íme potenciál dohromady nejvý¹e o~$2N^2$. - \:Nasycené pøevedení zvý¹í~$\Phi$ nejvý¹e o~$2N$, proto¾e buï po~pøevodu hranou~$uv$ v~$u$ zùstal nìjaký pøebytek, tak¾e se~mohl potenciál zvý¹it nejvý¹e o~$h(v) \leq 2N$, nebo je pøebytek v~$u$ po~pøevodu nulový a~potenciál se~dokonce o~jedna sní¾il. Za~celý prùbìh tak dojde k~maximálnì~$NM$ takovýmto pøevedením, díky nim¾ se~potenciál zvý¹í maximálnì o~$2N^2M$. - \:Koneènì kdy¾ pøevádíme po~hranì~$uv$ nenasycenì, tak od~potenciálu urèitì odeèteme vý¹ku vrcholu~$u$ (nebo» se~vynuluje pøebytek ve~vrcholu~$u$) a~mo¾ná pøièteme vý¹ku vrcholu~$v$. Jen¾e $h(v) = h(u) - 1$, a~proto nenasycené pøevedení potenciál v¾dy sní¾í alespoò o~jedna. - \endlist - -\>Z~tohoto rozboru chování potenciálu~$\Phi$ v~prùbìhu algoritmu získáváme, ¾e poèet v¹ech nenasycených pøevedení mù¾e být nejvý¹e $2N^2 + 2N^2M$, co¾ je $\O(N^2M)$. -\qed - -\s{Implementace:} - -Budeme si~pamatovat seznam~$P$ v¹ech vrcholù~$v \ne z,s$ s~kladným pøebytkem. Neboli -$$P = \{ v \in V \setminus \{z,s\} ~\vert~ f^{\Delta}(v) > 0 \}.$$ -Kdy¾ mìníme pøebytek nìjakého vrcholu, mù¾eme ná¹ seznam v~konstantním èase aktualizovat (napø. tak, ¾e si~ka¾dý vrchol pamatuje pozici, na~které v~seznamu~$P$ je). V~konstantním èase také umíme odpovìdìt, zda existuje nìjaký vrchol s~pøebytkem. - -Dále si~pro ka¾dý vrchol~$u \in V$ budeme pamatovat~$L(u)$-seznam hran~$uv \in E$ takových, které vedou dolù (mají spád alespoò 1) a~kladnou rezervu. Neboli -$$L(u) = \{ uv \in E ~\vert~ v \in V,~ r(uv) > 0,~ h(v) < h(u)\}.$$ -Díky tomu mù¾eme pøistupovat k~patøièným sousedùm~$u$ v~èase $\O(1)$, stejnì jako pøidávat hrany do~$L(u)$, resp. je mazat. Opìt ka¾dá hrana si~bude pamatovat pozici, na~které se~nachází v~seznamu~$L$. - -\s{Rozbor èasové slo¾itosti algoritmu:} - -\numlist\ndotted -\:Inicializace vý¹ek \dots\ $\O(N)$. -\:Inicializace vlny~$f$ \dots\ $\O(M)$. -\:Výbìr vrcholu~$u$ s~kladným pøebytkem -- vezmeme první vrchol v~$P$ \dots\ $\O(1)$. -\:Výbìr vrcholu~$v$, do~kterého vede z~$u$ hrana s~kladnou rezervou a~který je ní¾e ne¾~$u$ -- vezmeme první hranu z~$L(u)$ \dots\ $\O(1)$. - - Pøevedení pøebytku: \dots\ $\O(1)$. - \itemize\idot - \:Nasycené pøevedení \dots\ $\O(1)$. - \itemize\idot - \:Rezerva hrany~$uv$ klesne na~nulu $\Rightarrow$ hrana~$uv$ vypadne z~$L(u)$ \dots\ $\O(1)$. - \:Pøebytek vrcholu~$v$ se~zvý¹í $\Rightarrow$ pokud je¹tì nebyl v~seznamu~$P$, tak se~tam pøidá \dots\ $\O(1)$. - \:Pøebytek vrcholu~$u$ mo¾ná také klesne na~nulu $\Rightarrow$ pak by vrchol~$u$ vypadnul z~$P$ \dots\ $\O(1)$. - \endlist - \:Nenasycené pøevedení \dots\ $\O(1)$. - \itemize\idot - \:Rezerva hrany~$uv$ zùstane nezáporná $\Rightarrow$ hrana~$uv$ zùstane v~$L(u)$ \dots\ $\O(1)$. - \:Vynuluje se~pøebytek vrcholu~$u$~$\Rightarrow$ vrchol $u$ vypadne z~$P$ \dots~$\O(1)$. - \:Pøebytek vrcholu~$v$ se~zvý¹í~$\Rightarrow$ pokud je¹tì nebyl v~seznamu~$P$, tak se~tam pøidá \dots\ $\O(1)$. - \endlist - \endlist -\:Zvednutí vrcholu~$u$ \dots $\O(N)$. - -Musíme obejít v¹echny hrany do~$u$ a~z~$u$, kterých je nejvý¹e~$2N-2$, porovnat -vý¹ky a~pøípadnì tyto hrany~$uv$ odebrat ze~seznamu~$L(v)$ resp. pøidat -do~$L(u)$. Abychom pro~odebrání hrany~$uv$ ze~seznamu~$L(v)$ nemuseli procházet -celý seznam, budeme si~$\forall u \in V$ pamatovat je¹tì $L^{-1}(u) := $ seznam -ukazatelù na~hrany~$uv$ v~seznamech~$L(v)$. - -\endlist - -Vidíme, ¾e ka¾dé zvednutí je sice drahé, ale je jich zase pomìrnì málo. Naopak pøevádìní pøebytkù je èastá operace, tak¾e je výhodné, ¾e trvá konstantní èas. - -\s{Shrnutí:} - -\itemize\ibull -\:V¹ech zvednutí je $\O(N^2)$ (viz lemma Z), ka¾dé trvá $\O(N)$ \dots\ $\O(N^3).$ -\:V¹ech nasycených pøevedení je $\O(NM)$ (viz lemma S), ka¾dé trvá $\O(1)$ \dots\ $\O(NM).$ -\:V¹ech nenasycených pøevedení je $\O(N^2M)$ (viz lemma N), ka¾dé trvá $\O(1)$ \dots\ $\O(N^2M).$ -\endlist - -Dohromady má tedy Goldbergùv algoritmus èasovou slo¾itost $\O(N^2M)$. Vidíme, ¾e u¾ v~tomto obecném pøípadì to není hor¹í ne¾ Dinicùv algoritmus. Pøí¹tì si~uká¾eme, ¾e mù¾e mít i~mnohem lep¹í. Nejdøíve ale zformulujme v¹echna dokázaná tvrzení do~následující vìty: - -\s{Vìta:} Goldbergùv algoritmus najde maximální tok v~èase $\O(N^2M)$. - -\s{Pozorování:} Pokud bychom volili v¾dy nejvy¹¹í z~vrcholù s~pøebytkem, tak by se~mohl algoritmus chovat lépe. Podívejme se~na~to pozornìji a~vylep¹ený Goldebrgùv algoritmus oznaème G'. - -\s{Algoritmus (Vylep¹ený Goldbergùv algoritmus)} - -\algo -\:$\forall v \in V: h(v)\leftarrow 0$ (v¹em vrcholùm nastavíme poèáteèní vý¹ku nula) a~$h(z)\leftarrow N$ (zdroj zvedneme do~vý¹ky~$N$). -\:$\forall e \in E: f(e)\leftarrow 0$ (po~hranách nejdøíve nenecháme protékat nic) a~$\forall zu \in E : f(zu)\leftarrow c(zu)$ (ze~zdroje pustíme maximální mo¾nou vlnu). -\:Dokud $\exists u \in V \setminus \{z,s\}: f^{\Delta}(u)>0$: -\::Vybereme z~vrcholù s~pøebytkem ten s~nejvy¹¹í vý¹kou, oznaèíme ho~$u$. -\:::Pokud $\exists v \in V: uv \in E,~r(uv)>0$ a~$h(u)>h(v)$, pak pøevedeme pøebytek po~hranì z~$u$ do~$v$. -\:::V~opaèném pøípadì zvedneme $u$:~$h(u) \leftarrow h(u) + 1$. -\:Vrátíme tok~$f$ jako výsledek. -\endalgo - -Rozmysleme si, o~kolik bude vylep¹ený algoritmus G' lep¹í ne¾ ten pùvodní. Ten pùvodní mìl èasovou slo¾itost $\O(N^2M)$ a~pøevládal èlen, který odpovídal nenasyceným pøevedením. Zkusme tedy právì poèet nenasycených pøevedení odhadnout ve~vylep¹eném algoritmu o~nìco tìsnìji. - -\s{Lemma N' (Nenasycená pøevedení):} Algoritmus G' provede~$\O(N^3)$ nenasycených pøevedení. - -\proof -Dokazovat budeme opìt pomocí potenciálové metody. Zadefinujme si~potenciál {\I nejvy¹¹í hladinu s~pøebytkem}: -$$H := \max \{ h(v) \mid v \neq z,s ~\&~ f^\Delta(v) > 0\}.$$ -Rozdìlíme bìh algoritmu na~{\I fáze}. Ka¾dá fáze konèí tím, ¾e~se~$H$ zmìní. Jak se~mù¾e zmìnit? Buï se~$H$ zvý¹í, co¾ znamená, ¾e~nìjaký vrchol s~pøebytkem v~nejvy¹¹í hladinì byl o~1 zvednut, nebo se~$H$ sní¾í. My víme, ¾e zvednutí je v~celém algoritmu $\O(N^2)$. Zároveò si~mù¾eme uvìdomit, ¾e~$H$ je nezáporný potenciál, kdy sní¾ení i~zvý¹ení ho zmìní o~1, tedy poèet sní¾ení bude stejný jako poèet zvý¹ení, a~proto obojího je~$\O(N^2)$. Tudí¾ poèet fází je také~$\O(N^2)$. - -Je dùle¾ité, ¾e~bìhem jedné fáze provedeme nejvý¹e jedno nenasycené pøevedení z~ka¾dého vrcholu. Po~ka¾dém nenasyceném pøevedení po~hranì $uv$ se~toti¾ vynuluje pøebytek v~$u$ a~aby se~provedlo dal¹í nenasycené pøevedení z~vrcholu~$u$, muselo by nejdøíve být co~pøevádìt. Muselo by tedy do~$u$ nìco pøitéci. My ale víme, ¾e pøevádíme pouze shora dolù a~$u$ je v~nejvy¹¹í hladinì (to zajistí právì to vylep¹ení algoritmu), tedy nejdøíve by musel být nìjaký jiný vrchol zvednut. Tím by se~ale zmìnilo~$H$ a~skonèila by tato fáze. - -Proto poèet v¹ech nenasycených pøevedení bìhem jedné fáze je nejvý¹e~$N$. A ji¾ jsme dokázali, ¾e~fází je~$\O(N^2)$. Tedy poèet v¹ech nenasycených pøevedení je~$\O(N^3)$. -\qed - -Tento odhad je hezký, ale stále není tìsný a~algoritmus se~chová lépe. Doka¾me si~je¹tì jeden tìsnìj¹í odhad na~poèet nenasycených pøevedení. - -\s{Lemma N'' (Nenasycená pøevedení):} Poèet nenasycených pøevedení je~$\O(N^2 \sqrt{M})$. - -\s{Poznámka:} Tato èasová slo¾itost je výhodná napøíklad pro~øídké grafy. Ty mají toti¾ pomìrnì malý poèet hran. - -\proof -Rozdìlme si~fáze na~dva druhy: laciné a~drahé podle toho, kolik se~v~nich provede nenasycených pøevedení. Zvolme si~nìjaké nezáporné~$K$. Zatím nebudeme urèovat jeho hodnotu. Uvidíme, ¾e~èasová slo¾itost algoritmu bude závislá na~tomto parametru~$K$. Proto jeho hodnotu zvolíme a¾ pozdìji a~to tak, aby byla slo¾itost co nejni¾¹í. - -{\I Laciné fáze} budou ty, bìhem nich¾ se~provede nejvý¹e~$K$ nenasycených pøevedení. {\I Drahé fáze} budou ty ostatní, tedy takové, bìhem nich¾ se~provede více jak~$K$ nenasycených pøevedení. - -Teï potøebujeme odhadnout, kolik nás budou stát oba typy fází. Zaènìme s~tìmi jednodu¹¹ími -- s~lacinými. Víme, ¾e~v¹ech fází je~$\O(N^2)$. Tìch laciných bude tedy urèitì také~$O(N^2)$. Nenasycených pøevedení se~bìhem jedné laciné fáze provede nejvíce~$K$. Tedy celkem se~bìhem laciných fází provede~$\O(N^2K)$ nenasycených pøevedení. - -Pro~poèet nenasycených pøevedení v~drahých fázích si~zaveïme nový potenciál definovaný následovnì: -$$\Phi := \sum_{\scriptstyle{v \ne z,s} \atop \scriptstyle{f^{\Delta}(v) \ne 0}} {p(v) \over K},$$ -kde~$p(v)$ je poèet takových vrcholù~$u$, které nejsou vý¹e ne¾~$v$. Neboli -$$p(v) = \vert \{ u \in V \mid h(u) \leq h(v) \} \vert.$$ -Tedy platí, ¾e~$p(v)$ je v¾dy nezáporné a~nejvý¹e má hodnotu~$N$. Dále víme, ¾e~$\Phi$ bude v¾dy nezáporné (nebo» je to souèet nezáporných èlenù) a~nejvý¹e bude nabývat hodnoty~$N^2 \over K$. Rozmysleme si, jak nám ovlivní tento potenciál na¹e tøi operace: -\itemize\ibull -\:{\bf Zvednutí}: Za~ka¾dý zvednutý vrchol pøibude nejvý¹e~$N \over K$ (tento vrchol mù¾e být nadzvednut nejvý¹e nad~v¹echny ostatní vrcholy) a~mo¾ná nìco ubude (napø. kdy¾ vrchol vyzvedneme na~úroveò k~ostatním). -\:{\bf Nasycené pøevedení} po~hranì $uv$: Mù¾e vynulovat pøebytek ve~vrcholu~$u$, pak se~$\Phi$ sní¾í. Mù¾e zvý¹it pøebytek ve~$v$ z~nuly, pak se~$\Phi$ zvý¹í. Ale nejvý¹e se~zvý¹í o~$N \over K$, nebo» do~$\Phi$ pøibude jen jeden sèítanec za~vrchol $v$ a~ten pøispìje nejvý¹e hodnotou~$N \over K$ (pod ním mù¾e být nejvíce~$N$ vrcholù). -\:{\bf Nenasycená pøevedení} po~hranì $uv$ v~drahých fázích: Tato operace vynuluje pøebytek v~$u$, tedy~$\Phi$ klesne alespoò o~$p(u) \over K$. Zároveò mù¾e zvý¹it pøebytek ve~$v$ z~nuly, ale~$\Phi$ stoupne nejvý¹e o~$p(v) \over K$. Celkem tedy~$\Phi$ klesne alespoò o~$p(u) - p(v) \over K$. -\endlist -Uvìdomme si, ¾e~pokud pøevádíme po~hranì~$uv$, tak platí, ¾e~$h(u) = h(v) + 1$. Pak~$p(u) - p(v)$ je pøesnì poèet vrcholù na~hladinì~$H$. Tìch je alespoò tolik, kolik je nenasycených pøevedení bìhem jedné fáze (to jsme dokázali ji¾ v~lemmatu N'), a~my jsme si~zadefinovali, ¾e v~drahé fázi je poèet nenasycených pøevedení alespoò~$K$. Tedy~$p(u) - p(v) > K$. Proto bìhem jednoho nenasyceného pøevedení~$\Phi$ klesne alespoò o~${K \over K} = 1$. Nenasycená pøevedení potenciál nezvy¹ují. - -Potenciál~$\Phi$ se~mù¾e zvìt¹it pouze pøi~operacích zvednutí a~nasycené pøevedení. Zvednutí se~provede celkem~$\O(N^2)$ a~ka¾dé zvý¹í potenciál nejvý¹e o~$N \over K$. Nasycených pøevedení se provede celkem~$\O(NM)$ a~ka¾dé zvý¹í potenciál takté¾ nejvý¹e o~$N \over K$. Celkem se~tedy~$\Phi$ zvý¹í nejvý¹e o -$${N \over K} \O(N^2) + {N \over K} \O(NM) = \O \left({N^3 \over K} + {N^2M \over K}\right).$$ - -Teï vyu¾ijeme toho, ¾e~$\Phi$ je nezáporný potenciál, tedy kdy¾ ka¾dé nenasycené pøevdení v~drahé fázi sní¾í~$\Phi$ alespoò o~1, tak v¹ech nenasycených pøevdení v~drahých fázích je~$\O({N^3 \over K} + {N^2M \over K})$. U¾ jsme ukázali, ¾e~nenasycených pøevední v~laciných fázích je~$\O(N^2K)$. Proto celkem v¹ech nenasycených pøevedení je -$$\O \left(N^2K + {N^3 \over K} + {N^2M \over K} \right) = \O \left(N^2K + {N^2M \over K} \right)$$ -(nebo» pro~souvislé grafy platí, ¾e~$M \geq N \Rightarrow N^2M \geq N^3$). A~my chceme, aby jich bylo co nejménì. 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-grestore -grestore -grestore -showpage -%%EOF diff --git a/3-goldberg/Makefile b/3-goldberg/Makefile deleted file mode 100644 index 6330e4c..0000000 --- a/3-goldberg/Makefile +++ /dev/null @@ -1,3 +0,0 @@ -P=3-goldberg - -include ../Makerules diff --git a/4-hradla/4-hradla.tex b/4-hradla/4-hradla.tex deleted file mode 100644 index ccb0c50..0000000 --- a/4-hradla/4-hradla.tex +++ /dev/null @@ -1,139 +0,0 @@ -\input ../lecnotes.tex - -\prednaska{4}{Hradlové sítì}{(zapsal: Petr Jankovský)} - -\def\land{\mathbin{\&}} - -Výkon poèítaèù nelze zvy¹ovat donekoneèna a pøesto¾e ji¾ pìkných pár let platí, -¾e se jejich rychlost s~èasem exponenciálnì zvìt¹uje, jednou urèitì narazíme -pøinejmen¹ím na~fyzikální limity. - -Kdy¾ tedy nemù¾eme zvy¹ovat rychlost jednoho procesoru, jak poèítat rychleji? -Øe¹ením by mohlo být poøídit si procesorù víc. U¾~dnes na~bì¾ném PéCéèku máme -k~dispozici vícejádrové procesory, díky nim¾ mù¾eme vyu¾ít pararelní poèítání -a úlohu øe¹it tak, ¾e práci ¹ikovnì rozdìlíme mezi procesory (èi jádra) a -zamìstnáme je pøi~výpoètu v¹echny. - -My se podíváme na~abstraktní výpoèetní model, který je je¹tì paralelnìj¹í. -Techniky, které si uká¾eme na~tomto modelu, se v¹ak dají pøekvapivì vyu¾ít i~pøi~reálném -paralelizování na~nìkolika málo procesorech. Konec koncù i proto, ¾e vnitøní -architektura procesoru se na¹emu modelu velmi podobá. Budeme se zabývat -jednoduchým modelem paralelního poèítaèe, toti¾ hradlovou sítí. - -\h{Hradlové sítì} - -\s{Definice:} {\I Hradlo} je prvek, který umí vyhodnocovat nìjakou funkci -nad~koneènou abecedou $\Sigma$. - -Obecnì se na~hradlo díváme jako na~funkci $f: {\Sigma}^{k} \rightarrow \Sigma$, která dostane $k$ vstupù -a~vrátí jeden výstup, pøièem¾ hodnoty, nad~kterými pracuje, budou z~nìjaké koneèné -abecedy -- tedy z~nìjaké koneèné mno¾iny symbolù $\Sigma$. Písmenku $k$ zde øíkáme {\I arita -hradla}. - -\s{Pøíklad:} Èasto studujeme hradla booleovská (pracující nad abecedou $\{0,1\}$), která poèítají logické funkce. - -Z~nich nejèastìji potkáme: - -\itemize\ibull -\:nulární: to jsou konstanty (FALSE=0, TRUE=1), -\:unární: napø. negace (znaèíme~$\lnot$), -\:binární: logický souèin ({\sc and},~$\land$), souèet ({\sc or},~$\lor$), ... -\endlist - -\>Hradla kreslíme tøeba následovnì: - -\figure{hradlo_and.eps}{Binární hradlo provádící logickou operaci {\sc and}.}{1in} - -Jednotlivá hradla mù¾eme navzájem urèitým zpùsobem propojovat a vytváøet -z nich {\I hradlové sítì}. Pokud pou¾íváme pouze booleovská hradla, øíkáme takto vytvoøeným -sítím {\I booleovské obvody}. Pokud pracujeme s~operacemi nad nìjakou obecnìj¹í (ale koneènou) -mno¾inou symbolù (abecedou), nazývají se {\I kombinaèní obvody.} - -Ka¾dá hradlová sí» má nìjaké vstupy, nìjaké výstupy a uvnitø jsou propojovaná -hradla. Ka¾dý vstup hradla je pøipojen buïto na~nìkterý ze~vstupù sítì nebo -na~výstup jiného hradla. Výstupy hradel mohou být propojeny na~vstupy dal¹ích -hradel (mohou se vìtvit), nebo na výstupy sítì. Pøitom máme zakázáno vytváøet -cykly. - -Ne¾ si øekneme formální definici, podívejme se na obrázek. - -\figure{hradlova_sit.eps}{Hradlová sí» -- tøívstupová verze funkce {\I majorita}.}{3in} - -Obrazek znázoròuje hradlovou sí», která poèítá, zda je alespoò na~dvou ze~vstupù -jednièka. Pojïme si ale {\I hradlovou sí»} definovat formálnì. - -\s{Definice:} {\I Hradlová sí»} je urèena: -\itemize\ibull -\:{\I abecedou} $\Sigma$ (to je nìjaká koneèná mno¾ina symbolù, obvykle $\Sigma=\{0,1\}$); -\:po dvou disjunktními koneènými mno¾inami $I$~({\I vstupy}), $O$~({\I výstupy}) \hfil\break a~$H$~({\I hradla}); -\:acyklickým orientovaným multigrafem~$(V,E)$, kde~$V = I \cup O \cup H$; -\:zobrazením~$F$, které ka¾dému hradlu $h\in H$ pøiøadí nìjakou funkci~$F(h): - \Sigma^{a(h)} \rightarrow \Sigma$, co¾ je funkce, kterou toto hradlo vykonává. - Èíslu $a(h)$ øíkáme {\I arita} hradla~$h$; -\:zobrazením~$z: E \rightarrow {\bb N}$, které ka¾dé hranì vedoucí do~nìjakého - hradla pøiøazuje nìkterý ze vstupù tohoto hradla. -\endlist - -\>Pøitom jsou splnìny následující podmínky: - -\itemize\ibull -\:$\forall i \in I: \deg^{in}(i)=0$ (do~vstupù nic nevede); -\:$\forall o \in O: \deg^{in}(o)=1~\land~\deg^{out}(o)=0$ (z~výstupù nic nevede a do~ka¾dého vede právì jedna hrana); -\:$\forall h \in H: \deg^{in}(v)=a(v)$ (do~ka¾dého hradla vede tolik hran, kolik je jeho arita); -\:$\forall h \in H~\forall j: 1\le j\le a(h)$ existuje právì jedena hrana~$e$ taková, ¾e~$e$ konèí v~$h$ a~$z(e)=j$, - (v¹echny vstupy hradel jsou zapojeny). -\endlist - -\s{Pozorování:} Kdybychom pøipustili hradla s~libovolnì vysokým poètem vstupù, mohli -bychom libovolný problém se vstupem délky~$n$ vyøe¹it jedním hradlem o~$n$~vstupech, -kterému bychom pøiøadili funkci, která by na¹i úlohu rovnou vyøe¹ila. Tento model -v¹ak není ani realistický, ani pìkný. Proto pøijmìme omezení, ¾e~arity v¹ech hradel -budou omezeny nìjakou pevnou konstantou $k$ (uká¾e se, ¾e nám bude staèit dvojka -a~vystaèíme si tedy pouze s nulárními, unárními a binárními hradly). -Následující obrázky ukazují, jak hradla o~více vstupech nahradit dvouvstupovými: - -\twofigures{hradlo_ternor.eps}{Trojvstupové hradlo \sc or.}{0.5in}{hradlo_ternbior.eps}{Jeho nahrazení 2-vstupovými hradly.}{0.6in} - - -Nyní bychom je¹tì mìli definovat, co taková hradlová sí» vlastnì poèítá a~jak -její výpoèet probíhá. - -\s{Definice:} {\I Výpoèet sítì} probíhá po~{\I taktech.} V nultém taktu jsou definovány pouze -hodnoty na~vstupech sítì a na~výstupech hradel arity 0. Mù¾eme si to pøedstavit -tak, ¾e na~zaèátku nemá ¾ádné hradlo definovánu výstupní hodnotu (a¾ na ji¾ -zmínìná hradla nulární). V~ka¾dém dal¹ím taktu pak vydají výstup hradla, která -na~konci minulého taktu mìla definovány v¹echny hodnoty na vstupech. Jakmile -budou po~nìjakém koneèném poètu taktù definované i hodnoty v¹ech výstupù, sí» -se zastaví a~vydá výsledek. - -\s{Pozorování:} Proto¾e je sí» acyklická, je jasné, ¾e jakmile jednou nìjaké hradlo vydá -výstup, tak se tento výstup bìhem dal¹ího výpoètu sítì ji¾ nezmìní. - -\figure{vypocet_site.eps}{Výpoèet hradlové sítì.}{6cm} - -\>Podle toho, jak sí» poèítá, si ji mù¾eme rozdìlit na~vrstvy: - -\s{Definice:} {\I $i$-tá vrstva $S_i$} obsahuje právì takové vrcholy~$v$, pro~které nejdel¹í cesta ze~vstupù -sítì do $v$ má délku rovnou~$i$. - -\s{Pozorování:} V¹imnìme si, ¾e v $i$-tém taktu vydají hodnoty právì hradla z $S_i$. - -Dává tedy smysl prohlásit za~{\I èasovou slo¾itost} sítì poèet -jejích vrstev. Podobnì {\I prostorovou slo¾itost} definujeme jako poèet hradel v~síti. - -\s{Pøíklad:} Sestrojme sí», která zjistí, zda se mezi jejími~$n$ vstupy -vyskytuje alespoò jedna jednièka. - -\>{\I První øe¹ení:} zapojíme hradla za~sebe (sériovì). Èasová i prostorová -slo¾itost odpovídají~$\Theta(n)$. Zde ov¹em vùbec nevyu¾íváme toho, ¾e by mohlo poèítat více -hradel souèasnì. - -\figure{hloupy_or.eps}{Hradlová sí», která zjistí, zdali je na vstupu alespoò jedna jednièka.}{0.7in} - -\>{\I Druhé øe¹ení:} Hradla budeme spojovat do~dvojic, pak výsledky z~tìchto dvojic opìt -do~dvojic a tak dále. Díky paralelnímu zapojení dosáhneme èasové slo¾itosti $\Theta(\log n)$, -prostorová slo¾itost zùstane lineární. - -\figure{chytry_or.eps}{Chytøej¹í øe¹ení stejného problému pro vstup velikosti 16.}{3in} - -\bye diff --git a/4-hradla/Makefile b/4-hradla/Makefile deleted file mode 100644 index 65a25ae..0000000 --- a/4-hradla/Makefile +++ /dev/null @@ -1,3 +0,0 @@ -P=4-hradla - -include ../Makerules diff --git a/4-hradla/chytry_or.eps b/4-hradla/chytry_or.eps deleted file mode 100644 index 3d88cff..0000000 --- a/4-hradla/chytry_or.eps +++ /dev/null @@ -1,2324 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Creator: inkscape 0.46 -%%Pages: 1 -%%Orientation: Portrait -%%BoundingBox: 2 325 592 773 -%%HiResBoundingBox: 2.9557909 325.79571 591.83322 772.94254 -%%EndComments -%%Page: 1 1 -0 842 translate -0.8 -0.8 scale -0 0 0 setrgbcolor -[] 0 setdash -1 setlinewidth -0 setlinejoin -0 setlinecap -gsave [1 0 0 1 0 0] concat -gsave [1.086765 0 0 1.086765 -114.17957 349.28806] concat -grestore -gsave 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zavedli paralelní výpoèetní model, ve kterém si nyní nìco naprogramujeme \dots - -\h{Sèítání binárních èísel} - -Mìjme dvì èísla $x$ a $y$ zapsané ve~dvojkové soustavì. Jejich èíslice oznaème -$x_{n-1}\ldots x_0$ a $y_{n-1}\ldots y_0$, kde $i$-tý øád má váhu $2^i$. Nyní budeme chtít tato èísla -seèíst. - -K tomuto úèelu se~ihned nabízí pou¾ít starý dobrý \uv{¹kolní algoritmus}, který -funguje ve~dvojkové soustavì stejnì dobøe jako v~desítkové. Zkrátka sèítáme èísla -zprava doleva. V¾dy seèteme pøíslu¹né èíslice pod~sebou a~pøièteme pøenos z~ni¾¹ího -øádu. Tím dostaneme jednu èíslici výsledku a~pøenos do~vy¹¹ího øádu. Formálnì -bychom to mohli zapsat tøeba takto: -$$ -z_i=x_i \oplus y_i \oplus c_i, -$$ -kde $z_i$ je $i$-tá èíslice souètu, $\oplus$ znaèí operaci {\sc xor} (souèet modulo~2) a~$c_i$ je {\I pøenos} -z~$(i-1)$-ního øádu do~$i$-tého. Pøenos pøitom nastane tehdy, pokud se~nám potkají -dvì jednièky pod~sebou, nebo kdy¾ se~vyskytne alespoò jedna jednièka a~k~tomu -pøenos z~ni¾¹ího øádu. Jinými slovy tehdy, kdy¾ ze~tøí xorovaných èíslic jsou alespoò -dvì jednièky (pomocí obvodu pro majoritu z minulé pøedná¹ky lehce zkonstruujeme): -$$ -\eqalign{ -c_{0} &= 0, \cr -c_{i+1} &= (x_i~\&~y_i)\lor(x_i~\&~c_i)\lor(y_i~\&~c_i).\cr -} -$$ - -Takovéto sèítání sice perfektnì funguje, nicménì je bohu¾el pomìrnì pomalé. -Pokud bychom stavìli hradlovou sí» podle tohoto pøedpisu, byla by slo¾ená z~nìjakých -podsítí (\uv{krabièek}), které budou mít na~vstupu $x_i$, $y_i$ a~$c_i$ a jejich výstupem -bude $z_i$ a~$c_{i+1}$. - -\figure{hloupe_scitani.eps}{Sèítání ¹kolním algoritmem.}{1.5in} - -V¹imìme si, ¾e~ka¾dá krabièka závisí na~výstupu té pøedcházející. Jednotlivé -krabièky tedy musí urèitì le¾et na~rùzných hladinách. Celkovì bychom museli pou¾ít -$\Theta{(n)}$ hladin a~jeliko¾ je ka¾dá krabièka konstantnì velká, také $\Theta{(n)}$ hradel. To dává -lineární èasovou i~prostorovou slo¾itost, èili oproti sekvenènímu algoritmu jsme si nepomohli. - -Zamysleme se nad tím, jak by se proces sèítání mohl zrychlit. - -\h{Pøenosy v~blocích} -Jediné, co nás pøi sèítání brzdí, jsou pøenosy mezi jednotlivými øády. Ka¾dý øád, -aby~vydal souèet, musí poèkat na~to, a¾~dopoèítají v¹echny pøedcházející øády. -Teprve pak se~toti¾ dozví pøenos. Kdybychom ov¹em pøenosy dokázali spoèítat -nìjakým zpùsobem paralelnì, máme vyhráno. Jakmile známe v¹echny pøenosy, souèet -u¾~zvládneme dopoèítat na~konstantnì mnoho hladin -- tedy v~konstantním èase. - -Podívejme se na~libovolný {\I blok} v~na¹em souètu. Tak budeme øíkat èíslùm -$x_j\ldots x_i$ a $y_j\ldots y_i$ v~nìjakém intervalu indexù $\left$. Pøenos $c_{j+1}$ vystupující z~tohoto bloku závisí mimo hodnot sèítancù u¾ pouze na~pøenosu $c_{i}$, který do bloku vstupuje. - -\figure{blok_scitani.eps}{Blok souètu.}{3in} - -Z tohoto pohledu se mù¾eme na blok také dívat jako na nìjakou funkci, která -dostane jednobitový vstup a vydá jednobitový výstup. To je nám pøíjemné, nebo» -takových funkcí existují jenom ètyøi typy: -\numlist\ndotted -\:$f(x) = 0$, ~~~~(0) -\:$f(x) = 1$, ~~~~(1) -\:$f(x) = x$, ~~~~($<$ -- kopírování) -\:$f(x) = \neg{x}$. -\endlist -Jak se za~chvíli uká¾e, poslední pøípad, kdy by~nìjaký blok pøedával opaèný -pøenos, ne¾ do~nìj vstupuje, navíc nikdy nemù¾e nastat. Pojïme si to rozmyslet. -Jednobitové bloky se chovají velice jednodu¹e: - -\figure{bloky_1bit.eps}{Tabulka triviálních blokù.}{1.1in} - -Z prvního bloku evidentnì v¾dy vyleze 0, a»~do~nìj vstoupí jakýkoli pøenos. -Poslední blok naopak sám o~sobì pøenos vytváøí, a»~ji¾ do~nìj vleze jakýkoliv. -Bloky prostøední se chovají stejnì, a~to tak, ¾e~samy o~sobì ¾ádný pøenos nevytvoøí, -ale~pokud do~nich nìjaký pøijde, tak~také odejde. - -Mìjme nyní nìjaký vìt¹í blok~$C$ slo¾ený ze~dvou men¹ích podblokù $A$ a~$B$, jejich¾ -chování u¾ známe. Z~toho mù¾eme odvodit, jak se chová celý blok: - -\figure{tabulka_skladani_bloku.eps}{Skládání chování blokù.}{1.3in} - -Pokud vy¹¹í blok pøenos pohlcuje, pak a»~se u¾~ni¾¹í blok chová jakkoli, slo¾ení -tìchto blokù musí v¾dy pohlcovat. V~prvním øádku tabulky jsou tudí¾ nuly. Analogicky, -pokud vy¹¹í blok generuje pøenos, tak~ten ni¾¹í na~tom nic nezmìní. V~druhém -øádku tabulky jsou tedy samé jednièky. Zajímavìj¹í pøípad nastává, pokud vy¹¹í blok -kopíruje -- tehdy zále¾í èistì na~chování ni¾¹ího bloku. - -V¹imnìme si, ¾e~skládání chování blokù je vlastnì úplnì obyèejné skládání -funkcí. Nyní bychom mohli prohlásit, ¾e~budeme poèítat nad~tøíprvkovou abecedou, -a~¾e~celou tabulku doká¾eme spoèítat jedním jediným hradlem. Pojïmì si v¹ak -rozmyslet, jak~bychom takovouto vìc popsali èistì binárnì. Jak tedy tyto tøi stavy -popisovat pouze nìkolika bity? - -Evidentnì nám k tomuto binárnímu zakódování tøí stavù budou staèit bity dva. -Oznaème si je jako $p$ a $q$. Tato dvojice mù¾e nabývat hned ètyø mo¾ných hodnot, -kterým pøiøadíme tøi mo¾ná chování bloku. Toto kódování mù¾eme zvolit zcela -libovolnì, ale pokud si ho zvolíme ¹ikovnì, u¹etøíme si dále práci pøi kompozici. -Zvolme si tedy kódování takto: - -\itemize\ibull -\:$(1,*) = <$, -\:$(0,0) = 0$, -\:$(0,1) = 1$ -\endlist - -Tomu, ¾e blok kopíruje, odpovídá dvojice $p = 1$; $q =$ \. V~ostatních -pøípadech bude~$p$ nulové a~$q$ nám bude øíkat, co je na~výstupu pøíslu¹ného bloku. -Jinými slovy $p = 0$ znamená, ¾e funkce je konstanta, pøièem¾ $q$ øíká jaká; naproti -tomu $p = 1$~znamená, ¾e funkce je identita, a»~u¾~je $q$ cokoli. - -Pojïme si nyní ukázat, jak bude celé skládání blokù vypadat. Rozmysleme si, -kdy je~$p$ celého dvojbloku jednièkové, tedy kdy celý dvojblok kopíruje. To nastane -tehdy, pokud kopírují obì jeho èásti, a tedy $p = p_a~\&~p_b$. Dále $q$ bude rovno jednièce, -pokud $q = (\neg{p_a}~\&~q_a) \lor (p_a~\&~q_b)$. - -Skládání chování blokù lze tedy popsat buï ternárnì -- tabulkou, ale lze to -i~binárnì vý¹e uvedeným pøedpisem. - -Nyní si tedy mù¾eme dopøedu vypoèítat chování bloku velikosti jedna, poté - z~nich skládáním blokù velikosti dva, dál velikosti ètyøi, osm, atd \dots - -\h{Paralelní sèítání} - -Paralelní algoritmus na~sèítání u¾~zkonstruujeme pomìrnì snadno. Bez újmy -na~obecnosti budeme pøedpokládat, ¾e~poèet bitù vstupních èísel je mocnina dvojky, -jinak si vstup doplníme nulami, co¾ výsedný èas bìhu algoritmu zhor¹í maximálnì -konstanta-krát. - -\algo -\:Spoèteme chování blokù velikosti~1. ($\O(1)$ hladin) -\:Postupnì poèítáme chování blokù\foot{myslíme \uv{pøirozenì zarovnané} bloky, tedy takové, jejich¾ poloha je násobkem velikosti} velikosti 2, 4, 8, ..., $2^k$. - ($\O(\log n)$ hladin, na~nich¾ se skládají bloky) -\:$c_0 \leftarrow 0$ (pøenos do nejni¾¹ího øádu je v¾dy 0) -\:Urèíme $c_n$ podle $c_0$ a chování (jediného) bloku velikosti~$n$. -\:Postupnì dopoèítáme pøenosy na~hranicích dìlitelných $2^k$ \uv{zahu¹»ováním}: - jakmile víme $c_{2^k}$, mù¾eme dopoèítat $c_{2^k+2^{k-1}}$ podle - chování bloku $\left< 2^k, 2^k+2^{k-1}\right>$. ($\O(\log n)$ hladin, - na~nich¾ se dosazuje) -\:Seèteme: $\forall i: z_i = x_i \oplus y_i \oplus c_i$. -\endalgo - -\figure{1_9_deleni_bloku.eps}{Výpoèet pøenosu.}{2.5in} - -Algoritmus pracuje v~èase $\O(\log n)$. Hradel je pou¾ito lineárnì: na~jednotlivých -hladinách kroku~2 poèet hradel exponenciálnì klesá od~$n$ k~1, na~hladinách kroku~5 -exponenciálnì stoupá od~1 k~$n$, tak¾e dohromady se seète na~$\Theta(n)$. - -\h{Tøídìní} - -Nyní se podíváme na~druhý problém, a~to na~problém tøídìní. Ji¾ známe pomìrnì efektivní sekvenèní algoritmy, které dovedou tøídit v~èase $\O(n\log n)$. Byli bychom jistì rádi, kdybychom to zvládli je¹tì rychleji. Pojïme se podívat, zda by nám v tom nepomohlo problém paralelizovat. - -Budeme pøi tom pracovat ve~výpoèetním modelu, kterému se øíká komparátorová sí». Ta je postavená z~hradel, kterým se øíká komparátory. - -\s{Definice:} {\I Komparátorová sí»} je kombinaèní obvod, jeho¾ hradla jsou -komparátory. - -\figure{sortnet.0}{Komparátor}{0.7in} - -Komparátor umí porovnat dvì hodnoty a~rozhodnout, která z~nich je vìt¹í -a~která men¹í. Nevrací v¹ak booleovský výsledek jako bì¾né hradlo, ale má dva -výstupy, pøièem¾ na~jednom vrátí men¹í ze~vstupních hodnot a~na~druhém vìt¹í. - -Výstupy komparátorù se nevìtví. Nemù¾eme tedy jeden výstup \uv{rozdvojit} a~pøipojit ho na~dva vstupy. (Vìtvení by dokonce ani nemìlo smysl, proto¾e zatímco rozdvojit bychom mohli, slouèit u¾~ne. Pokud tedy chceme, aby sí» mìla $n$~vstupù i~$n$~výstupù, rozdvojení stejnì nesmíme provést, i kdybychom jej mìli povolené.) - -\s{Pøíklad:} {\sl Bubble sort} - -Obrázek Bubble 1 ilustruje pou¾ití komparátorù pro tøídìní Bubble sortem. -©ipky pøedstavují jednotlivé komparátory. Výpoèet v¹ak je¹tì mù¾eme vylep¹it. - -\twofigures{sortnet.1}{Bubble 1}{143pt}{sortnet.2}{Bubble 2}{143pt} - -Sna¾íme se výpoèet co nejvíce paralelizovat (viz obrázek Bubble 2). -Jak je vidìt, komparátory na sebe nemusejí èekat. Tím mù¾eme výpoèet urychlit a místo èasu $\Theta{(n^2)}$ docílit èasové slo¾itosti $\Theta{(n)}$. V obou pøípadech je zachován kvadratický prostor. - -Nyní si uká¾eme je¹tì rychlej¹í tøídící algoritmus. Pùjdeme na nìj v¹ak trochu \uv{od lesa}. Nejdøíve vymyslíme sí», která bude umìt tøídit jenom nìco -- toti¾ bitonické posloupnosti. Bez újmy na obecnosti budeme pøedpokládat, ¾e ka¾dé dva prvky na vstupu jsou navzájem rùzné. - -\medskip -\s{Definice:} Øekneme, ¾e posloupnost $x_0,\dots,x_{n-1} $ je {\I èistì bitonická} právì tehdy, kdy¾ -pro nìjaké $x_k, k\in\{0, \dots, n-1\} $ platí, ¾e~v¹echny prvky pøed ním (vèetnì jeho samotného) -tvoøí rostoucí poslopnost, kde¾to prvky stojící za~ním tvoøí poslopnost klesající. -Formálnì zapsáno musí platit, ¾e: -$$x_0\leq x_1\leq \dots \leq x_k \geq x_{k+1}\geq\dots \geq x_{n-1}.$$ - -\s{Definice:} Posloupnost $x_0 \dots x_{n-1}$ je {\I bitonická}, právì kdy¾ $\exists~j\in \{0,\dots ,n-1\}$, pro -které je rotace pùvodní posloupnosti o $j$ prvkù, tedy posloupnost -$$x_j,x_{(j+1) \bmod n},\dots, x_{(j+n-1) \bmod n},$$ èistì bitonická. - -\s{Definice:} {\I Separátor $S_n$} je sí», ve které jsou v¾dy~$i$-tý a~$(i+{n/2})$-tý prvek vstupu -(pro $i=0,\dots, {n/2}-1$) propojeny komparátorem. Minimum se pak stane~$i$-tým, -maximum $(i+{n/2})$-tým prvkem výstupu. -\figure{sortnet.3}{$(y_i, y_{i+{n/2}}) = \(x_i, x_{i+{n/2}})$} {300pt} - -\s{Lemma:} Pokud separátor dostane na~vstupu bitonickou posloupnost, pak jeho výstup $y_0, \dots, y_{n-1}$ -splòuje: - -(i) $y_0,\dots, y_{n/2 -1}$ a~$y_{n/2},\dots, y_{n-1}$ jsou -bitonické posloupnosti, - -(ii) Pro v¹echna $i,j< {n/2}$ platí $y_i < y_{j + {n/2}}$. - -Separátor nám tedy jednu bitonickou posloupnost na~vstupu rozdìlí na~dvì -bitonické posloupnosti, pøièem¾ navíc ka¾dý prvek první posloupnosti ($y_0,\dots, y_{n/2 -1}$) -je men¹í nebo roven prvkùm druhé posloupnosti ($y_{n/2}, \dots, y_{n-1}$). - -\>Ne¾ pøistoupíme k dùkazu lemmatu, uka¾me si, k èemu se nám bude hodit. - - -\s{Definice:} {\I Bitonická tøídièka $B_n$} je obvod sestavený ze separátorù, který dostane-li na vstupu bitonickou posloupnost délky $n$ (BÚNO konstruujeme tøídièku pro $n=2^k$), vydá setøídìnou zadanou posloupnost délky $n$. - -Tøídièka dostane na~vstupu bitonickou posloupnost. Separátor~$S_n$ ji pak dle lemmatu rozdìlí na~dvì bitonické posloupnosti, kdy je ka¾dý prvek z~první posloupnosti men¹í ne¾ libovolný prvek z~druhé. Tyto poloviny pak dal¹í separátory rozdìlí na~ètvrtiny, ..., a¾~na~konci zbudou pouze jednoduché posloupnosti délky jedna (zjevnì setøídìné), které mezi sebou mají po¾adovanou nerovnost -- tedy ka¾dá posloupnost (nebo spí¹e prvek) nalevo je $\leq$ ne¾ prvek napravo od~nìj. - -\centerline{\epsfbox{sortnet.5}} - -Jak je vidìt, bitonická tøídièka nám libovolnou bitonickou posloupnost délky~$n$ -setøídí na~$\Theta(\log n)$ hladin. - -Nyní se dá odvodit, ¾e pokud umíme tøídit bitonické posloupnosti, umíme setøídit v¹echno. -Vzpomeòme si na~tøídìní sléváním -- Merge sort. To funguje tak, ¾e zaène s~jednoprvkovými posloupnostmi, které jsou evidentnì setøídìné, a~poté v¾dy dvì setøídìné posloupnosti slévá do~jedné. Kdybychom nyní umìli paralelnì slévat, mohli bychom vytvoøit i~paralelní Merge sort. Jinými slovy, potøebujeme dvì rostoucí posloupnosti nìjak efektivnì slít do~jedné setøídìné. Uvìdomme si, ¾e to zvládneme jednodu¹e -- staèí druhou z~posloupností obrátit a~\uv{pøilepit} za~první, èím¾ vznikne bitonická posloupnost, kterou poté mù¾eme setøídit na¹í bitonickou tøídièkou. - -\s{Pøíklad:} {\sl Merge sort} - -Bitonická tøídièka se tedy dá pou¾ít ke~slévání setøídìných posloupností. -Uka¾me si, jak s~její pomocí sestavíme souèástky {\I slévaèky} $M_n$: - -Setøídìné posloupnosti $x_0,\dots, x_{n-1}$ a~$y_0,\dots, y_{n-1}$ spojíme do jedné bitonické -posloupnosti $x_0,\dots, x_{n-1},y_{n-1},\dots, y_0$. Z~této posloupnosti vytvoøíme pomocí bitonické tøídièky -$B_{2n}$ setøídìnou posloupnost. Vytvoøíme tedy blok $M_{2n}$, který se ov¹em sestává de~facto pouze z~bloku $B_{2n}$, jeho¾ druhá polovina vstupù je zapojena v~obráceném poøadí. -\figure{sortnet.6}{Paralelní MergeSort.}{3in} - -Nyní se pokusme odhadnout èasovou slo¾itost. Ná¹ MergeSort bude mít øádovì hloubku blokù $\log n$. -V ka¾dém bloku $M_n$ je navíc ukryta bitonická tøídièka s takté¾ logaritmickou hloubkou. -Celková hloubka tedy bude $\log 2 + \log4 + \dots + \log 2^k + \dots + \log n$. -Po seètení nakonec dostáváme výslednou èasovou slo¾itost $\Theta (\log^2 n)$. - -Dodejme je¹tì, ¾e existuje i~tøídicí algoritmus, kterému staèí jen ${\O}(\log n)$ hladin. -Jeho multiplikativní konstanta je v¹ak pøíli¹ veliká, tak¾e je v~praxi nepou¾itelný. - -Vra»me se nyní k dùkazu lemmatu, který jsme na pøedminulé stránce vynechali. - -\h{Dùkaz Lemmatu:} -(i) Nejprve nahlédneme, ¾e lemma platí, je-li vstupem èistì bitonická -posloupnost. Dále BÚNO pøedpokládejme, ¾e vrchol posloupnosti je v~první polovinì (kdyby -byl vrchol za~polovinou, staèilo by zrcadlovì obrátit posloupnost i~komparátory a~øe¹ili -bychom stejný problém). Nyní si definujme $k := \min j: x_j > x_{j+n/2}$. (Pokud -by~takové~$k$ neexistovalo, znamenalo by~to, ¾e vstupní posloupnost je monotónní. -Separátor by tedy nic nedìlal a~pouze zkopíroval vstup na~výstup, co¾ jistì lemma splòuje.) Nyní si v¹imìme, -¾e jakmile jednou zaène platit, ¾e prvek na~levé stranì je men¹í ne¾ na~pravé, bude -nám tato relace platit a¾~do~konce. Oznaème vrchol vstupní posloupnosti jako~$x_m$. -Pak~$k$ bude jistì men¹í ne¾~$m$ a~$k+{n/2}$ bude vìt¹í ne¾~$m$. Mezi~$k$ a~$m$ je tedy -vstupní posloupnost neklesající, mezi $k+{n/2}$ a~$n-1$ nerostoucí. - -Do pozice~$k$ tedy separátor bude pouze kopírovat vstup na~výstup, od~pozice~$k$ -dál u¾~jen prohazuje. Pro ka¾dé~$i$, $(k\leq i\leq {n/2}-1)$ se prvky -$x_i$ a~$x_{i+{n/2}}$ prohodí. Úsek mezi~$k$ a~${n/2}-1$ tedy -nahradíme nerostoucí posloupností, první polovina výstupu tedy bude -(dokonce èistì) bitonická. Podobnì úsek $k+{n/2}$ a¾~$n-1$ nahradíme èistì -bitonickou posloupností. Obì poloviny tedy budou bitonické. - -Dostaneme-li na vstupu obecnou bitonickou posloupnost, pøedstavíme si, -¾e je to èistì bitonická posloupnost zrotovaná o~$r$ prvkù (BÚNO -doprava). Zjistíme, ¾e v~komparátorech se porovnávají tyté¾ prvky, -jako kdyby zrotovaná nebyla. Výstup se od výstupu èistì bitonické -posloupnosti zrotovaného o~$r$ bude li¹it prohozením úsekù $x_0$ a¾ -$x_{r-1}$ a~$x_{n/2}$ a¾ $x_{{n/2}+r-1}$. Obì výstupní -posloupnosti tedy budou zrotované o~$r$ prvkù, ale na jejich -bitoniènosti se nic nezmìní. - -(ii) Z dùkazu (i) pro èistì bitonickou posloupnost víme, ¾e $y_0\dots y_{n/2-1}$ je èistì bitonická a bude rovna $x_0\dots x_{k-1},x_{k+n/2}\dots x_{n-1}$ pro vhodné $k$ a navíc bude mít maximum v $x_{k-1}$ nebo $x_k+{n/2}$. Mezi tìmito body ov¹em ve vstupní posloupnosti urèitì nele¾el ¾ádný $x_i$ men¹í ne¾ $x_k-1$ nebo $x_k+{n/2}$ (jak je vidìt z obrázku) a posloupnost $x_k \dots x_{k-1+{n/2}}$ je rovna $y_{n/2}\dots y_{n-1}$. Pro obecné bitonické posloupnosti uká¾eme stejnì jako v (i). -\qed - -\medskip - -\centerline{\epsfbox{sortnet.7}} - - -\h{Paralelní násobení} - -Podobnì jako u~sèítání si vzpomeneme na~¹kolní algoritmus -- tentokráte v¹ak pro násobení. -To fungovalo tak, ¾e~jsme si jedno ze~dvou binárních èísel na~vstupu (øíkejme mu tøeba $x$) $n$-krát posouvali. Tam kde pak byly v~èísle~$y$ jednièky, pøíslu¹né kopie $x$ jsme seèetli. Jinými slovy tedy násobení umíme pøevést na~nìjaké posuny (ty lze realizovat pouze \uv{pøedrátováním} -- nic nás nestojí), násobení jedním bitem (co¾ je -{\sc and} ) a~nakonec potøebujeme výsledných~$n$ èísel seèíst. -\figure{skolni_scitani.eps}{©kolní sèítání.}{2in} - -Jak nyní seèíst $n$ $n$-bitových èísel..? Nabízí se vyu¾ít osvìdèený \uv{stromeèek} -- sèítat dvojice èísel, výsledky pak opìt po~dvojicích seèíst, a¾ na~konci vyjde jediný výsledek. -\figure{stromecek.eps}{Stromeèek}{1.4in} - -Toto øe¹ení by v¹ak vedlo na~èasovou slo¾itost $\Theta (\log^2 n)$, nebo» sèítat umíme v~èase $\Theta (\log n)$. To je sice dle na¹ich mìøítek -docela efektivní, ale pøekvapivì to jde je¹tì lépe -- toti¾ na~$\Theta (\log n)$ hladin. Této -slo¾itosti dosáhneme malým trikem. - -Vymyslíme obvod konstantní hloubky, který na~vstupu dostane tøi èísla. Odpoví pak dvìma èísly -takovými, ¾e budou mít stejný souèet jako pùvodní tøi èísla. Jinými slovy pomocí -tohoto obvodu budeme umìt seètení tøí èísel pøevést na seètení dvou èísel. - -\figure{obvod.eps}{$x+y+z = p+q$}{0.3in} - -V¹imnìme si, ¾e~kdy¾ sèítáme tøi bity, mù¾e být pøenos do~vy¹¹ího øádu nula èi~jednièka. Vezmeme si tedy bity $x_i$, $y_i$, $z_i$ a~ty seèteme. To nám dá dvoubitový výsledek, pøièem¾ ni¾¹í bit z~tohoto výsledku po¹leme do~èísla~$p$, vy¹¹í do~èísla~$q$. - -\figure{obvod_real.eps}{Redukování sèítání}{1.2in} - -Toto zredukování sèítání nám nyní umo¾ní opìt stavit strom, by» o malièko slo¾itìj¹í. - -\figure{sl_stromecek.eps}{Slo¾itìj¹í stromeèek}{0.9in} - -Pokud jsme mìli na~zaèátku $n$ èísel, po~první redukci nám jich zbývá jen $2/3 \cdot n$ a~obecnì v~$k$-té hladiné $(2/3)^k \cdot n$. Znamená to, ¾e èísel nám ubývá exponenciálnì, tak¾e poèet hladin bude logaritmický. Redukující obvod je pøi tom jen konstantnì hluboký, tak¾e celé redukování zvládneme v~èase $\Theta (\log n)$. Na~konci Slo¾itìj¹ího stromeèku pak máme umístìnou jednu obyèejnou sèítaèku, která zbývající dvì èísla seète v~logaritmickém èase. - -Seètení v¹ech $n$ èísel tedy zabere $\Theta (\log n)$ hladin. - -Kdy¾ se nyní vrátíme k~násobení, zbývá nám vyøe¹it posouvání a~{\sc and}ování. Uvìdomme si, ¾e to je plnì paralelení a~zvládneme ho za~konstantnì mnoho hladin. 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-z16=(0v,6v); -z17=(0v,7v); -z18=(0v,8v); -z19=(0v,9v); -z110=(0v,10v); - -z21=(2v,1v); -z22=(2v,2v); -z23=(2v,3v); -z24=(2v,4v); -z25=(2v,5v); -z26=(2v,6v); -z27=(2v,7v); -z28=(2v,8v); -z29=(2v,9v); -z210=(2v,10v); - -z31=(4v,1v); -z32=(4v,2v); -z33=(4v,3v); -z34=(4v,4v); -z35=(4v,5v); -z36=(4v,6v); -z37=(4v,7v); -z38=(4v,8v); -z39=(4v,9v); -z310=(4v,10v); - -z41=(6v,1v); -z42=(6v,2v); -z43=(6v,3v); -z44=(6v,4v); -z45=(6v,5v); -z46=(6v,6v); -z47=(6v,7v); -z48=(6v,8v); -z49=(6v,9v); -z410=(6v,10v); - -z51=(8v,1v); -z52=(8v,2v); -z53=(8v,3v); -z54=(8v,4v); -z55=(8v,5v); -z56=(8v,6v); -z57=(8v,7v); -z58=(8v,8v); -z59=(8v,9v); -z510=(8v,10v); - -z111=(0v,11v); -z211=(2v,11v); -z311=(4v,11v); -z411=(6v,11v); -z511=(8v,11v); - -z1015=(-1v,1.5v); -z1615=(9v,1.5v); -z1035=(-1v,3.5v); -z1635=(9v,3.5v); -z1065=(-1v,6.5v); -z1665=(9v,6.5v); - -pickup pencircle scaled 0.4pt; -draw(z10--z111); -draw(z20--z211); -draw(z30--z311); -draw(z40--z411); -draw(z50--z511); -drawarrow(z11--z21); -drawarrow(z13--z23); -drawarrow(z16--z26); -drawarrow(z110--z210); -drawarrow(z22--z32); -drawarrow(z25--z35); -drawarrow(z29--z39); -drawarrow(z34--z44); -drawarrow(z38--z48); -drawarrow(z47--z57); - -pickup pencircle scaled 0.7pt; -draw(z1015--z1615) dashed withdots scaled 0.7; -draw(z1035--z1635) dashed withdots scaled 0.7; -draw(z1065--z1665) dashed withdots scaled 0.7; - -label.top(btex x1 etex,z111); -label.top(btex x2 etex,z211); -label.top(btex x3 etex,z311); -label.top(btex x4 etex,z411); -label.top(btex x5 etex,z511); - -endfig; - -beginfig(2); -v:=u*5mm; -z13=(0v,3v); -z23=(2v,3v); -z33=(4v,3v); -z43=(6v,3v); -z53=(8v,3v); - -z14=(0v,4v); -z15=(0v,5v); -z16=(0v,6v); -z17=(0v,7v); -z18=(0v,8v); -z19=(0v,9v); -z110=(0v,10v); - -z24=(2v,4v); -z25=(2v,5v); -z26=(2v,6v); -z27=(2v,7v); -z28=(2v,8v); -z29=(2v,9v); -z210=(2v,10v); - -z34=(4v,4v); -z35=(4v,5v); -z36=(4v,6v); -z37=(4v,7v); -z38=(4v,8v); -z39=(4v,9v); -z310=(4v,10v); - -z44=(6v,4v); -z45=(6v,5v); -z46=(6v,6v); -z47=(6v,7v); -z48=(6v,8v); -z49=(6v,9v); -z410=(6v,10v); - -z54=(8v,4v); -z55=(8v,5v); -z56=(8v,6v); -z57=(8v,7v); -z58=(8v,8v); -z59=(8v,9v); -z510=(8v,10v); - -z111=(0v,11v); -z211=(2v,11v); -z311=(4v,11v); -z411=(6v,11v); -z511=(8v,11v); - -pickup pencircle scaled 0.4pt; -draw(z13--z111); -draw(z23--z211); -draw(z33--z311); -draw(z43--z411); -draw(z53--z511); -drawarrow(z14--z24); -drawarrow(z16--z26); -drawarrow(z18--z28); -drawarrow(z110--z210); -drawarrow(z25--z35); -drawarrow(z27--z37); -drawarrow(z29--z39); -drawarrow(z36--z46); -drawarrow(z38--z48); -drawarrow(z47--z57); - -label.top(btex x1 etex,z111); -label.top(btex x2 etex,z211); -label.top(btex x3 etex,z311); -label.top(btex x4 etex,z411); -label.top(btex x5 etex,z511); - -endfig; - - -beginfig(3); -v:=u*5mm; - -z10=(0v,0v); -z15=(0v,5v); -z16=(0v,6v); - -z20=(2v,0v); -z24=(2v,4v); -z26=(2v,6v); - -z30=(4v,0v); -z33=(4v,3v); -z36=(4v,6v); - -z40=(6v,0v); -z42=(6v,2v); -z46=(6v,6v); - -z50=(8v,0v); -z51=(8v,1v); -z56=(8v,6v); - -z60=(10v,0v); -z65=(10v,5v); -z66=(10v,6v); - -z70=(12v,0v); -z74=(12v,4v); -z76=(12v,6v); - -z80=(14v,0v); -z83=(14v,3v); -z86=(14v,6v); - -z90=(16v,0v); -z92=(16v,2v); -z96=(16v,6v); - -z100=(18v,0v); -z101=(18v,1v); -z106=(18v,6v); - -z110=(20v,0v); -z116=(20v,6v); - - -pickup pencircle scaled 0.4pt; -draw(z10--z16); -draw(z20--z26); -draw(z30--z36); -draw(z40--z46); -draw(z50--z56); -draw(z60--z66); -draw(z70--z76); -draw(z80--z86); -draw(z90--z96); -draw(z100--z106); -drawarrow(z15--z65); -drawarrow(z24--z74); -drawarrow(z33--z83); -drawarrow(z42--z92); -drawarrow(z51--z101); - -label.top(btex $x_0$ etex,z16); -label.top(btex $x_1$ etex,z26); -label.top(btex $x_2$ etex,z36); -label.top(btex \dots etex,z66); -label.top(btex $x_{n-2}$ etex,z96); -label.top(btex $x_{n-1}$ etex,z106); - -endfig; - - - -beginfig(4); -v:=u*5mm; - -z1075=(7.5v,0v); - -z10=(0v,1v); -z175=(7.5v,1v); -z115=(15v,1v); - -z20=(0v,2v); -z235=(3.5v,2v); -z211=(11.5v,2v); -z215=(15v,2v); - -z30=(0v,3v); -z335=(3.5v,3v); -z37=(7v,3v); -z38=(8v,3v); -z311=(11.5v,3v); -z315=(15v,3v); - -z40=(0v,4v); -z415=(1.5v,4v); -z455=(5.5v,4v); -z47=(7v,4v); -z48=(8v,4v); -z495=(9.5v,4v); -z413=(13.5v,4v); -z416=(15v,4v); - -z50=(0v,5v); -z515=(1.5v,5v); -z53=(3v,5v); -z54=(4v,5v); -z555=(5.5v,5v); -z57=(7v,5v); -z58=(8v,5v); -z595=(9.5v,5v); -z511=(11v,5v); -z512=(12v,5v); -z513=(13.5v,5v); -z516=(15v,5v); - -z60=(0v,6v); -z61=(1v,6v); -z62=(2v,6v); -z63=(3v,6v); -z64=(4v,6v); -z65=(5v,6v); -z66=(6v,6v); -z67=(7v,6v); -z68=(8v,6v); -z69=(9v,6v); -z610=(10v,6v); -z611=(11v,6v); -z612=(12v,6v); -z613=(13v,6v); -z614=(14v,6v); -z615=(15v,6v); - -z71=(1v,7v); -z72=(2v,7v); -z75=(5v,7v); -z76=(6v,7v); -z79=(9v,7v); -z710=(10v,7v); -z713=(13v,7v); -z714=(14v,7v); - - - - -pickup pencircle scaled 0.4pt; -draw(z10--z115--z215--z20--cycle); -draw(z30--z37--z47--z40--cycle); -draw(z38--z315--z416--z48--cycle); -draw(z50--z53--z63--z60--cycle); -draw(z54--z57--z67--z64--cycle); -draw(z58--z511--z611--z68--cycle); -draw(z512--z516--z615--z612--cycle); -drawarrow(z71--z61); -drawarrow(z72--z62); -drawarrow(z75--z65); -drawarrow(z76--z66); -drawarrow(z79--z69); -drawarrow(z710--z610); -drawarrow(z713--z613); -drawarrow(z714--z614); -drawarrow(z515--z415); -drawarrow(z555--z455); -drawarrow(z595--z495); -drawarrow(z513--z413); -drawarrow(z335--z235); -drawarrow(z311--z211); -drawarrow(z175--z1075); - -endfig; - - -beginfig(5); -v:=u*5mm; - -z075=(7.5v,8v); - -z10=(0v,7v); -z175=(7.5v,7v); -z115=(15v,7v); - -z20=(0v,6v); -z235=(3.5v,6v); -z211=(11.5v,6v); -z215=(15v,6v); - -z30=(0v,5v); -z335=(3.5v,5v); -z37=(7v,5v); -z38=(8v,5v); -z311=(11.5v,5v); -z315=(15v,5v); - -z40=(0v,4v); -z415=(1.5v,4v); -z455=(5.5v,4v); -z47=(7v,4v); -z48=(8v,4v); -z495=(9.5v,4v); -z413=(13.5v,4v); -z416=(15v,4v); - -z50=(0v,3v); -z515=(1.5v,3v); -z53=(3v,3v); -z54=(4v,3v); -z555=(5.5v,3v); -z57=(7v,3v); -z58=(8v,3v); -z595=(9.5v,3v); -z511=(11v,3v); -z512=(12v,3v); -z513=(13.5v,3v); -z516=(15v,3v); - -z60=(0v,2v); -z61=(1v,2v); -z62=(2v,2v); -z63=(3v,2v); -z64=(4v,2v); -z65=(5v,2v); -z66=(6v,2v); -z67=(7v,2v); -z68=(8v,2v); -z69=(9v,2v); -z610=(10v,2v); -z611=(11v,2v); -z612=(12v,2v); -z613=(13v,2v); -z614=(14v,2v); -z615=(15v,2v); - -% ve skutecnosti dle znaceni -% by melo byt z6* ale uz -% obsazeno -z815=(1.5v,2v); -z855=(5.5v,2v); -z895=(9.5v,2v); -z813=(13.5v,2v); - -z715=(1.5v,1v); -z755=(5.5v,1v); -z795=(9.5v,1v); -z713=(13.5v,1v); - -z9=(4v,0v); - -pickup pencircle scaled 0.4pt; -draw(z10--z115--z215--z20--cycle); -draw(z30--z37--z47--z40--cycle); -draw(z38--z315--z416--z48--cycle); -draw(z50--z53--z63--z60--cycle); -draw(z54--z57--z67--z64--cycle); -draw(z58--z511--z611--z68--cycle); -draw(z512--z516--z615--z612--cycle); -drawarrow(z075--z175); -drawarrow(z415--z515); -drawarrow(z455--z555); -drawarrow(z495--z595); -drawarrow(z413--z513); -drawarrow(z235--z335); -drawarrow(z211--z311); -drawarrow(z815--z715); -drawarrow(z855--z755); -drawarrow(z895--z795); -drawarrow(z813--z713); - -label.llft(btex $n$ etex,z075); -label.bot(btex $S_n$ etex,z175); -label.bot(btex $S_{n\over 2}$ etex,z335); -label.bot(btex $S_{n\over 2}$ etex,z311); -label.bot(btex $S_{n\over 4}$ etex,z515); -label.bot(btex $S_{n\over 4}$ etex,z555); -label.bot(btex $S_{n\over 4}$ etex,z595); -label.bot(btex $S_{n\over 4}$ etex,z513); -label.rt(btex Bitonick\'a t\v r\'\i di\v cka $B_{n}$ etex,z9); - -endfig; - - - -beginfig(6); -v:=u*5mm; - -z1075=(7.5v,0v); - -z10=(0v,1v); -z175=(7.5v,1v); -z115=(15v,1v); - -z20=(0v,2v); -z235=(3.5v,2v); -z211=(11.5v,2v); -z215=(15v,2v); - -z30=(0v,3v); -z335=(3.5v,3v); -z37=(7v,3v); -z38=(8v,3v); -z311=(11.5v,3v); -z315=(15v,3v); - -z40=(0v,4v); -z415=(1.5v,4v); -z455=(5.5v,4v); -z47=(7v,4v); -z48=(8v,4v); -z495=(9.5v,4v); -z413=(13.5v,4v); -z416=(15v,4v); - -z50=(0v,5v); -z515=(1.5v,5v); -z53=(3v,5v); -z54=(4v,5v); -z555=(5.5v,5v); -z57=(7v,5v); -z58=(8v,5v); -z595=(9.5v,5v); -z511=(11v,5v); -z512=(12v,5v); -z513=(13.5v,5v); -z516=(15v,5v); - -z60=(0v,6v); -z61=(1v,6v); -z62=(2v,6v); -z63=(3v,6v); -z64=(4v,6v); -z65=(5v,6v); -z66=(6v,6v); -z67=(7v,6v); -z68=(8v,6v); -z69=(9v,6v); -z610=(10v,6v); -z611=(11v,6v); -z612=(12v,6v); -z613=(13v,6v); -z614=(14v,6v); -z615=(15v,6v); - -z71=(1v,7v); -z72=(2v,7v); -z75=(5v,7v); -z76=(6v,7v); -z79=(9v,7v); -z710=(10v,7v); -z713=(13v,7v); -z714=(14v,7v); - - - - -pickup pencircle scaled 0.4pt; -draw(z10--z115--z215--z20--cycle); -draw(z30--z37--z47--z40--cycle); -draw(z38--z315--z416--z48--cycle); -draw(z50--z53--z63--z60--cycle); -draw(z54--z57--z67--z64--cycle); -draw(z58--z511--z611--z68--cycle); -draw(z512--z516--z615--z612--cycle); -drawarrow(z71--z61); -drawarrow(z72--z62); -drawarrow(z75--z65); -drawarrow(z76--z66); -drawarrow(z79--z69); -drawarrow(z710--z610); -drawarrow(z713--z613); -drawarrow(z714--z614); -drawarrow(z515--z415); -drawarrow(z555--z455); -drawarrow(z595--z495); -drawarrow(z513--z413); -drawarrow(z335--z235); -drawarrow(z311--z211); -drawarrow(z175--z1075); - -label.top(btex $M_8$ etex,z175); -label.top(btex $M_4$ etex,z335); -label.top(btex $M_4$ etex,z311); -label.top(btex $M_2$ etex,z515); -label.top(btex $M_2$ etex,z555); -label.top(btex $M_2$ etex,z595); -label.top(btex $M_2$ etex,z513); - -endfig; - - -beginfig(7); -v:=u*7mm; - -z12=(1v,2v); -z13=(1v,3v); -z16=(1v,6v); -z356=(3.5v,6v); -z40=(5v,1v); -z42=(5v,2v); -z47=(5v,6.5v); -z72=(9v,2v); -z74=(9v,4v); -z76=(9v,6v); - -z100=(0.5v,0.5v); -z101=(1.5v,0.5v); - -z0=whatever[z13,z356]; -z1=whatever[z356,z74]; -z1=z0+4v*right; -z2=whatever[z12,z72]; -z2=z0+whatever*down; -z3=whatever[z12,z72]; -z3=z1+whatever*down; -z4=z356+4v*right; -z5=whatever[z72,z76]; -z5=whatever[z4,z1+4v*right]; -z6=whatever[z40,z47]; -z6=z74+4v*left; - - -pickup pencircle scaled 0.4pt; -draw(z16--z12--z72--z76); -draw(z13--z356--z74); -draw(z40--z47) dashed evenly; -draw(z1--z4) dashed withdots scaled 0.7; -draw(z4--z5) dashed withdots scaled 0.7; -draw(z0--z6) dashed withdots scaled 0.7; -draw(z0--z2) dashed evenly; -draw(z1--z3) dashed evenly; - -draw(z100--z101) dashed withdots scaled 0.7; - -pickup pencircle scaled 3pt; -drawdot(z0); -drawdot(z1); - -label.bot(btex \strut 0 etex,z12); -label.bot(btex $k$ etex,z2); -label.llft(btex \strut ${n\over 2} - 1$ etex,z42); -label.bot(btex \strut $k+{n\over 2}$ etex,z3); -label.bot(btex \strut $n-1$ etex,z72); -label.rt(btex posloupnost prohozen\'a separ\'atorem etex,z101); - -endfig; - - - - - - - -end; diff --git a/5-addsort/sortnet.mpx b/5-addsort/sortnet.mpx deleted file mode 100644 index 9814d6d..0000000 --- a/5-addsort/sortnet.mpx +++ /dev/null @@ -1,548 +0,0 @@ -% Written by DVItoMP, Version 0.64/color (Web2C 7.5.4) -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("a",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,-3.4869)--(4.9813,-3.4869)-- - (4.9813,8.4682)--(0,8.4682)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("b",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,-3.4869)--(5.5348,-3.4869)-- - (5.5348,8.4682)--(0,8.4682)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("min",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(16.6045,0.0000)-- - (16.6045,6.6536)--(0,6.6536)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("max",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(18.5416,0.0000)-- - (18.5416,4.2895)--(0,4.2895)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x1",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x2",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x3",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x4",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x5",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x1",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x2",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x3",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x4",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("x5",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(10.2394,0.0000)-- - (10.2394,6.4204)--(0,6.4204)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("x",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("0",_n2,1.00000,5.6939,-1.4944,); -setbounds _p to (0,-1.4944)--(10.1633,-1.4944)-- - (10.1633,4.2895)--(0,4.2895)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("x",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("1",_n2,1.00000,5.6939,-1.4944,); -setbounds _p to (0,-1.4944)--(10.1633,-1.4944)-- - (10.1633,4.2895)--(0,4.2895)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("x",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("2",_n2,1.00000,5.6939,-1.4944,); -setbounds _p to (0,-1.4944)--(10.1633,-1.4944)-- - (10.1633,4.2895)--(0,4.2895)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s(":",_n1,1.00000,0.0000,0.0000,); -_s(":",_n1,1.00000,4.4278,0.0000,); -_s(":",_n1,1.00000,8.8556,0.0000,); -setbounds _p to (0,0.0000)--(13.2834,0.0000)-- - (13.2834,1.0516)--(0,1.0516)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("x",_n1,1.00000,0.0000,0.0000,); -_n3="cmmi7"; -_s("n",_n3,1.00000,5.6939,-1.4944,); -_n4="cmsy7"; -_s(char0,_n4,1.00000,10.6188,-1.4944,); -_n2="cmr7"; -_s("2",_n2,1.00000,16.8454,-1.4944,); -setbounds _p to (0,-2.3246)--(21.3148,-2.3246)-- - (21.3148,4.2895)--(0,4.2895)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("x",_n1,1.00000,0.0000,0.0000,); -_n3="cmmi7"; -_s("n",_n3,1.00000,5.6939,-1.4944,); -_n4="cmsy7"; -_s(char0,_n4,1.00000,10.6188,-1.4944,); -_n2="cmr7"; -_s("1",_n2,1.00000,16.8454,-1.4944,); -setbounds _p to (0,-2.3246)--(21.3148,-2.3246)-- - (21.3148,4.2895)--(0,4.2895)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("n",_n1,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(5.9799,0.0000)-- - (5.9799,4.2895)--(0,4.2895)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("S",_n1,1.00000,0.0000,0.0000,); -_n3="cmmi7"; -_s("n",_n3,1.00000,6.1090,-1.4944,); -setbounds _p to (0,-1.4944)--(11.5320,-1.4944)-- - (11.5320,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("S",_n1,1.00000,0.0000,0.0000,); -_n5="cmmi5"; -_s("n",_n5,1.00000,7.3045,1.1829,); -interim linecap:=0; -vardef _r(expr _a,_w)(text _t) = - addto _p doublepath _a withpen pencircle scaled _w _t enddef; -_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); -_n6="cmr5"; -_s("2",_n6,1.00000,7.8033,-3.8949,); -setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- - (13.3857,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("S",_n1,1.00000,0.0000,0.0000,); -_n5="cmmi5"; -_s("n",_n5,1.00000,7.3045,1.1829,); -interim linecap:=0; -vardef _r(expr _a,_w)(text _t) = - addto _p doublepath _a withpen pencircle scaled _w _t enddef; -_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); -_n6="cmr5"; -_s("2",_n6,1.00000,7.8033,-3.8949,); -setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- - (13.3857,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("S",_n1,1.00000,0.0000,0.0000,); -_n5="cmmi5"; -_s("n",_n5,1.00000,7.3045,1.1829,); -interim linecap:=0; -vardef _r(expr _a,_w)(text _t) = - addto _p doublepath _a withpen pencircle scaled _w _t enddef; -_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); -_n6="cmr5"; -_s("4",_n6,1.00000,7.8033,-3.8949,); -setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- - (13.3857,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("S",_n1,1.00000,0.0000,0.0000,); -_n5="cmmi5"; -_s("n",_n5,1.00000,7.3045,1.1829,); -interim linecap:=0; -vardef _r(expr _a,_w)(text _t) = - addto _p doublepath _a withpen pencircle scaled _w _t enddef; -_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); -_n6="cmr5"; -_s("4",_n6,1.00000,7.8033,-3.8949,); -setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- - (13.3857,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("S",_n1,1.00000,0.0000,0.0000,); -_n5="cmmi5"; -_s("n",_n5,1.00000,7.3045,1.1829,); -interim linecap:=0; -vardef _r(expr _a,_w)(text _t) = - addto _p doublepath _a withpen pencircle scaled _w _t enddef; -_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); -_n6="cmr5"; -_s("4",_n6,1.00000,7.8033,-3.8949,); -setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- - (13.3857,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("S",_n1,1.00000,0.0000,0.0000,); -_n5="cmmi5"; -_s("n",_n5,1.00000,7.3045,1.1829,); -interim linecap:=0; -vardef _r(expr _a,_w)(text _t) = - addto _p doublepath _a withpen pencircle scaled _w _t enddef; -_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); -_n6="cmr5"; -_s("4",_n6,1.00000,7.8033,-3.8949,); -setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- - (13.3857,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("Bitonic",_n0,1.00000,0.0000,0.0000,); -_s("k"&char19,_n0,1.00000,31.1333,0.0000,); -_s("a",_n0,1.00000,36.3914,0.0000,); -_s("t",_n0,1.00000,44.6936,0.0000,); -_s(char20,_n0,1.00000,48.0283,0.0000,); -_s("r",_n0,1.00000,48.5680,0.0000,); -_s(char19,_n0,1.00000,51.3631,0.0000,); -_s(char16&"di",_n0,1.00000,52.4700,0.0000,); -_s(char20,_n0,1.00000,63.2629,0.0000,); -_s("ck",_n0,1.00000,63.5397,0.0000,); -_s("a",_n0,1.00000,72.6721,0.0000,); -_n1="cmmi10"; -_s("B",_n1,1.00000,80.9743,0.0000,); -_n3="cmmi7"; -_s("n",_n3,1.00000,88.5311,-1.4944,); -setbounds _p to (0,-1.4944)--(93.9541,-1.4944)-- - (93.9541,6.9185)--(0,6.9185)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("M",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("8",_n2,1.00000,9.6652,-1.4944,); -setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- - (14.1345,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("M",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("4",_n2,1.00000,9.6652,-1.4944,); -setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- - (14.1345,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("M",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("4",_n2,1.00000,9.6652,-1.4944,); -setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- - (14.1345,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("M",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("2",_n2,1.00000,9.6652,-1.4944,); -setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- - (14.1345,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("M",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("2",_n2,1.00000,9.6652,-1.4944,); -setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- - (14.1345,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("M",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("2",_n2,1.00000,9.6652,-1.4944,); -setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- - (14.1345,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("M",_n1,1.00000,0.0000,0.0000,); -_n2="cmr7"; -_s("2",_n2,1.00000,9.6652,-1.4944,); -setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- - (14.1345,6.8078)--(0,6.8078)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n0="cmr10"; -_s("0",_n0,1.00000,0.0000,0.0000,); -setbounds _p to (0,-3.4869)--(4.9813,-3.4869)-- - (4.9813,8.4682)--(0,8.4682)--cycle; -_p endgroup -mpxbreak -begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; -string _n[]; -vardef _s(expr _t,_f,_m,_x,_y)(text _c)= - addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; -_n1="cmmi10"; -_s("k",_n1,1.00000,0.0000,0.0000,); -setbounds _p to (0,0.0000)--(5.5002,0.0000)-- - (5.5002,6.9185)--(0,6.9185)--cycle; 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-Nyní se budeme vìnovat následujícímu problému: v~textu délky $S$ (senì) budeme chtít najít v¹echny výskyty hledaného slova délky $J$ (jehly). Nejprve se podívejme na~jeden primitivní algoritmus, který nefunguje. Je ale zajímavé rozmyslet si, proè. - -\h{Hloupý algoritmus} -Zaèneme prvním písmenkem hledaného slova a~budeme postupnì procházet text, a¾ najdeme první výskyt poèáteèního písmenka. Poté budeme testovat, zda souhlasí i~písmenka dal¹í. Pokud nastane neshoda, v~hledaném slovì se vrátíme na~zaèátek a~v~textu pokraèujeme znakem, ve~kterém neshoda nastala. Podívejme se na~pøíklad. - -\s{Pøíklad:} Budeme hledat slovo |jehla| v~textu |jevkupcejejehla|. Vezmeme si tedy první písmenko |j| v~hledaném slovì a~zjistíme, ¾e v~textu se nachází hned na~zaèátku. Vezmeme tedy dal¹í písmenko |e|, které se vyskytuje jako druhé i~v~textu. Pøi tøetím písmenku ale narazíme na~neshodu. V~tuto chvíli tedy zresetujeme a~opìt hledáme výskyt písmenka |j|, tentokrát v¹ak a¾ od~tøetího písmene v~textu. Takto postupujeme postupnì dál, a¾ narazíme na~dal¹í |je|, které ov¹em není následováno písmenem~|h|, tudí¾ opìt zresetujeme a~nakonec najdeme shodu s~celým hledaným øetìzcem. V~tomto pøípadì tedy algoritmus na¹el hledané slovo. - -Tento algoritmus v¹ak zjevnì mù¾e hanebnì selhat. Mù¾e se stát, ¾e zaèneme porovnávat, a¾ v~jednu chvíli narazíme na~neshodu. Celý tento kus tedy pøeskoèíme. Pøi tom se ale v~tomto kusu textu mohl vyskytovat nìjaký pøekrývající se výskyt hledané \uv{jehly}. Hledejme napøíklad øetìzec |kokos| v~textu |clanekokokosu|. Algoritmus tedy zaène porovnávat. Ve~chvíli kdy najde prefix |koko| a~na~vstupu dostane~|k|, dochází k~neshodì. Proto algoritmus zresetuje a~pokraèuje v~hledání od~tohoto znaku. Najde sice je¹tì výskyt |ko|, ov¹em s~dal¹ím písmenkem |s| ji¾ dochází k~neshodì a~algotimus sel¾e. Nesprávnì se toti¾ \uv{upnul} na~první nalezené |koko| a~s~dal¹ím~|k| pak \uv{zahodil} i~správný zaèátek. - -Máme tedy algoritmus, který i~kdy¾ je ¹patnì, tak funguje urèitì kdykoli se první písmenko hledaného slova v~tomto slovì u¾ nikde jinde nevyskytuje -- co¾ |jehla| splòovala, ale |kokos| u¾ ne. - -{\I Hloupý algoritmus} se na~ka¾dé písmenko textu podívá jednou, tudí¾ èasová slo¾itost bude lineární s~délkou textu ve~kterém hledáme -- tedy $\O(S)$. - -\h{Pomalý algoritmus} -Zkusíme algoritmus vylep¹it tak, aby fungoval správnì: pokud nastane nìjaká neshoda, vrátíme se zpátky tìsnì za~zaèátek toho, kdy se nám to zaèalo shodovat. To je ov¹em vlastnì skoro toté¾, jako brát postupnì v¹echny mo¾né zaèátky v~\uv{senì} a~pro ka¾dý z~nìj ovìøit, jestli se tam \uv{jehla} nachází èi nikoliv. - -Tento algoritmus evidentnì funguje. Bì¾í v¹ak v~èase: $S$ mo¾ných zaèátkù, krát èas potøebný na~jedno porovnání (zda se na~dané pozici nenachází \uv{jehla}), co¾ nám mù¾e trvat a¾ $J$. Proto je èasová slo¾itost $\O(SJ)$. V praxi bude algoritmus èasto rychlej¹í, proto¾e typicky velmi brzo zjistíme, ¾e se øetìzce neshodují, ale je mo¾né vymyslet vstup, kde bude potøeba porovnání opravdu tolik. - -Nyní se pokusme najít takový algoritmus, který by byl tak rychlý, jako {\I Hloupý algoritmus}, ale chytrý, jako ten {\I Pomalý}. - -\h{Chytrý algoritmus} -Ne¾ vlastní algoritmus vybudujeme, zkusíme se cestou nauèit pøemý¹let o~øetìzcích obèas trochu pøekrouceným zpùsobem. Podívejme se na~je¹tì jeden pøíklad. - -\s{Pøíklad:} Vezmìme si napøíklad staré italské pøízvisko |barbarossa|, které znamená Rudovous. Pøedstavme si, ¾e takovéto slovo hledáme v~nìjakém textu, který zaèíná |barbar|. Víme, ¾e a¾ sem se nám hledaný øetìzec shodoval. Øeknìme, ¾e dal¹í písmenko textu se shodovat pøestane -- místo |o| naèteme napøíklad opìt |b|. {\I Hloupý algoritmus} by velil vrátit se k~|a| a~od~nìj hledat dál. Uvìdomme si ale, ¾e kdy¾ se vracíme z~|barbar| do~|arbar| (tedy øetìzce, který ji¾ známe), mù¾eme si pøedpoèítat, jak dopadne hledání, kdy¾ ho pustíme na~nìj. V~pøedpoèítaném bychom tedy chtìli ukládat, ¾e kdy¾ máme øetìzec |arbar|, tak |ar| a~|r| nám do~hledaného nepasuje a~a¾~|bar| se bude shodovat. Tedy místo toho, abychom spustili nové hledání od~|a|, mù¾eme ho spustit a¾~od~|b|. Co víc, my dokonce víme, jak dopadne to -- pokud toti¾ nastane neshoda po~pøeètení |barbar|, je to stejné, jako kdybychom pøeèetli pouze |bar|, na~které se (pùvodne neshodující se) |b| u¾ navázat dá. Kdyby se nedalo navázat ani tam, tak bychom opìt zkracovali... Nejen, ¾e tedy víme, kam se máme vrátit, ale víme dokonce i~to, co tam najdeme. - -My¹lenka, ke které míøíme, je pøedpoèítat si nìjakou tabulku, která nám bude øíkat, jak se máme pøi hledání vracet a~jak to dopadne, a~pak u¾ jenom prohlédávat s~pou¾itím této tabulky. - -Aby se nám o~tìchto algoritmech lépe mluvilo a~pøedev¹ím psalo, pojïme si povìdìt nìkolik definic. - -\s{Definice:} -\itemize\ibull -\:{\I Abeceda $\Sigma$} je koneèná mno¾ina znakù\foot{Mù¾eme pøi tom jít a¾~do~extrémù. Pøíkladem extrémních abeced je binární abeceda slo¾ená pouze z~nul a~jednièek. Pøíklad z~druhého konce (který rádi dìlají lingvisti) je abeceda, která má jako abecedu v¹echna èeská slova. V¹echny èeské vìty, pak nejsou nic jiného, ne¾ slova nad touto abecedou. Pou¾itá abeceda tedy mù¾e být i~relativnì obrovská. Dal¹ím takovým pøíkladem mù¾e být Unicode. Pro na¹e potøeby ale zatím budeme pøedpokládat, ¾e abeceda je nejen konstantnì velká, ale i~rozumnì malá. Budeme si moci tedy dovolit napøíklad indexovat pole znakem abecedy (kdybychom nemohli, tak bychom místo pole pou¾ili napøíklad hashovací tabulku, èi nìco podobného\dots).}, ze~kterých tvoøíme text, øetìzce, slova. - - -\:{\I $\Sigma^*$} je mno¾ina v¹ech slov nad abecedou $\Sigma$. Èili mno¾ina v¹ech neprázdných koneèných posloupností znakù ze $\Sigma$. -\endlist -\s{Znaèení:} -Aby se nám nepletlo znaèení, budeme rozli¹ovat promìnné pro slova, promìnné pro písmenka a~promìnné pro èísla. - -\itemize\ibull -\:{\I Slova} budeme znaèit malými písmenky øecké abecedy $\alpha$, $\beta$, ... . -\:$\iota$ bude oznaèovat \uv{jehlu} -\:$\sigma$ bude oznaèovat \uv{seno} -\:{\I Znaky} oznaèíme malými písmeny latinky $a$, $b$, \dots . -\:{\I Èísla} budeme znaèit velkými písmeny $A$, $B$, \dots . -\:{\I Délka slova} $\vert \alpha \vert$ pro $\alpha \in \Sigma^*$ je poèet jeho znakù. -\:{\I Prázdné slovo} znaèíme písmenem $\varepsilon$, $\vert \varepsilon \vert = 0$. -\:{\I Zøetìzení} $\alpha\beta$ vznikne zapsáním slov $\alpha$ a~$\beta$ za sebe. Platí $\vert \alpha\beta \vert=\vert \alpha \vert+\vert \beta \vert$, $\alpha\varepsilon=\varepsilon\alpha=\alpha$. -\:$\alpha[k]$ je $k$-tý znak slova $\alpha$, indexujeme od~$0$. -\:$\alpha[k:l]$ je podslovo zaèínající $k$-tým znakem a~$l$-tý znak je první, který v~nìm není. Jedná se tedy o~podslovo skládající se z~$\alpha[k]$,$\alpha[k+1]$,\dots,$\alpha[l-1]$. Platí tedy: $\alpha[k:k]=\varepsilon$, $\alpha[k:k+1]=\alpha[k]$. Jednu (èi obì) meze mù¾eme i~vynechat, tento zápis pak bude znamenat buï \uv{od zaèátku slova a¾ nìkam}, nebo \uv{odnìkud a¾ do~konce}: -%TODO - zaøadit pod pøedchozí bod -\:$\alpha[:k]$ je {\I prefix} obsahující prvních $k$ znakù slova $\alpha$ ($\alpha[0]$,\dots,$\alpha[k-1]$). -\:$\alpha[k:]$ je {\I suffix} obsahující znaky slova $\alpha$ poèínaje $k$-tým znakem a¾ do~konce. -\:$\alpha[:] = \alpha$ -\endlist - - -V¹imnìme si, ¾e prázdné slovo je prefixem, suffixem i~podslovem jakéhokoliv slova vèetnì sebe sama. -Ka¾dé slovo je také prefixem, suffixem i~podslovem sebe sama. To se ne v¾dy hodí. Nìkdy budeme chtít øíct, ¾e nìjaké slovo je {\I vlastním} prefixem nebo suffixem. To bude znamenat, ¾e to nebude celé slovo. - -\> $\alpha$ je {\I vlastní prefix} slova $\beta \equiv \alpha$ je prefix $\beta~\&~\alpha \neq \beta$. - -\h{Vyhledávací automat (Knuth, Morris, Pratt)} -{\I Vyhledávací automat} bude graf, jeho¾ vrcholùm budeme øíkat {\I stavy}. Jejich jména budou prefixy hledaného slova a~hrany budou odpovídat tomu, jak jeden prefix mù¾eme získat z~pøedchozího prefixu pøidáním jednoho písmene. Poèáteèní stav je prázdné slovo $\varepsilon$ a~koncový je celá $\iota$. Dopøedné hrany grafu budou popisovat pøechod mezi stavy ve~smyslu zvìt¹ení délky jména stavu (dopøedná funkce $h(\alpha)$, urèující znak na~dopøedné hranì z~$\alpha$). Zpìtné hrany grafu budou popisovat pøechod (zpìtná funkce $z(\alpha)$) mezi stavem $\alpha$ a~nejdel¹ím vlastním suffixem $\alpha$, který je prefixem $\iota$, kdy¾ nastane neshoda. - -\figure{barb.eps}{Vyhledávací automat.}{4.1in} - -\s{Hledej($\sigma$):} -\algo -\:$\alpha \leftarrow \varepsilon$. -\:Pro $x\in\sigma$ postupnì: -\:$\indent$Dokud $h(\alpha) \neq x~\&~\alpha \neq \varepsilon : \alpha \leftarrow z(\alpha)$. -\:$\indent$Pokud $h(\alpha) = x: \alpha \leftarrow \alpha x$. -\:$\indent$Pokud $\alpha = \iota$, ohlásíme výskyt. -\endalgo - -\>Vstupem je $\iota$, hledané slovo (jehla) délky $J=\vert \iota \vert$ a~$\sigma$, text (seno) délky $S=\vert \sigma \vert$. -\>Výstupem jsou v¹echny výskyty hledaného slova $\iota$ v~textu $\sigma$, tedy mno¾ina $\left\{ k \mid \sigma[k:k+J]=\iota \right\}$ - -Pojïme nyní dokázat, ¾e tento algoritmus správnì ohlásí v¹echny výskyty. - -\s{Definice}: $\alpha(\tau) := $ stav automatu po~pøeètení $\tau$ - -\s{Invariant:} Pokud algoritmus pøeète nìjaký vstup, nachází se ve~stavu, který je nejdel¹ím suffixem pøeèteného vstupu, který je nìjakým stavem. -$\alpha(\tau) =$ nejdel¹í stav (nejdel¹í prefix jehly), který je suffixem $\tau$ (pøeèteného vstupu). - -Pojïme si rozmyslet, ¾e z~tohoto invariantu ihned plyne, ¾e algoritmus najde to, co má. Kdykoli toti¾ ohlásí nìjaký výskyt, tak tam tento výskyt opravdu je. Kdykoli pak má nìjaký výskyt ohlásit, tak se v~této situaci jako suffix toho právì pøeèteného textu vyskytuje hledané slovo, pøièem¾ hledané slovo je urèitì stav a~zároveò nejdel¹í ze v¹ech existujících stavù. Tak¾e invariant opravdu øíká, ¾e jsme právì v~koncovém stavu a~algoritmus nám tedy ohlásí výskyt. - -\proof {\I (invariantu)} -Indukcí podle kroku algoritmu. Na~zaèátku pro prázdný naètený vstup invariant triviálnì platí, tedy prázdný suffix $\tau$ je prefixem $\iota$. V~kroku $n$ máme naètený vstup $\tau$ a~k~nìmu pøipojíme znak $x$. Invariant nám øíká, ¾e nejdel¹í stav, který je suffixem, je nejdel¹í suffix, který je stavem. Nyní se ptáme, jaký je nejdel¹í stav, který se dá \uv{napasovat} na~konec øetìzce $\tau x$. Kdykoli v¹ak takovýto suffix máme, tak z~nìj mù¾eme $x$ na~konci odebrat, èím¾ dostaneme suffix slova $\tau$. - -\>Tedy: pokud $\beta$ je neprázdným suffixem slova $\tau x$, pak $\beta = \gamma x$, kde $\gamma$ je suffix $\tau$. - -Suffix, který máme sestrojit, tedy vznikne z~nìjakého suffixu slova $\tau$ pøipsáním~$x$. Chceme najít nejdel¹í suffix slova $\tau x$, který je stavem, tak¾e chceme najít i~nejdel¹í suffix pùvodního slova $\tau$, za který se dá pøidat $x$ tak, aby vy¹lo jméno stavu. Staèí tedy u¾ jen \uv{probírat} suffixy slova $\tau$ od~nejdel¹ího po~nejkrat¹í, zkou¹et k~nim pøidávat $x$ a~a¾ to pùjde, tak jsme na¹li nejdel¹í suffix $\tau x$. Pøesnì toto ov¹em algoritmus dìlá, nebo» zpìtná funkce mu v¾dy øekne nejbli¾¹í krat¹í suffix, který je stavem. Pokud pak nemù¾eme $x$ pøidat ani do~$\varepsilon$, pak je øe¹ením prázdný suffix. Algoritmus tedy funguje. \qed - -Nyní pojïmì zkoumat to, jak je ve~skuteènosti ná¹ algoritmus rychlý. K tomu bychom si ale nejdøív mìli øíct, jak pøesnì budeme automat reprezentovat. V~algoritmu vystupují nìjaká porovnávání stavù, pøièem¾ není úplnì jasné, jak zaøídit, aby v¹e trvalo konstantnì dlouho. Vyjde nám to ale docela snadno. K reprezentaci automatu nám toti¾ budou staèit pouze dvì pole. - -\s{Reprezentace automatu:} -Oèíslujeme si stavy délkami pøíslu¹ných prefixù, tedy èísly $0 \dots J$. Poté je¹tì potøebujeme nìjakým zpùsobem zakódovat dopøedné a~zpìtné hrany. Vzhledem k~tomu, ¾e z~ka¾dého vrcholu vede v¾dy nejvý¹e jedna dopøedná a~nejvý¹e jedna zpìtná, tak nám evidentnì staèí pamatovat si pro ka¾dý typ hran pouze jedno èíslo na~vrchol. Budeme mít tedy nìjaké pole dopøedných hran, které nám pro ka¾dý stav øekne, jakým písmenkem je nadepsaná dopøedná hrana ze stavu $I$ do~$I+1$. To jsou ale pøesnì písmenka jehly, tak¾e si staèí pamatovat jehlu samotnou. Èili z~$I$ do~$I+1$ vede hrana nadepsaná $\iota [I]$. Pro zpìtné hrany pak budeme potøebovat pole $Z$, které nám pro stav $I$ øekne èíslo stavu, do~kterého vede zpìtná hrana. Tedy $Z[I]$ je cíl zpìtné hrany ze stavu $I$. -S~touto reprezentací ji¾ doká¾eme na¹i hledací proceduru pøímoèaøe pøepsat tak, aby sahala pouze do~tìchto dvou polí: -\algo -\:$I \leftarrow 0$. -\:Pro znaky $x$ z~textu: -\:$\indent$Dokud $\iota[I] \neq x~\&~I \neq 0: I \leftarrow Z[I]$. -\:$\indent$Pokud $\iota[I] = x$, pak $I \leftarrow I + 1$. -\:$\indent$Pokud $I = J$, ohlásíme výskyt. -\endalgo - -Zatím se v~algoritmu je¹tì skrývá drobná chyba -- toti¾ algoritmus se obèas zeptá na~dopøednou hranu z~posledního stavu. Pokud jsme právì ohlásili výskyt (jsme tedy v~posledním stavu) a~pøijde nìjaký dal¹í znak, algoritmus se ptá, zda je roven tomu, co je na~dopøedné hranì z~posledního stavu. Ta ale ov¹em neexistuje. Jednodu¹e to ale napravíme tak, ¾e si pøidáme fiktivní hranu, na~které se vyskytuje nìjaké \uv{nepísmenko} -- nìco, co se nerovná ¾ádnému jinému písmenku. Zajistíme tak, ¾e se po~této hranì nikdy nevydáme. Dodefinujeme tedy $\iota[J]$ odli¹nì od~v¹ech znakù.\foot{V jazyce C se toto dodefinování provede vlastnì zadarmo, nebo» ka¾dý øetìzec je v~nìm ukonèen znakem s~kódem nula, který se ve~vstupu nevyskytne\dots Algoritmus bude tedy fungovat i~bez tohoto dodefinování. V jiných jazycích je ale tøeba na~nìj nezapomenout!} - -\s{Lemma:} Funkce {\I Hledej} bì¾í v~èase $\O(S)$. - -\proof -Funkce {\I Hledej} chodí po~dopøedných a~zpìtných hranách. Dopøedných hran projdeme urèitì maximálnì tolik, kolik je délka sena. Pro ka¾dý znak pøeètený ze sena toti¾ jdeme nejvý¹e jednou po~dopøedné hranì. Se zpìtnými hranami se to má tak, ¾e na~jeden pøeètený znak z~textu se mù¾eme po~zpìtné hranì vracet maximálnì $J$-krát. Z~tohoto by nám v¹ak vy¹la slo¾itost $\O(JS)$, èím¾ bychom si nepomohli. Zachrání nás ale pøímoèarý potenciál. Uvìdomme si, ¾e chùze po~dopøedné hranì zvý¹í $I$ o~jedna a~chùze po~zpìtné hranì $I$ sní¾í alespoò o~jedna. Vzhledem k~tomu, ¾e $I$ není nikdy záporné a~na~zaèátku je nulové, zjistíme, ¾e krokù zpìt mù¾e být maximálnì tolik, kolik krokù dopøedu. Èasová slo¾itost hledání je tedy lineární vzhledem k~délce sena. \qed - -Nyní nám zbývá na~první pohled malièkost -- toti¾ zkonstruovat automat. Zkonstruovat dopøedné hrany zvládneme zjevnì snadno, jsou toti¾ explicitnì popsané hledaným slovem. Tì¾¹í u¾ to bude pro hrany zpìtné. Vyu¾ijeme k~tomu následující pozorování: - -\s{Pozorování:} -Pøedstavme si, ¾e automat u¾ máme hotový a~tím, ¾e budeme sledovat jeho chování, chceme zjistit, jak v~nìm vedou zpìtné hrany. -Vezmìme si nìjaký stav~$\beta$. To, kam z~nìj vede zpìtná hrana zjistíme tak, ¾e spustíme automat na~øetìzec $\beta$~bez prvního písmenka a~stav, ve~kterém se automat zastaví, je pøesnì ten, kam má vést i~zpìtná hrana z~$\beta$. Jinými slovy víme, ¾e $z(\beta) = \alpha (\beta[1:])$. -Proè takováto vìc funguje? V¹imìme si, ¾e definice $z$ a~to, co nám o~$\alpha$ øíká invariant, je témìø toto¾né -- $z(\beta)$ je nejdel¹í vlastní suffix $\beta$, který je stavem, $\alpha(\beta)$ je nejdel¹í suffix $\beta$, který je stavem. Jediná odli¹nost je v~tom, ¾e definice $z$ narozdíl od~definice $\alpha$ zakazuje nevlastní suffixy. Jak nyní vylouèit suffix $\beta$, který by byl roven $\beta$ samotné? Zkrátíme $\beta$ o~první znak. Tím pádem v¹echny suffixy $\beta$ bez prvního znaku jsou stejné jako v¹echny vlastní suffixy $\beta$. - -K èemu je toto pozorování dobré? Rozmysleme si, ¾e pomocí nìj u¾ doká¾eme zkonstruovat zpìtné hrany. Není to ale trochu divné, kdy¾ pøi simulování automatu na~øetìzec bez prvního znaku u¾ zpìtné hrany potøebujeme? Není. Za chvíli zjistíme, ¾e takto mù¾eme zji¹»ovat zpìtné hrany postupnì -- a~to tak, ¾e pou¾íváme v¾dy jenom ty, které jsme u¾ sestrojili. - -Takovémuhle pøístupu, kdy pøi konstruování chtìného u¾ pou¾íváme to, co chceme sestrojit, ale pouze ten kousek, který ji¾ máme hotový, se v~angliètinì øíká {\I bootstrapping}\foot{Z~tohoto slova vzniklo i~{\I bootování} poèítaèù, kdy operaèní systém v~podstatì zavádí sám sebe. Bootstrap znamená èesky ¹truple -- tedy oèko na~konci boty, které slou¾í k~usnadnìní nazouvání. A~jak souvisí ¹truple s~algoritmem? To se zase musíme vrátit k~pøíbìhùm o~baronu Prá¹ilovi, mezi nimi¾ je i~ten, ve~kterém baron Prá¹il vypráví o~tom, jak sám sebe vytáhl z~ba¾iny za ¹truple. Stejnì tak i~my budeme algoritmus konstruovat tím, ¾e se budeme sami vytahovat za ¹truple, tedy bootstrappovat.}. -V¹imnìme si, ¾e pøi výpoètu se vstupem $\beta$ projde automat jenom prvních $\vert \beta \vert$ stavù. Automat se evidentnì nemù¾e dostat dál, proto¾e na~ka¾dý krok dopøedu (doprava) spotøebuje písmenko $\beta$. Tak¾e krokù doprava je maximálnì tolik, kolik je $\vert \beta \vert$. Jinými slovy kdybychom ji¾ mìli zkonstruované zpìtné hrany pro prvních $\vert \beta \vert$ stavù (tedy $0 \dots \vert \beta \vert - 1$), tak pøi tomto výpoètu, který potøebujeme na~zkonstruování zpìtné hrany z~$\beta$, je¹tì tuto zpìtnou hranu nemù¾eme potøebovat. Vystaèíme si s~tìmi, které ji¾ máme zkonstruované. - -Nabízí se tedy zaèít zpìtnou hranou z~prvního znaku (která vede evidentnì do~$\varepsilon$), pak postupnì brát dal¹í stavy a~pro ka¾dý z~nich si spoèítat, kdy spustíme automat na~jméno stavu bez prvního znaku a~tím získáme dal¹í zpìtnou hranu. Toto funguje, ale je to kvadratické \dots. Máme toti¾ $J$ stavù a~pro ka¾dý z~nich nám automat bì¾í v~èase a¾ lineárním s~$J$. Jak z~toho ven? - -Z~prvního stavu povede zpìtná funkce do~$\varepsilon$. Pro dal¹í stavy chceme spoèítat zpìtnou funkci. Z~druhého stavu $\iota[0:2]$ tedy automat spustíme na~$\iota[1:2]$, dále pak na~$\iota[1:3]$, $\iota[1:4]$, atd. Ty øetìzce, pro které potøebujeme spo¹tìt automat, abychom dostali zpìtné hrany, jsou tedy ve~skuteènosti takové, ¾e ka¾dý dal¹í dostaneme roz¹íøením pøedchozího o~jeden znak. To jsou ale pøesnì ty stavy, kterými projde automat pøi zpracovávání øetìzce $\iota$ od~prvního znaku dál. Jedním prùchodem automatu nad jehlou bez prvního písmenka se tím pádem rovnou dozvíme v¹echny údaje, které potøebujeme. -Z~pøedchozího pozorování plyne, ¾e nikdy nebudeme potøebovat zpìtnou hranu, kterou jsme je¹tì nezkonstruovali a~jeliko¾ víme, ¾e jedno prohledání trvá lineárnì s~délkou toho, v~èem hledáme, tak toto celé pobì¾í v~lineárním èase. Dostaneme tedy následující algoritmus: - -\s{Konstrukce zpìtné funkce:} -\algo -\:$Z[0] \leftarrow ?$, $Z[1] \leftarrow 0$. -\:$I \leftarrow 0$. -\:Pro $k = 2 \dots J$: -\::$I \leftarrow \( I , \iota [k])$. -\::$Z[k] \leftarrow I$. -\endalgo - -Zaèínáme tím, ¾e nastavíme zpìtnou hranu z~prvních dvou stavù, pøièem¾ $z[0]$ je nedefinované, proto¾e tuto zpìtnou hranu nikdy nepou¾íváme. Dále postupnì simulujeme výpoèet automatu nad slovem bez prvního znaku a~po ka¾dém kroku se dozvíme novou zpìtnou hranu. {\I Krokem} automatu pak není nic jiného ne¾ vnitøek (3. a~4. bod) na¹í hledací procedury. To, kam jsme se dostali, pak zaznamenáme jako zpìtnou funkci z~$k$. -Èili pou¹tíme automat na~jehlu bez prvního písmenka, provedeme v¾dy jeden krok automatu (pøes dal¹í písmenko jehly) a~zapamatujeme si, jakou zpìtnou funkci jsme zrovna dostali. Díky pozorováním navíc víme, ¾e zpìtné hrany konstruujeme správnì, nikdy nepou¾ijeme zpìtnou hranu, kterou jsme je¹tì nesestrojili a~víme i~to, ¾e celou konstrukci zvládneme v~lineárním èase s~délkou jehly. - -\s{Vìta:} Algoritmus KMP najde v¹echny výskyty v~èase $O(J+S)$. - -\proof -Lineární èas s~délkou jehly potøebujeme na~postavení automatu, lineární èas s~délkou sena pak potøebujeme na~samotné vyhledání. - -\h{Rabinùv-Karpùv algoritmus} - -Nyní si uká¾eme je¹tì jeden algoritmus na~hledání jedné jehly, který nebude mít v~nejhor¹ím pøípadì lineární slo¾itost, ale bude ji mít prùmìrnì. Bude daleko jednodu¹¹í a~uká¾e se, ¾e je v~praxi daleko rychlej¹í. Bude to algoritmus zalo¾ený na~hashování. - - -Pøedstavme si, ¾e máme seno délky $S$ a~jehlu délky $J$, a~vezmìme si nìjakou hashovací funkci, které dáme na~vstup $J$-tici znakù (tedy podslova dlouhá jako jehla). Tato hashovací funkce nám je pak zobrazí do~mno¾iny $\{0,\ldots,N-1\}$ pro nìjaké dost velké~$N$. Jak nám toto pomù¾e pøi hledání jehly? Vezmeme si libovolné \uv{okénko} délky $J$ a~ne¾ budeme zji¹»ovat, zda se v~nìm jehla vyskytuje, tak si spoèítáme hashovací funkci a~porovnáme ji s~hashem jehly. Èili ptáme se, jestli je hash ze sena od~nìjaké pozice $I$ do~pozice $I+J$ roven hashi jehly -- formálnì: $h(\sigma [I: I+J ]) = h(\iota)$. Teprve tehdy, kdy¾ zjistíme, ¾e se hodnota hashovací fce shoduje, zaèneme doopravdy porovnávat øetìzce. - -Není to ale nìjaká hloupost? Mù¾e nám vùbec takováto konstrukce pomoci? Není to tak, ¾e na~spoèítání hashovací funkce z~$J$ znakù, potøebujeme tìch $J$ znakù pøeèíst, co¾ je stejnì rychlé, jako rovnou øetìzce porovnávat? Pou¾ijeme trik, který bude spoèívat v~tom, ¾e si zvolíme ¹ikovnou hashovací funkci. Udìláme to tak, abychom ji mohli pøi posunutí \uv {okénka} o~jeden znak doprava v~konstantním èase pøepoèítat. Chceme umìt z~$h(x_1 \dots x_j)$ spoèítat $h(x_2 \dots x_{j+1})$. -Na~zaèátku si tedy spoèítáme hash jehly a~první $J$-tice znakù sena. Pak ji¾ jenom posouváme \uv {okénko} o~jedna, pøepoèítáme hashovací funkci a~kdy¾ se shoduje s~hashem jehly, tak porovnáme. Budeme pøitom vìøit tomu, ¾e pokud se tam jehla nevyskytuje, pak máme hashovací funkci natolik rovnomìrnou, ¾e pravdìpodobnost toho, ¾e se pøesto strefíme do~hashe jehly, je $1/N$. Jinými slovy jenom v~jednom z~øádovì $N$ pøípadù budeme porovnávat fale¹nì -- tedy provedeme porovnání a~vyjde nám, ¾e výsledek je neshoda. V~prùmìrném pøípadì tedy mù¾eme stlaèit slo¾itost a¾ témìø k~lineární. - -Podívejme se teï na~prùmìrnou èasovou slo¾itost. Budeme urèitì potøebovat èas na~projití jehly a~sena. Navíc strávíme nìjaký èas nad fale¹nými porovnáními, kterých bude v~prùmìru na~ka¾dý $N$-tý znak sena jedno porovnání s~jehlou -- tedy $SJ / N$, pøièem¾ $N$ mù¾eme zvolit dost velké na~to, abychom tento èlen dostali pod nìjakou rozumnou konstantu... Nakonec budeme potøebovat jedno porovnání na~ka¾dý opravdový výskyt, èemu¾ se nevyhneme. Pøipoèteme tedy je¹tì $J \cdot$ {\I $\sharp$výskytù}. Dostáváme tedy: $ \O(J+S+SJ/N+J \cdot$ {\I $\sharp$výskytù}). - -Zbývá malièkost -- toti¾ kde vzít hashovací funkci, která toto v¹e splòuje. Jednu si uká¾eme. Bude to vlastnì takový hezký polynom: -$$h(x_1 \dots x_j) := \left(\sum_{I=1}^{J} x_I \cdot p^{J-I}\right) \bmod N.$$ -Jinak zapsáno se tedy jedná o: -$$(x_1 \cdot p^{J-1} + x_2 \cdot p^{J-2} + \dots + x_J \cdot p^0 ) \bmod N.$$ -Po posunutí okénka o~jedna chceme dostat: -$$(x_2 \cdot p^{J-1} + x_3 \cdot p^{J-2} + \dots + x_J \cdot p^1 + x_{J+1} \cdot p^0 ) \bmod N.$$ -Kdy¾ se ale podíváme na~èleny tìchto dvou polynomù, zjistíme, ¾e se li¹í jen o~málo. Pùvodní polynom staèí pøenásobit~$p$, odeèíst první èlen s~$x_1$ a~naopak pøièíst chybìjící èlen $x_{J+1}$. Dostáváme tedy: -$$h(x_2 \dots x_{J+1}) = (p \cdot h(x_1 \dots x_J) - x_1 \cdot p^J + x_{J+1}) \bmod N.$$ -Pøepoèítání hashovací funkce tedy není nic jiného, ne¾ pøenásobení té minulé~$p$, odeètení nìjakého násobku toho znaku, který vypadl z~okénka, a~pøiètení toho znaku, o~který se okénko posunulo. Pokud tedy máme k~dispozici aritmetické operace v~konstantním èase, zvládneme konstantnì pøepoèítávat i~hashovací funkci. - -Tato hashovací funkce se dokonce nejen hezky poèítá, ale dokonce se i~opravdu \uv{hezky} chová (tedy \uv{rozumnì} náhodnì), pokud zvolíme vhodné~$p$. To bychom mìli zvolit tak, aby bylo rozhodnì nesoudìlné s~$N$ -- tedy $\(p, N) = 1$. Aby se nám navíc dobøe projevilo modulo obsa¾ené v~hashovací funkci, mìlo by být~$p$ relativnì velké (lze dopoèítat, ¾e optimum je mezi $2/3 \cdot N$ a~$3/4 \cdot N$). S~takto zvoleným~$p$ se tato hashovací funkce chová velmi pøíznivì a~v~praxi má celý algoritmus takøka lineární èasovou slo¾itost (prùmìrnou). - -\h{Hledání více øetìzcù najednou} -Nyní si zahrajeme tuté¾ hru, ov¹em v~trochu slo¾itìj¹ích kulisách. Podíváme se na~algoritmus, který si poradí i~s více ne¾ jednou jehlou. -Mìjme tedy jehly $\iota_1 \dots \iota_n$, a~jejich délky $J_i = \vert \iota_i \vert $. Dále budeme potøebovat seno $\sigma$ délky $S=\vert \sigma \vert$. - -Pøedtím, ne¾ se pustíme do~vlastního vyhledávacího algoritmu, mo¾ná bychom si mìli ujasnit, co vlastnì bude jeho výstupem. U problému hledání jedné jehly to bylo jasné -- byla to nìjaká mno¾ina pozic v~senì, na~kterých zaèínaly výskyty jehly. Jak tomu ale bude zde? Sice bychom také mohli vrátit pouze mno¾inu pozic, ale my budeme chtít malièko víc. Budeme toti¾ chtít vìdìt i~to, která jehla se na~které pozici vyskytuje. Výstup tedy bude vypadat následovnì: $V = \{(i,j)~\vert~\sigma[i:i+J_j]= \iota_j \}$. - -Zde se v¹ak skrývá jedna drobná zrada. Budeme se asi muset vzdát nadìje, ¾e najdeme algoritmus, jeho¾ slo¾itost je lineární v~celkové délce v¹ech jehel a~sena. Výstup toti¾ mù¾e být del¹í ne¾ lineární. Mù¾e se nám klidnì stát, ¾e na~jedné pozici v~senì se bude vyskytovat více rùzných jehel -- pokud bude jedna jehla prefixem jiné (co¾ jsme nikde nezakázali), tak máme povinnost ohlásit oba výskyty. Vzhledem k~tomu budeme hledat takový algoritmus, který bude lineární v~délce vstupu plus délce výstupu, co¾ je evidentnì to nejlep¹í, èeho mù¾eme dosáhnout. - -Algoritmus, který si nyní uká¾eme, vymysleli nìkdy v~roce 1975 pan Aho a~paní Corasicková. Bude to takové zobecnìní Knuthova-Morrisova-Prattova algoritmu. - -\h{Algoritmus Aho-Corasicková} - -Opìt se budeme sna¾it sestrojit nìjaký vyhledávací automat a~nìjakým zpùsobem tento automat pou¾ít k~procházení sena. Podívejme se nejprve na~pøíklad. Budeme chtít vyhledávat tato slova: |ara|, |bar|, |arab|, |baraba|, |barbara|. Mìjme tedy tìchto pìt jehel a~rozmysleme si, jak by vypadal nìjaký automat, který by tato slova umìl zatím jenom rozpoznávat. Pro jedno slovo automat vypadal jako cesta, zde u¾ to bude strom. (viz obrázek). - -\figure{ara_strom_blank.eps}{Vyhledávací automat -- strom.}{1in} - -Navíc budeme muset do~automatu zanést, kde nìjaké slovo konèí. V~pùvodním automatu pro jedno slovo to bylo jednoduché -- ono jedno jediné slovo odpovídalo poslednímu vrcholu cesty. Tady se v¹ak slova mohou vyskytovat vícekrát a~konèit nejenom v~listech ale i~v~nìjakém vnitøním vrcholu (co¾ se stane tehdy, pokud je jedno hledané slovo prefixem jiného hledaného slova). Formálnì to nebudeme dokazovat, ale snadno nahlédneme, ¾e listy stromu odpovídají hledaným slovùm, ale opaènì to neplatí. - -\figure{ara_strom_end.eps}{Vyhledávací automat s~konci slov.}{1in} - -Dále bychom mìli do~automatu pøidat zpìtné hrany. Jejich definice bude úplnì stejná jako u automatu pro hledání jednoho slova. Jinými slovy z~ka¾dého stavu pùjde zpìtná hrana do~nejdel¹ího vlastního suffixu, který je stavem. Èili kdy¾ budeme mít nìjaké jméno stavu, budeme se ho sna¾it co nejménì (ale alespoò o~znak) zkrátit zleva, abychom zase dostali jméno stavu. Z~koøene -- prázdného stavu -- pak evidentnì ¾ádná zpìtná hrana nepovede. - -\figure{ara_strom_final.eps}{Vyhledávací automat se zpìtnými hranami.}{1,25in} - -Zbývá nám je¹tì si rozmyslet, jakým zpùsobem bude ná¹ automat hlásit výstup. Opìt smìøujeme k~tomu, aby se automat po~pøeètení nìjakého kusu textu nacházel ve~stavu odpovídajícímu nejdel¹ímu mo¾nému suffixu toho textu. Zatímco u hledání jediné jehly bylo hlá¹ení výskytù jednoduché -- kdykoliv jsme se dostali na~konec \uv{automatové cestièky} tady to bude opìt slo¾itìj¹í. - -První, co se nabízí, je vyu¾ít toho, ¾e jsme si oznaèili nìjaké vrcholy, kde hledaná slova konèí. Co tedy zkusit hlásit výskyt tohoto slova v¾dy, kdy¾ pøijdeme do~nìjakého oznaèeného vrcholu? Tento zpùsob v¹ak nefunguje, pokud se uvnitø nìkteré jehly skrývá jehla vnoøená. Napøíklad po~pøeètení slova |bara|, nám ná¹ souèasný automat neøíká, ¾e bychom mìli nìjaké slovo ohlásit, a~pøitom tam evidentì konèí podøetìzec |ara|. Stejnì tak pokud pøeèteme |barbara|, u¾ si nev¹imneme toho, ¾e tam konèí zároveò i~|ara|. Pouhé \uv{hlá¹ení teèek} tedy nefunguje. - -Dále si mù¾eme v¹imnout toho, ¾e v¹echna slova, která by se mìla v~daném stavu hlásit, jsou suffixy jména tohoto stavu. Pøitom víme, ¾e zpìtná hrana jméno stavu zkracuje zleva. Tak¾e speciálnì v¹echny suffixy daného stavu, které jsou také stavy, se dají najít tak, ¾e se vydáme po~zpìtných hranách do~koøene. Nabízí se tedy v¾dy projít cestu po~zpìtných hranách a¾ do~koøene a~hlásit v¹echny \uv{teèky}. Tento zpùsob by nám v¹ak celý algoritmus znaènì zpomalil, proto¾e cesta do~koøene mù¾e být relativnì dlouhá, ale teèek na~ní obvykle bude málo. - -Mohli bychom také zkusit si pro ka¾dý stav $\beta$ pøedpoèítat mno¾inu $cache(\beta)$, která by obsahovala v¹echna slova, která máme hlásit, kdy¾ se ve~stavu $\beta$ nacházíme. Pokud pak do~tohoto stavu vstoupíme, podíváme se na~tuto mno¾inu a~vypí¹eme v¹e, co v~ní je. Výpis nám bude evidentnì trvat lineárnì k~velikosti mno¾iny, celkovì tedy lineárnì k~velikosti výstupu. Problém je ale ten, ¾e jednotlivé cache mohou být hodnì velké, tak¾e je nestihneme sestrojit v lineárním èase. (Rozmyslete si pøíklad slovníku, kdy se to stane.) - -To, co nám ale ji¾ opravdu pomù¾e, bude zavedení zkratek. V¹imli jsme si, ¾e po~zpìtných hranách mù¾eme projít do~koøene a~hlásit v¹echny nalezené teèky. Vadilo nám ale, ¾e se mù¾e stát, ¾e budeme dlouho po~cestì chodit a~pøi tom ¾ádné teèky nenalézat. Zavedeme si proto zkratky k~nejbli¾¹í teèce. - -\s{Definice} (zkratková hrana): -Budeme mít tedy nìjakou funkci $slovo(\beta) :=$ slovo, které konèí ve~stavu $\beta$ (nebo $\emptyset$, pokud ¾ádné takové slovo není). Dále pak funkci $out(\beta) :=$ nejbli¾¹í vrchol dosa¾itelný po~zpìtných hranách, èili nejdel¹í vlastní suffix stavu $\beta$, v~nìm¾ je definovaná funkce $slovo$. Trochu lid¹tìji øeèeno, ten nejbli¾¹í dosa¾itelný vrchol, ve~kterém je teèka. - -Po pøidání tìchto zkratkových hran ji¾ máme reprezentaci, ve~které opravdu umíme v~daném stavu vyjmenovat v¹echna slova, která máme vypsat, a~to v~èase lineárním s~tím, kolik tìch slov je. - -\s{Definice:} -Vyhledávací automat sestává ze stromu dopøedných hran (vrcholy jsou prefixy jehel, hrany odpovídají roz¹íøení o~písmenko), zpìtných hran ($z(\beta) :=$ nejdel¹í vlastní suffix slova $\beta$, který je stavem) a~zkratkových hran. - -Automat pak bude na~na¹em pøíkladu vypadat takto (zkratkové hrany jsou znázornìny zelenì): - -\figure{ara_strom_zkr.eps}{Vyhledávací automat se zkratkovými hranami.}{1,25in} - -Nyní u¾ nám zbývá jenom vlastní algoritmus -- nejdøív popí¹eme algoritmus, který bude hledat pomocí takového automatu, a~potom se pustíme do~toho, jak se takový automat staví. - -Nejprve si nadefinujeme, jak vypadá jeden krok automatu. Bude to vlastnì nìjaká funkce, která dostane stav a~písmenko. Ona nás pak pomocí tohoto písmenka posune po~automatu. ($f(\alpha, x)$ bude dopøedná hrana ze stavu $\alpha$ oznaèená písmenem~$x$) - -\s{Krok ($\alpha$, $x$):} -\algo -\:Dokud $f(\alpha, x) = \emptyset~\&~\alpha \neq \~~\alpha \leftarrow z(\alpha)$. -\:Pokud $f(\alpha, x) \neq \emptyset:~~\alpha \leftarrow f(\alpha, x)$. -\:Vrátíme výsledek. -\endalgo - -\s{Hledání:} -\algo -\:$\alpha \leftarrow \$. -\:Pro znaky $x$ ze slova $\sigma$: -\::$\alpha \leftarrow \(\alpha, x)$. -\::$\beta \leftarrow \alpha$ -\::Dokud $\beta \neq \emptyset$: -\:::Je-li $\(\beta) \neq \emptyset$: -\::::Ohlásíme $\(\beta)$. -\:::$\beta \leftarrow \(\beta)$. -\endalgo - -Algoritmus hledání vlastnì není nic jiného, ne¾ prosté projití po~zelených zkratkových hranách ze stavu $\alpha$, ve~kterém právì jsme, a~ohlá¹ení v¹eho, co po~cestì najdeme. - -V ka¾dém okam¾iku se automat nachází ve~stavu, který odpovídá nejdel¹ímu mo¾nému suffixu toho, co jsme u¾ pøeèetli. Dùkaz tohoto invariantu je stejný jako u verze automatu pro hledání pouze jedné jehly, nebo» vychází pouze z~definice zpìtných hran. Podobnì nahlédneme, ¾e èasová slo¾itost vyhledávací procedury je lineární v~délce sena plus to, co spotøebujeme na~hlá¹ení výskytù. Nejprve na~chvíli zapomeneme, ¾e nìjaké výskyty hlásíme a~spoèítáme jenom kroky. Ty mohou vést dopøedu a~zpátky. Krok dopøedu prodlu¾uje jméno stavu o~jedna, krok dozadu zkracuje aspoò o~jedna. Tudí¾ krokù dozadu je maximálnì tolik, co krokù dopøedu a~krokù dopøedu je maximálnì tolik, kolik je délka sena. V¹echny kroky dohromady tedy trvají $\O(S)$. Hlá¹ení výskytù pak trvá $\O(S~+ \vert V \vert)$. Celé hledání tedy trvá lineárnì v~délce vstupu a~výstupu. - -Zbývá nám u¾ jen konstrukce automatu. Opìt vyu¾ijeme faktu, ¾e zpìtná hrana ze stavu $\beta$ vede tam, kam by se dostal automat pøi hledání $\beta$ bez prvního písmenka. Tak¾e zase chceme nìco, jako simulovat výpoèet toho automatu na~slovech bez prvního písmenka a~doufat v~to, ¾e si vystaèíme s~tou èástí automatu, kterou jsme u¾ postavili. Tentokrát to v¹ak nemù¾eme dìlat jedno slovo po~druhém, proto¾e zpìtné hrany mohou vést køí¾em mezi jednotlivými vìtvemi automatu. Mohlo by se nám tedy stát, ¾e pøi hledání nìjakého slova potøebujeme zpìtnou hranu, která vede do~jiného slova, které jsme je¹tì nezkonstruovali. Tak¾e tento postup sel¾e. Mù¾eme v¹ak vyu¾ít toho, ¾e ka¾dá zpìtná hrana vede ve~stromu alespoò o~jednu hladinu vý¹. Mù¾eme tak strom konstruovat po~hladinách. Lze si to tedy pøedstavit tak, ¾e paralelnì spustíme vyhledávání v¹ech slov bez prvních písmenek a~v¾dycky udìláme jeden podkrok ka¾dého z~tìch hledání, co¾ nám dá zpìtné hrany z~dal¹ího patra stromu. - -\s{Konstrukce automatu:} -\algo -\:Zalo¾íme prázdný strom, $r \leftarrow$ jeho koøen. -\:Vlo¾íme do~stromu slova $\iota_1 \dots \iota_n$, nastavíme $slovo(*)$. -\:$z(r) \leftarrow \emptyset$, $out(r) \leftarrow \emptyset$. -\:Zalo¾íme frontu $F$ a~vlo¾íme do~ní syny koøene. -\:$\forall v~\in F:~~z(v) \leftarrow r, \(v) \leftarrow \emptyset$. -\:Dokud $F \neq \emptyset$: -\::Vybereme $u$ z~fronty $F$. -\::Pro v¹echny syny $v$ vrcholu $u$: -\:::$q \leftarrow \(z(u), \)$. -\:::$z(v) \leftarrow q$. -\:::Pokud $slovo(q) \neq \emptyset$, pak $out(v) \leftarrow q$. -\::::Jinak $out(v) \leftarrow out(q)$. -\:::Vlo¾íme $v$ do~fronty $F$. -\endalgo - -To, ¾e tento algoritmus zkonstruuje zpìtné hrany jak má, vyplývá z~toho, ¾e nedìláme nic jiného, ne¾ ¾e spou¹tíme výpoèty po~hladinách na~v¹echna hledaná slova bez prvního písmenka. Stejnì tak to, ¾e dobìhne v~lineárním èase, je takté¾ dùsledkem toho, ¾e efektivnì spou¹tíme v¹echny tyto výpoèty. Jen nìkdy udìláme najednou krok dvou èi více výpoètù (napøíklad |araba| a~|arbara| se poèítají na~zaèátku, dokud jsou stejné, jen jednou). Èasová slo¾itost této konstrukce je tedy men¹í nebo rovna souètu èasových slo¾itostí výpoètù nad v¹emi tìmi slovy. To u¾ ale víme, ¾e je lineární v~celkové délce tìchto slov. Konstrukce automatu tedy trvá nejvý¹e tolik, co hledání v¹ech $\iota_i$, co¾ je $\O(\sum_{i} \iota_i)$. - -\s{Vìta:} Algoritmus Aho-Corasicková najde v¹echny výskyty v~èase -$$\O\left(\sum_i~\iota_i~+~S~+~\sharp\\right).$$ - -Je¹tì se na~závìr zamysleme, jak bychom si takový automat ukládali do~pamìti. Urèitì se nám bude hodit si stavy nìjak oèíslovat (tøeba v~poøadí, v~jakém budou vznikat). Potom funkce pro zpìtné a~zkratkové hrany mohou být reprezentované polem indexovaným èíslem stavu. Funkce {\I Slovo}, která øíká, jaké slovo ve~stavu konèí, zase mù¾e být pole indexované stavem, které nám øekne poøadové èíslo slova ve~slovníku. Pro dopøedné hrany v~ka¾dém vrcholu pak mù¾eme mít pole indexované písmenky abecedy, které nám pro ka¾dé písmenko øekne, buï ¾e taková hrana není, nebo nám øekne, kam tato hrana vede. Je vidìt, ¾e takovéto pole se hodí pro pomìrnì malé abecedy. U¾ pro abecedu A-Z~bude velikosti 26 a~z~vìt¹iny bude prázdné, tak¾e bychom plýtvali pamìtí. V praxi se proto èasto pou¾ívá hashovací tabulka. Pøípadnì bychom mohli mít i~jen jednu velkou spoleènou hashovací tabulku, která bude reprezentovat funkci celou, ve~které budou zahashované dvojice (stav, písmenko). Tìchto dvojic je evidentnì tolik, kolik hran stromu, èili lineárnì s~velikostí slovníku, a~je to asi nejkompaktnìj¹í reprezentace. - - -\bye diff --git a/6-kmp/Makefile b/6-kmp/Makefile deleted file mode 100644 index 1831d14..0000000 --- a/6-kmp/Makefile +++ /dev/null @@ -1,3 +0,0 @@ -P=6-kmp - -include ../Makerules diff --git a/6-kmp/ara_strom.eps b/6-kmp/ara_strom.eps deleted file mode 100644 index 7d054ab..0000000 --- a/6-kmp/ara_strom.eps +++ /dev/null @@ -1,1334 +0,0 @@ -%!PS-Adobe-3.0 EPSF-3.0 -%%Creator: inkscape 0.46 -%%Pages: 1 -%%Orientation: Portrait -%%BoundingBox: 163 220 443 747 -%%HiResBoundingBox: 163.64601 220.18395 442.55996 746.83829 -%%EndComments -%%Page: 1 1 -0 842 translate -0.8 -0.8 scale -0 0 0 setrgbcolor -[] 0 setdash -1 setlinewidth -0 setlinejoin -0 setlinecap -gsave [1 0 0 1 0 0] concat -gsave [0.5411688 0 0 0.5411688 203.68828 84.001202] concat -0 0 0 setrgbcolor -[] 0 setdash -1.478282 setlinewidth -2 setlinejoin -0 setlinecap -newpath -299.99999 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$n=1$ etex, pos); - c := (2cm,0); - pos := pos + c; - pair A[]; - A[0] := (-0.3cm, -0.2cm)+c; A[1] := (0.2cm, 0.3cm)+c; - draw A[0]--A[1]; - drawemptyvertex(A[0]); drawemptyvertex(A[1]); - label.bot(btex $n=2$ etex, pos); - - pos := pos + c; - A[2] := (+0.3cm, -0.4cm)+c; - for i := 0 upto 2: A[i] := A[i] shifted c; endfor - draw A[0]--A[1]--A[2]--cycle; - for i := 0 upto 2: drawemptyvertex(A[i]); endfor - label.bot(btex $n=3$ etex, pos); - - pos := pos + c; - A[3] := (A[0]+A[1]+A[2])/3; - for i := 0 upto 3: A[i] := A[i] shifted c; endfor - draw A[0]--A[1]--A[2]--cycle; - for i := 0 upto 2: drawemptyvertex(A[i]); endfor - draw vertex(A[3]); - - c := (1cm,0); - pos := pos + c/2; - A[3] := A[1]+(0.3cm,-0.2cm); - for i := 0 upto 3: A[i] := A[i] shifted c; endfor - draw A[0]--A[1]--A[3]--A[2]--cycle; - for i := 0 upto 3: drawemptyvertex(A[i]); endfor - label.bot(btex $n=4$ etex, pos); -endfig; - -figtag("pridani_bodu"); -beginfig(2); - pair A[],B[],C,shift; shift := (4.5cm,0); - A[0] := (-1.7cm,1.1cm); - A[1] := (-1.2cm,1.2cm); - A[2] := (-0.4cm,1cm); - A[3] := (0.2cm,0.2cm); - A[4] := (0.4cm,-0.7cm); - A[5] := (-0.8cm,-1.3cm); - A[6] := (-1.4cm,-1.4cm); - B[0] := (-1.1cm, 0.7cm); - B[1] := (-0.6cm, 0.1cm); - B[2] := (-1.3cm, -0.6cm); - C := (1cm, 0.1cm); - - % krok 1 - pickup boldpen; - draw A[0] for i := 1 upto 6: --A[i] endfor; - for i := 1 upto 5: drawemptyvertex(A[i]); endfor - for i := 0 upto 2: draw vertex(B[i]); endfor - draw vertex(C); - drawarrow (C+(0.5cm,0)--C+(1.5cm,0)) shifted (0, -0.1cm) withpen normalpen; - for i:=0 upto 6: A[i] := A[i] shifted shift; endfor - for i:=0 upto 2: B[i] := B[i] shifted shift; endfor - C := C shifted shift; - - % krok 2 - draw A[0] for i := 1 upto 6: --A[i] endfor; - draw A[4]{dir 70}..C; - draw A[4]{dir 45}..C; - draw C--A[2] dashed evenly withpen normalpen; - draw C--A[3] dashed evenly withpen normalpen; - for i := 1 upto 5: drawemptyvertex(A[i]); endfor - for i := 0 upto 2: draw vertex(B[i]); endfor - drawemptyvertex(C); - drawarrow (C+(0.5cm,0)--C+(1.5cm,0)) shifted (0,-0.1cm) withpen normalpen; - for i:=0 upto 6: A[i] := A[i] shifted shift; endfor - for i:=0 upto 2: B[i] := B[i] shifted shift; endfor - C := C shifted shift; - - % krok 3 - draw for i := 0 upto 2: A[i]-- endfor C for i := 4 upto 6: --A[i] endfor; - for i := 1 upto 2: drawemptyvertex(A[i]); endfor - for i := 4 upto 5: drawemptyvertex(A[i]); endfor - for i := 0 upto 2: draw vertex(B[i]); endfor - draw vertex(A[3]); - drawemptyvertex(C); -endfig; - -figtag("obalky"); -beginfig(3); - labeloffset := 0.2cm; - pickup boldpen; - pair A[],B[]; - A[0] := (-7cm, 0cm); - A[1] := (-6.2cm, 0.9cm); - A[2] := (-4.6cm,1.5cm); - A[3] := (-2.4cm,1.8cm); - A[4] := (-0.8cm,1.5cm); - A[5] := (0.4cm,0.6cm); - A[6] := (0.8cm,-0.10cm); - A[7] := (-1.6cm,-1.9cm); - A[8] := (-4cm,-2.1cm); - A[9] := (-6cm, -1.5cm); - A[10] := (-7cm, 0cm); - - B[0] := (-2.2cm, 0.7cm); - B[1] := (-1.2cm, 0.1cm); - B[2] := (-2.6cm, -0.6cm); - B[3] := (-3.6cm, -0.4cm); - B[4] := (-3cm, 0.6cm); - B[5] := (-2.6cm, 1cm); - B[6] := (-1cm, -1.2cm); - B[7] := (-6.5cm, 0.2cm); - B[8] := (-5cm, 0.8cm); - B[9] := (-6cm, -0.6cm); - B[10] := (-5cm, -1.2cm); - - draw createpath(for i := 0 upto 5: A[i]-- endfor A[6]); - draw (for i := 6 upto 9: A[i]-- endfor A[10]) dashed evenly; - for i := 0 upto 9: drawemptyvertex(A[i]); endfor - for i := 0 upto 10: draw vertex(B[i]); endfor - - label(btex \font\myfont=csr10 \myfont horní obálka etex, ((-7cm+0.8cm)/2,2.2cm)); - label(btex \font\myfont=csr10 \myfont dolní obálka etex, ((-7cm+0.8cm)/2,-2.5cm)); - label.lft(btex $L$ etex, A[0]); - label.rt(btex $P$ etex, A[6]); -endfig; - -figtag("determinant"); -beginfig(4); - labeloffset := 0.1cm; - pair A[], shift; shift := (4cm,1cm); - - % det(M) > 0 - A[0] := (-2cm, 0); - A[1] := (0,-1cm); - A[2] := (1.5cm, 0cm); - A[3] := A[0] + A[2] - A[1]; - - fill A[0]--A[1]--A[2]--A[3]--cycle withcolor 0.8white; - draw A[0]--A[1]--A[2]--A[3]--cycle dashed evenly; - drawarrow A[0]--0.96[A[0],A[1]] withpen boldpen; - drawarrow A[1]--0.96[A[1],A[2]] withpen boldpen; - for i:=0 upto 2: draw vertex(A[i]); endfor - label.lft(btex $h_{k-1}$ etex, A[0]); - label.bot(btex $h_k$ etex, A[1]); - label.llft(btex $\vec u$ etex, 0.5[A[0],A[1]]); - label.lrt(btex $\vec v$ etex, 0.5[A[1],A[2]]); - label.rt(btex $b$ etex, A[2]); - label(btex $\det(M) > 0$ etex, 0.5[A[0],A[2]]); - - % det(M) = 0 - A[0] := (-1cm, -0.5cm) + shift; - A[1] := (0, -1cm) + shift; - A[2] := (1cm, -1.5cm) + shift; - drawarrow A[0]--0.96[A[0],A[1]] withpen boldpen; - drawarrow A[1]--0.96[A[1],A[2]] withpen boldpen; - for i:=0 upto 2: draw vertex(A[i]); endfor - label.lft(btex $h_{k-1}$ etex, A[0]); - label.llft(btex $h_k$ etex, A[1]); - label.llft(btex $\vec u$ etex, 0.5[A[0],A[1]]); - label.llft(btex $\vec v$ etex, 0.5[A[1],A[2]]); - label.bot(btex $b$ etex, A[2]); - label(btex $\det(M) = 0$ etex, origin) shifted (0,0.3cm) rotated -28 shifted 0.5[A[0], A[2]]; - - % det(M) < 0 - shift := (7.5cm, 1.25cm); - A[0] := (-1cm, -0.5cm) + shift; - A[1] := (1.5cm, -1cm) + shift; - A[2] := (2cm, -2.5cm) + shift; - A[3] := A[0] + A[2] - A[1]; - fill A[0]--A[1]--A[2]--A[3]--cycle withcolor 0.8white; - draw A[0]--A[1]--A[2]--A[3]--cycle dashed evenly; - drawarrow A[0]--0.96[A[0],A[1]] withpen boldpen; - drawarrow A[1]--0.96[A[1],A[2]] withpen boldpen; - for i:=0 upto 2: draw vertex(A[i]); endfor - label.lft(btex $h_{k-1}$ etex, A[0]); - label.urt(btex $h_k$ etex, A[1]); - label.top(btex $\vec u$ etex, 0.5[A[0],A[1]]); - label.rt(btex $\vec v$ etex, 0.5[A[1],A[2]]); - label.rt(btex $b$ etex, A[2]); - label(btex $\det(M) < 0$ etex, 0.5[A[0],A[2]]); -endfig; - -figtag("rybi_motivace"); -beginfig(5); - u := 0.3cm; - def draw_fish(expr pos,size,rot) = - draw ((-1.3u*size,0){dir 60}..{right}(u*size,-u*size/4)) rotated rot shifted pos; - draw ((-1.3u*size,0){dir -50}..{right}(u*size,u*size/4)) rotated rot shifted pos; - draw ((u*size,-u*size/4)--(u*size,u*size/4)) rotated rot shifted pos; - draw (-1u*size,u*size/15) rotated rot shifted pos withpen pencircle scaled (u/8); - for i:=1 upto 3: draw (dirs((u*size,-u*size/4+i*u*size/8), 180, u*size/6)) rotated rot shifted pos; endfor - enddef; - - pair A[],B[]; - A[0] := (-7cm, 0cm); - A[1] := (-6.2cm, 0.9cm); - A[2] := (-4.6cm,1.5cm); - A[3] := (-2.4cm,1.8cm); - A[4] := (-0.8cm,1.5cm); - A[5] := (0.4cm,0.6cm); - A[6] := (0.8cm,-0.10cm); - A[7] := (-1.6cm,-1.9cm); - A[8] := (-4cm,-2.1cm); - A[9] := (-6cm, -1.5cm); - A[10] := (-7cm, 0cm); - - B[0] := (-2.2cm, 0.7cm); - B[1] := (-1.2cm, 0.1cm); - B[2] := (-2.6cm, -0.6cm); - B[3] := (-3.6cm, -0.4cm); - B[4] := (-3cm, 0.6cm); - B[5] := (-2.6cm, 1cm); - B[6] := (-1cm, -1.2cm); - B[7] := (-6.5cm, 0.2cm); - B[8] := (-5cm, 0.8cm); - B[9] := (-6cm, -0.6cm); - B[10] := (-5cm, -1.2cm); - - for i:=0 upto 9: draw_fish(A[i], 1, 0); endfor; - for i:=0 upto 10: draw_fish(B[i], 1, 0); endfor; - draw createpath(for i:=0 upto 9: A[i]-- endfor cycle) scaled 1.13 shifted (0.4cm,0) withpen boldpen; -endfig; - -figtag("provazkovy_algoritmus"); -beginfig(6); - pickup boldpen; - pair A[],B[],u; u := (-3cm, 0); - for i := 0 upto 3: A[i] := u rotated (-30*i) yscaled 0.7; endfor; - A[2] := A[2] + (0,0.1cm); - draw for i:=0 upto 2: A[i]-- endfor A[3]; - drawarrow ((u/2) for i:=1 upto 3: ..u/2 rotated (-30*i) endfor) yscaled 0.7 withpen normalpen; - B[0] := (-2cm,0.5cm); - B[1] := (-1cm,1.5cm); - B[2] := (-0.5cm,0.2cm); - for i:=0 upto 2: draw vertex(B[i]); endfor - - path ub; ub := (-20cm,3cm)--(20cm,3cm); - - numeric ang[]; ang[0] = 90; ang[1] = angle(A[1]-A[0]); ang[2] = angle(A[2]-A[1]); ang[3] = angle(A[3]-A[2]); - for i:=0 upto 2: - draw reverse(dirs(A[i],ang[i],6cm) cutafter ub) withpen normalpen dashed evenly; - l := 1cm + (i-1)*0.2cm; - drawarrow from(A[i],ang[i],l)..from(A[i],(ang[i]+ang[i+1])/2,l)..from(A[i],ang[i+1],l) withpen normalpen; - endfor - - for i:=0 upto 3: drawemptyvertex(A[i]); endfor; -endfig; - -figtag("naslednik_pres_konvexni_obal"); -beginfig(7); - pair A[], C; - label.lrt(btex $Q_i$ etex, (1.5cm,-0.8cm)); - pickup boldpen; - - C := (-4cm,-0.3cm); - for i:=0 upto 6: - A[i] := (2cm,0) rotated (360*i/7+5) yscaled 0.7; - draw vertex(A[i]); - endfor; - draw for i:=0 upto 6: A[i]-- endfor cycle withpen normalpen; - draw C--A[2] dashed evenly; - - draw dirs(C, -140, 0.5cm); - drawemptyvertex(A[2]); - drawemptyvertex(C); - drawdblarrow (fullcircle scaled 2cm rotated (360*2/7-5) yscaled 0.7) cutbefore (origin--(3cm,0) rotated (360*2/7+25)) withpen normalpen; - %drawarrow C+(0,0.5cm){dir 60}..A[2]+(0,0.5cm) withpen normalpen; - %drawarrow 0.6A[5]{dir 170}..(0.6A[3] rotated -15) withpen normalpen; - %drawarrow 0.7A[6]{dir 60}..(0.5A[1] rotated 30) withpen normalpen; -endfig; -end diff --git a/7-geom/7-geom.tex b/7-geom/7-geom.tex deleted file mode 100644 index d600d11..0000000 --- a/7-geom/7-geom.tex +++ /dev/null @@ -1,136 +0,0 @@ -\input lecnotes.tex - -\prednaska{7}{Geometrické algoritmy}{(sepsal Pavel Klavík)} - -\>Uká¾eme si nìkolik základních algoritmù na øe¹ení geometrických problémù v~rovinì. Proè zrovna v~rovinì? Inu, jednorozmìrné problémy bývají triviální -a naopak pro vy¹¹í dimenze jsou velice komplikované. Rovina je proto rozumným kompromisem mezi obtí¾ností a zajímavostí. - -Celou kapitolou nás bude provázet pohádka ze ¾ivota ledních medvìdù. Pokusíme se vyøe¹it jejich \uv{ka¾dodenní} problémy~\dots - -\h{Hledání konvexního obalu} - -{\I Daleko na severu ¾ili lední medvìdi. Ve vodách tamního moøe byla hojnost ryb a jak je známo, ryby jsou oblíbenou pochoutkou ledních medvìdù. -Proto¾e medvìdi z~na¹í pohádky rozhodnì nejsou ledajací a ani chytrost jim neschází, rozhodli se v¹echny ryby pochytat. Znají pøesná místa výskytu -ryb a rádi by vyrobili obrovskou sí», do které by je v¹echny chytili. Pomozte medvìdùm zjistit, jaký nejmen¹í obvod taková sí» mù¾e mít.} - -\figure{7-geom5_rybi_motivace.eps}{Problém ledních mìdvìdù: Jaký je nejmen¹í obvod sítì?}{3in} - -Neboli v~øeèi matematické, chceme pro zadanou mno¾inu bodù v~rovinì nalézt její konvexní obal. Co je to konvexní obal? Mno¾ina bodù je {\I konvexní}, -pokud pro ka¾dé dva body obsahuje i celou úseèku mezi nimi. {\I Konvexní obal} je nejmen¹í konvexní podmno¾ina roviny, která obsahuje v¹echny zadané -body.\foot{Pamatujete si na lineární obaly ve vektorových prostorech? Lineární obal mno¾iny vektorù je nejmen¹í vektorový podprostor, který tyto -vektory obsahuje. Není náhoda, ¾e tato definice pøipomíná definici konvexního obalu. Na druhou stranu ka¾dý vektor z~lineárního obalu lze vyjádøit -jako lineární kombinaci daných vektorù. Podobnì platí i pro konvexní obaly, ¾e ka¾dý bod z~obalu je konvexní kombinací daných bodù. Ta se li¹í od -lineární v~tom, ¾e v¹echny koeficienty jsou v~intervalu $[0,1]$ a navíc souèet v¹ech koeficientù je $1$. Tento algebraický pohled mù¾e mnohé vìci -zjednodu¹it. Zkuste si dokázat, ¾e obì definice konvexního obalu jsou ekvivalentní.} Z~algoritmického hlediska nás v¹ak bude zajímat jenom jeho -hranice, kterou budeme dále oznaèovat jako konvexní obal. - -Na¹ím úkolem je nalézt konvexní obal koneèné mno¾iny bodù. To je v¾dy konvexní mnohoúhelník, navíc s~vrcholy v~zadaných bodech. Øe¹ením problému tedy -bude posloupnost bodù, které tvoøí konvexní obal. Pro malé mno¾iny je konvexní obal nakreslen na obrázku, pro více bodù je v¹ak situace mnohem -slo¾itìj¹í. - -\figure{7-geom1_male_obaly.eps}{Konvexní obaly malých mno¾in.}{3in} - -Pro jednoduchost budeme pøedpokládat, ¾e v¹echny body mají rùzné $x$-ové souøadnice. Tedy utøídìní bodù zleva doprava je urèené jednoznaènì.\foot{To si -mù¾eme dovolit pøedpokládat, nebo» se v¹emi body staèí nepatrnì pootoèit. Tím konvexní obal urèitì nezmìníme. Av¹ak jednodu¹¹í øe¹ení je naprogramovat -tøídìní lexikograficky (druhotnì podle souøadnice $y$) a vyøadit identické body.} Tím máme zaji¹tìné, ¾e existují dva body, nejlevìj¹í a -nejpravìj¹í, pro které platí následující invariant: - -\s{Invariant:} Nejlevìj¹í a nejpravìj¹í body jsou v¾dy v~konvexním obalu. - -Algoritmus na nalezení konvexního obalu funguje na následujícím jednoduchém principu, kterému se nìkdy øíká {\I zametání roviny}. Procházíme body -zleva doprava a postupnì roz¹iøujeme doposud nalezený konvexní obal o~dal¹í body. Na zaèátku bude konvexní obal jediného bodu samotný bod. Na konci -$k$-tého kroku algoritmu známe konvexní obal prvních $k$ bodù. Kdy¾ algoritmus skonèí, známe hledaný konvexní obal. Podle invariantu musíme v~$k$-tém -kroku pøidat do obalu $k$-tý nejlevìj¹í bod. Zbývá si jen rozmyslet, jak pøesnì tento bod pøidat. - -Pøidání dal¹ího bodu do konvexního obalu funguje, jak je naznaèeno na obrázku. Podle invariantu víme, ¾e bod nejvíc vpravo je souèástí konvexního -obalu. Za nìj napojíme novì pøidávaný bod. Tím jsme získali nìjaký obal, ale zpravidla nebude konvexní. To lze v¹ak snadno napravit, staèí -odebírat body, v obou smìrech podél konvexního obalu, tak dlouho, dokud nezískáme konvexní obal. Na pøíkladu z obrázku nemusíme po smìru hodinových -ruèièek odebrat ani jeden bod, obal je v poøádku. Naopak proti smìru hodinových ruèièek musíme odebrat dokonce dva body. - -\figure{7-geom2_pridani_bodu.eps}{Pøidání bodu do konvexního obalu.}{4.5in} - -Pro pøípadnou implementaci a rozbor slo¾itosti si nyní popí¹eme algoritmus detailnìji. Aby se lépe popisoval, rozdìlíme si konvexní obal na dvì èásti -spojující nejlevìj¹í a nejpravìj¹í bod obalu. Budeme jim øíkat {\I horní obálka} a {\I dolní obálka}. - -\figure{7-geom3_obalky.eps}{Horní a dolní obálka konvexního obalu.}{3.4in} - -Obì obálky jsou lomené èáry, navíc horní obálka poøád zatáèí doprava a dolní naopak doleva. Pro udr¾ování bodù v~obálkách staèí dva zásobníky. -V~$k$-tém kroku algoritmu pøidáme zvlá¹» $k$-tý bod do horní i dolní obálky. Pøidáním $k$-tého bodu se v¹ak mù¾e poru¹it smìr, ve kterém obálka -zatáèí. Proto budeme nejprve body z~obálky odebírat a $k$-tý bod pøidáme a¾ ve chvíli, kdy jeho pøidání smìr zatáèení neporu¹í. - -\s{Algoritmus:} - -\algo - -\:Setøídíme body podle $x$-ové souøadnice, oznaème body $b_1, \ldots, b_n$. -\:Vlo¾íme do horní a dolní obálky bod $b_1$: $H = D = (b_1)$. -\:Pro ka¾dý dal¹í bod $b = b_2,\ldots,b_n$: -\::Pøepoèítáme horní obálku: -\:::Dokud $\vert H\vert \ge 2$, $H = (\ldots, h_{k-1}, h_k)$ a úhel $h_{k-1} h_k b$ je orientovaný doleva: -\::::Odebereme poslední bod $h_k$ z~obálky $H$. -\:::Pøidáme bod $b$ do obálky $H$. -\::Symetricky pøepoèteme dolní obálku (s orientací doprava). -\: Výsledný obal je tvoøen body v~obálkách $H$ a $D$. - -\endalgo - -Rozebereme si èasovou slo¾itost algoritmu. Setøídit body podle $x$-ové souøadnice doká¾eme v~èase $\O(n \log n)$. Pøidání dal¹ího bodu do obálek -trvá lineárnì vzhledem k~poètu odebraných bodù. Zde vyu¾ijeme obvyklý postup: Ka¾dý bod je odebrán nejvý¹e jednou, a tedy v¹echna odebrání trvají -dohromady $\O(n)$. Konvexní obal doká¾eme sestrojit v~èase $\O(n \log n)$, a pokud bychom mìli seznam bodù ji¾ utøídený, doká¾eme to dokonce v -$\O(n)$. - -\s{Algebraický dodatek:} Existuje jednoduchý postup, jak zjistit orientaci úhlu? Uká¾eme si jeden zalo¾ený na lineární algebøe. Budou se hodit -vlastnosti determinantu. Absolutní hodnota determinantu je objem rovnobì¾nostìnu urèeného øádkovými vektory matice. Dùle¾itìj¹í v¹ak je, ¾e znaménko -determinantu urèuje \uv{orientaci} vektorù, zda je levotoèivá èi pravotoèivá. Proto¾e ná¹ problém je rovinný, budeme uva¾ovat determinanty matic $2 -\times 2$. - -Uva¾me souøadnicový systém v~rovinì, kde $x$-ová souøadnice roste smìrem doprava a~$y$-ová smìrem nahoru. Chceme zjistit orientaci úhlu $h_{k-1} h_k -b$. Polo¾me $\vec u = (x_1, y_1)$ jako rozdíl souøadnic $h_k$ a~$h_{k-1}$ a podobnì $\vec v = (x_2, y_2)$ je rozdíl souøadnic $b$ a~$h_k$. Matice $M$ -je definována následovnì: -$$M = \pmatrix{\vec u \cr \vec v} = \pmatrix {x_1&y_1\cr x_2&y_2}.$$ -Úhel $h_{k-1} h_k b$ je orientován doleva, právì kdy¾ $\det M = x_1y_2 - x_2y_1$ je nezáporný,\foot{Neboli vektory $\vec u$ a $\vec v$ odpovídají -rozta¾ení a zkosení vektorù báze $\vec x = (1,0)$ a $\vec y = (0,1)$, pro nì¾ je determinant nezáporný.} a spoèítat hodnotu determinantu je jednoduché. -Mo¾né situace jsou nakresleny na obrázku. Poznamenejme, ¾e k~podobnému vzorci se lze také dostat pøes vektorový souèin vektorù $\vec u$ a $\vec v$. - -\figure{7-geom4_determinant.eps}{Jak vypadají determinanty rùzných znamének v~rovinì.}{4.6in} - -\s{©lo by to vyøe¹it rychleji?} Také vám vrtá hlavou, zda existují rychlej¹í algoritmy? Na závìr si uká¾eme nìco, co na pøedná¹ce nebylo.\foot{A také -se nebude zkou¹et.} Nejrychlej¹í známý algoritmus, jeho¾ autorem je T.~Chan, funguje v~èase $\O(n \log h)$, kde $h$ je poèet bodù le¾ících na -konvexním obalu, a pøitom je pøekvapivì jednoduchý. Zde si naznaèíme, jak tento algoritmus funguje. - -Algoritmus pøichází s~následující my¹lenkou. Pøedpokládejme, ¾e bychom znali velikost konvexního obalu $h$. Rozdìlíme body libovolnì do $\lceil {n -\over h} \rceil$ mno¾in $Q_1, \ldots, Q_k$ tak, ¾e $\vert Q_i \vert \le h$. Pro ka¾dou z~tìchto mno¾in nalezneme konvexní obal pomocí vý¹e popsaného -algoritmu. To doká¾eme pro jednu v~èase $\O(h \log h)$ a pro v¹echny v~èase $\O(n \log h)$. V druhé fázi spustíme hledání konvexního obalu pomocí -provázkového algoritmu a pro zrychlení pou¾ijeme pøedpoèítané obaly men¹ích mno¾in. Nejprve popí¹eme jeho my¹lenku. Pou¾ijeme následující pozorování: - -\s{Pozorování:} Úseèka spojující dva body $a$ a $b$ le¾í na konvexním obalu, právì kdy¾ v¹echny ostatní body le¾í pouze na jedné její -stranì.\foot{Formálnì je podmínka následující: Pøímka $ab$ urèuje dvì poloroviny. Úseèka le¾í na konvexním obalu, právì kdy¾ v¹echny body le¾í v jedné -z polorovin.} - -Algoritmu se øíká {\I provázkový}, proto¾e svojí èinností pøipomíná namotávání provázku podél konvexního obalu. Zaèneme s bodem, který na konvexním -obalu urèitì le¾í, to je tøeba ten nejlevìj¹í. V ka¾dém kroku nalezneme následující bod po obvodu konvexního obalu. To udìláme napøíklad tak, ¾e -projdeme v¹echny body a vybereme ten, který svírá nejmen¹í úhel s poslední stranou konvexního obalu. Novì pøidaná úseèka vyhovuje pozorování a proto -do konvexního obalu patøí. Po $h$ krocích se dostaneme zpìt k nejlevìj¹ímu bodu a výpoèet ukonèíme. V ka¾dém kroku potøebujeme projít v¹echny body a -vybrat následníka, co¾ doká¾eme v èase $\O(n)$. Celková slo¾itost algoritmu je tedy $\O(n \cdot h)$. - -\twofigures{7-geom6_provazkovy_algoritmus.eps}{Provázkový algoritmus.}{1.25in}{7-geom7_naslednik_pres_konvexni_obal.eps}{Hledání kandidáta v pøedpoèítaném obalu.}{2.5in} - -Provázkový algoritmus funguje, ale má jednu obrovskou nevýhodu -- je toti¾ ukrutnì pomalý. Ký¾eného zrychlení dosáhneme, pokud pou¾ijeme pøedpoèítané -konvexní obaly. Ty umo¾ní rychleji hledat následníka. Pro ka¾dou z mno¾in $Q_i$ najdeme zvlá¹» kandidáta a poté z nich vybereme toho nejlep¹ího. -Mo¾ný kandidát v¾dy le¾í na konvexním obalu mno¾iny $Q_i$. Vyu¾ijeme toho, ¾e body obalu jsou \uv{uspoøádané}, i kdy¾ trochu netypicky do kruhu. -Kandidáta mù¾eme hledat metodou pùlení intervalu, i kdy¾ detaily jsou malièko slo¾itìj¹í ne¾ je obvyklé. Jak pùlit zjistíme podle smìru zatáèení -konvexního obalu. Detaily si rozmyslí ètenáø sám. - -Èasová slo¾itost pùlení je $\O(\log h)$ pro jednu mno¾inu. Mno¾in je nejvý¹e $\O({n \over h})$, tedy následující bod konvexního obalu nalezneme v èase -$\O({n \over h} \log h)$. Celý obal nalezneme ve slibovaném èase $\O(n \log h)$. - -Popsanému algoritmu schází jedna dùle¾itá vìc: Ve skuteènosti vìt¹inou neznáme velikost $h$. Budeme proto algoritmus iterovat s~rostoucí hodnotou $h$, -dokud konvexní obal nesestrojíme. Pokud pøi slepování konvexních obalù zjistíme, ¾e konvexní obal je vìt¹í ne¾ $h$, výpoèet ukonèíme. Zbývá je¹tì -zvolit, jak rychle má $h$ rùst. Pokud by rostlo moc pomalu, budeme poèítat zbyteènì mnoho fází, naopak pøi rychlém rùstu by nás poslední fáze mohla -stát pøíli¹ mnoho. - -V~$k$-té iteraci polo¾íme $h = 2^{2^k}$. Dostáváme celkovou slo¾itost algoritmu: -$$\sum_{m=0}^{\O(\log \log h)} \O(n \log 2^{2^m}) = \sum_{m=0}^{\O(\log \log h)} \O(n \cdot 2^m) = \O(n \log h),$$ -kde poslední rovnost dostaneme jako souèet prvních $\O(\log \log h)$ èlenù geometrické øady $\sum 2^m$. - -\bye diff --git a/7-geom/7-geom1_male_obaly.eps b/7-geom/7-geom1_male_obaly.eps deleted file mode 100644 index 94a501d..0000000 --- a/7-geom/7-geom1_male_obaly.eps +++ /dev/null @@ -1,324 +0,0 @@ -%!PS -%%BoundingBox: -13 -35 216 12 -%%HiResBoundingBox: -12.12236 -34.76685 215.33812 11.24376 -%%Creator: MetaPost 0.993 -%%CreationDate: 2009.11.17:1821 -%%Pages: 1 -%*Font: cmmi10 9.96265 9.96265 6e:8 -%*Font: cmr10 9.96265 9.96265 31:f008 -%%BeginProlog -%%EndProlog -%%Page: 1 1 - 1 1 1 setrgbcolor -newpath 1.99252 0 moveto -1.99252 0.52847 1.78256 1.03523 1.4089 1.4089 curveto -1.03523 1.78256 0.52847 1.99252 0 1.99252 curveto --0.52847 1.99252 -1.03523 1.78256 -1.4089 1.4089 curveto --1.78256 1.03523 -1.99252 0.52847 -1.99252 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--git a/7-geom/lib.mp b/7-geom/lib.mp deleted file mode 100644 index 2536305..0000000 --- a/7-geom/lib.mp +++ /dev/null @@ -1,82 +0,0 @@ -% implementation of figure naming and figure transparency -string name,tag; name := ""; tag := ""; -def updatefigname = - if tag="": filenametemplate (name & "%c.eps"); - else: filenametemplate (name & "%c_" & tag & ".eps"); - fi; -enddef; - -def figname(expr n) = name := n; updatefigname; enddef; -def figtag(expr t) = tag := t; updatefigname; enddef; - -picture transparent_picture; -color transparent_color; transparent_color := 0.9white; -def drawtransparent(expr num) = - transparent_picture := currentpicture; -endfig; - -if tag="": filenametemplate (name & "%c_transparent.eps"); -else: filenametemplate (name & "%c_" & tag & "_transparent.eps"); -fi; -beginfig(num); - draw transparent_picture withcolor transparent_color; -endfig; - -updatefigname; -enddef; - -def from(expr p,d,len) = - p+dir(d)*len -enddef; - -def dirs(expr p,d,len) = - p--from(p,d,len) -enddef; - -def drawvertices(expr s,n) = - for i:=s upto n: - draw vertex(PQ[i]); - endfor -enddef; - -def drawfvertices(expr s,n,flags) = - for i:=s upto n: - draw vertex(PQ[i]) flags; - endfor -enddef; - -def vertex(expr p) = p withpen pencircle scaled 4pt enddef; -def drawemptyvertex(expr p) = unfill fullcircle scaled 4pt shifted p; draw fullcircle scaled 4pt shifted p; enddef; -def drawendpointvertex(expr p) = draw vertex(p) withcolor red; draw fullcircle scaled 6pt shifted p; enddef; -def createpath(expr p) = shakepath(p, 0.015cm,0.1cm) enddef; -vardef shakepath(expr p,d,l) = - save r,b; - path r; r := point(arctime 0 of p) of p; - b := -1; - for i:=l step l until arclength(p): - r := r--(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*d*b; - b := -b; - endfor - r--point(arctime arclength(p) of p) of p -enddef; - -pen normalpen; normalpen := pencircle scaled 0.6pt; -pen boldpen; boldpen := pencircle scaled 1.5pt; -pen bolderpen; bolderpen := pencircle scaled 2pt; -def dotline = withdots scaled 0.82 withpen boldpen enddef; - -vardef unclosedbubblec(expr p,c) = - bubblec((p..reverse p..cycle),c) -enddef; - -vardef bubblec(expr p,c) = - save r; - path r; r := (point(arctime 0 of p) of p)+dir(angle(direction(arctime 0 of p) of p rotated 90))*c; - for i:=0.01cm step 0.025cm until arclength(p): - r := r..(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*c; - endfor - r..(point(arctime arclength(p) of p) of p)+dir(angle(direction(arctime arclength(p) of p) of p rotated 90))*c..cycle -enddef; - -vardef bubble(expr p) = bubblec(p,0.12cm) enddef; -vardef unclosedbubble(expr p) = unclosedbubblec(p,0.12cm) enddef; diff --git a/8-geom2/8-geom2.mp b/8-geom2/8-geom2.mp deleted file mode 100644 index 5a44396..0000000 --- a/8-geom2/8-geom2.mp +++ /dev/null @@ -1,137 +0,0 @@ -input lib - -figname("8-geom2_"); -figtag("usecky"); -beginfig(1); - def drawusecka(expr p,q) = draw vertex(p); draw vertex(q); draw p--q; enddef; - pair A[],B[]; - A0 := origin; B0 := (4cm,1.5cm); - A1 := (0.5cm,1.5cm); B1 := (1cm,0); - A2 := (2cm,1.3cm); B2 := (3cm, -0.9cm); - A3 := (1cm,-1.3cm); B3 := (3.5cm,0); - z0 = whatever[A0,B0]; z0 = whatever[A1,B1]; - z1 = whatever[A0,B0]; z1 = whatever[A2,B2]; - z2 = whatever[A2,B2]; z2 = whatever[A3,B3]; - - for i:=0 upto 2: fill fullcircle scaled 7pt shifted z[i]; drawemptyvertex(z[i]); endfor - for i:=0 upto 3: drawusecka(A[i], B[i]); endfor -endfig; - -figtag("polorovina"); -beginfig(2); - pair A,B; - A := origin; - B := (1.7cm,0.5cm); - an := angle(B-A); - path p; p := from(.5[A,B],an+90,3cm)--from(.5[A,B],an-90,3cm); - fill p--reverse(p shifted (A-0.8[A,B]))--cycle withcolor 0.8white; - %fill p{dir (an+180)}..-0.35[A,B]..{dir an}cycle withcolor 0.8white; - draw p withpen boldpen; - - pair C; C := point(0.9) of p; - drawarrow from(C,an+180,0.1cm)--from(C,an+180,1cm) withpen boldpen; - C := point(0.1) of p; - drawarrow from(C,an+180,0.1cm)--from(C,an+180,1cm) withpen boldpen; - label.rt(btex $p$ etex, point(0.15) of p); - - draw A--B dashed evenly; - drawemptyvertex(A); drawemptyvertex(B); - label.llft(btex $a$ etex, A); - label.urt(btex $b$ etex, B); - label(btex $B_a$ etex, A+2(-0.1cm,0.5cm)); - label(btex $B_b$ etex, B+2(-0.1cm,0.5cm)); -endfig; - -figtag("voroneho_diagram"); -beginfig(3); - u := 1.35cm; - pair A[],B[]; - A0 := origin; A1 := (-u,-u); A2 := (1.3u,-u); A3 := (1.50821u,0.7u); A4 := (0.6u,0.8u); A5 := (0.2u,1.9u); A6 := (-1.3u,1u); - def osa(expr a, b, an,l) = from(.5[a,b], angle(b-a)+an, l) enddef; - vardef prusecik_os(expr p,q,r) = - save b; pair b; - b = whatever[osa(p,q, 90,1cm), osa(p,q,-90,1cm)]; - b = whatever[osa(q,r, 90,1cm), osa(q,r,-90,1cm)]; - b - enddef; - B[0] := prusecik_os(A0,A1,A2); B[1] := prusecik_os(A0,A1,A6); B[2] := prusecik_os(A0,A4,A6); B[3] := prusecik_os(A4,A5,A6); - B[4] := prusecik_os(A3,A4,A5); B[5] := prusecik_os(A0,A3,A4); B[6] := prusecik_os(A0,A2,A4); - draw osa(A1,A2,-90,2cm)--B[0]--B[1]--osa(A1,A6,90,1.3cm) withpen boldpen; - draw B[1]--B[2]--B[3]--osa(A6,A5,90,2cm) withpen boldpen; - draw B[3]--B[4]--osa(A3,A5,-90,2.7cm) withpen boldpen; - draw B[4]--B[5]--B[2] withpen boldpen; - draw B[5]--B[6]--osa(A2,A3,-90,1.3cm) withpen boldpen; - draw B[6]--B[0] withpen boldpen; - - draw A0--A1--A2--A0--A6--A1 dashed evenly; - draw A6--A5--A4--A0 dashed evenly; draw A3--A2 dashed evenly; - draw A3--A5 dashed evenly; draw A6--A4--A3 dashed evenly; - - for i:=0 upto 6: draw vertex(B[i]); endfor - for i:=0 upto 6: drawemptyvertex(A[i]); endfor -endfig; - -figtag("pasy_mnohouhelniku"); -beginfig(4); - u := 1.35cm; - pair A[],B[]; - A0 := origin; A1 := (-u,-u); A2 := (1.3u,-u); A3 := (1.50821u,0.7u); A4 := (0.6u,0.8u); A5 := (0.2u,1.9u); A6 := (-1.3u,1u); - def osa(expr a, b, an,l) = from(.5[a,b], angle(b-a)+an, l) enddef; - vardef prusecik_os(expr p,q,r) = - save b; pair b; - b = whatever[osa(p,q, 90,1cm), osa(p,q,-90,1cm)]; - b = whatever[osa(q,r, 90,1cm), osa(q,r,-90,1cm)]; - b - enddef; - def drawline(expr p) = draw ((-2.3u,p)--(2.5u,p)) cutbefore (D0--D1) cutafter (D2--D3) enddef; - B[0] := prusecik_os(A0,A1,A2); B[1] := prusecik_os(A0,A1,A6); B[2] := prusecik_os(A0,A4,A6); B[3] := prusecik_os(A4,A5,A6); - B[4] := prusecik_os(A3,A4,A5); B[5] := prusecik_os(A0,A3,A4); B[6] := prusecik_os(A0,A2,A4); - - pair C; C := origin; for i:=0 upto 6: C := C + B[i]; endfor C := C/6; - pair D[]; - D0 := C+(-2.25,2.25)*u; D1 := C+(-2.25,-2.25)*u; D2 := C+(2.25,-2.25)*u; D3 := C+(2.25,2.25)*u; - - draw B[0]--B[1]--B[2]--B[3]--B[4]--B[5]--B[6]--B[0] withpen boldpen; - draw B[5]--B[2] withpen boldpen; - pair E; E := 0.6[B0,B4]; - drawline(ypart(E)); - drawline(ypart(E)) cutbefore (B2--B5) cutafter (B4--B5) dashed evenly withpen bolderpen; - drawemptyvertex(E); - - for i:=0 upto 6: drawline(ypart(B[i])) dashed evenly; endfor - for i:=0 upto 6: draw vertex(B[i]); endfor -endfig; - -figtag("upravy_stromu"); -beginfig(5); - u := 1cm; - draw (0,0.1u)--(2.05u,-2.05u)--(-2.05u,-2.05u)--cycle; - pair A[]; A0 := from(origin,-90,0.1u); A1 := from(A0, -70, 0.5u); A2 := from(A1, -110, 0.5u); A3 := from(A2, -80, 0.5u); A4 := from(A3, -120, 0.45u); - path p; p := createpath(A0--A1--A2--A3--A4); - path q; q := (p scaled 0.93 shifted (-0.15u,-0.15u))--(-1.85u,-1.95u)--cycle; fill q withcolor 0.8white; draw q; - q := (p scaled 0.93 shifted (0.15u,-0.15u))--(1.85u,-1.95u)--cycle; fill q withcolor 0.8white; draw q; - p := createpath(A0--from(A0,-110,0.1u)--A1--A2--A3--A4); draw p withpen boldpen; -endfig; - -figtag("rychla_perzistence"); -beginfig(6); - u := 1cm; - def drawtable(expr p, lab, sa, sb) = - draw centersquare xscaled 2u yscaled (2u/3) shifted (p+(0,u/3)); - draw centersquare xscaled 2u yscaled (2u/3) shifted (p-(0,u/3)); - label(sa, p+(0,u/3)); - label(sb, p-(0,u/3)); - label.bot(lab, p-(0,2u/3)); - enddef; - - pair A[]; - for i:=0 upto 2: A[i] := (0,2u) rotated 120i; endfor - drawtable(A1, btex $v$ etex, btex verze 2 etex, btex verze 1 etex); - drawtable(A2, btex $v'$ etex, btex verze 3 etex, ""); - drawtable(A0, btex $u$ etex, btex verze 2 etex, btex verze 1 etex); - - drawarrow from(A[2]+(0,u/3), 180, 7u/6)--from(A[1]+(0,u/3), 0, 7u/6); - drawarrow from(A[0]+(0,-u/3), 180, 7u/6)..{dir -90}from(A[1]+(-3u/4,2u/3), 90, u/6); - drawarrow from(A[0]+(0,u/3), 0, 7u/6)..{dir -90}from(A[2]+(3u/4,2u/3), 90, u/6); -endfig; -end diff --git a/8-geom2/8-geom2.tex b/8-geom2/8-geom2.tex deleted file mode 100644 index 034b395..0000000 --- a/8-geom2/8-geom2.tex +++ /dev/null @@ -1,167 +0,0 @@ -\input lecnotes.tex - -\prednaska{8}{Geometrie vrací úder}{(sepsal Pavel Klavík)} - -\>Kdy¾ s geometrickými problémy poøádnì nezametete, ony vám to vrátí! Ale kdy¾ u¾ zametat, tak urèitì ne pod koberec a místo smetáku pou¾ijte pøímku. -V této pøedná¹ce nás spolu s dvìma geometrickými problémy samozøejmì èeká pokraèování pohádky o ledních medvìdech. - -{\I Medvìdi vyøe¹ili rybí problém a hlad je ji¾ netrápí. Av¹ak na severu ne¾ijí sami, za sousedy mají Eskymáky. Proto¾e je rozhodnì lep¹í se sousedy -dobøe vycházet, jsou medvìdi a Eskymáci velcí pøátelé. Skoro ka¾dý se se svými pøáteli rád schází. Av¹ak to je musí nejprve nalézt~\dots} - -\h{Hledání prùseèíkù úseèek} - -Zkusíme nejprve Eskymákùm vyøe¹it lokalizaci ledních medvìdù. - -{\I Kdy¾ takový medvìd nemá co na práci, rád se prochází. Na místech, kde se trasy protínají, je zvý¹ená ¹ance, ¾e se dva medvìdi potkají a zapovídají --- ostatnì co byste èekali od medvìdù. To jsou ta správná místa pro Eskymáka, který chce potkat medvìda. Jenom¾e jak tato køí¾ení najít?} - -Pro zjednodu¹ení pøedpokládejme, ¾e medvìdi chodí po úseèkách tam a zpìt. Budeme tedy chtít nalézt v¹echny prùseèíky úseèek v rovinì. - -\bigskip -\centerline{\epsfxsize=1.5in\epsfbox{8-geom2_0_bear.eps}\hskip 4em\epsfxsize2in\epsfbox{8-geom2_1_usecky.eps}} -\smallskip -\centerline{Problém Eskymákù: Kde v¹ude se køí¾í medvìdí trasy?} -\bigskip - -Pro $n$ úseèek mù¾e existovat a¾ $\Omega(n^2)$ prùseèíkù.\foot{Zkuste takový pøíklad zkonstruovat.} Tedy optimální slo¾itosti by dosáhl i algoritmus, -který by pro ka¾dou dvojici úseèek testoval, zda se protínají. Èasovou slo¾itost algoritmu v¹ak posuzujeme i vzhledem k velikosti výstupu $p$. Typické -rozmístìní úseèek mívá toti¾ prùseèíkù spí¹e pomálu. Pro tento pøípad si uká¾eme podstatnì rychlej¹í algoritmus. - -Pro jednodu¹¹í popis pøedpokládejme, ¾e úseèky le¾í v obecné poloze. To znamená, ¾e ¾ádné tøi úseèky se neprotínají v jednom bodì a prùnikem ka¾dých -dvou úseèek je nejvý¹e jeden bod. Navíc pøedpokládejme, ¾e krajní bod ¾ádné úseèky nele¾í na jiné úseèce a také neexistují vodorovné úseèky. Na závìr si -uká¾eme, jak se s tìmito pøípady vypoøádat. - -Algoritmus funguje na principu zametání roviny, popsaném v minulé pøedná¹ce. Budeme posouvat vodorovnou pøímku odshora dolù. V¾dy, kdy¾ narazíme na -nový prùnik, ohlásíme jeho výskyt. Samozøejmì spojité posouvání nahradíme diskrétním a pøímku v¾dy posuneme do dal¹ího zajímavého bodu. - -Zajímavé události jsou {\I zaèátky úseèek}, {\I konce úseèek} a {\I prùseèíky úseèek}. Po utøídìní známe pro první dva typy událostí poøadí, v jakém -se objeví. Výskyty prùseèíkù budeme poèítat prùbì¾nì, jinak bychom celý problém nemuseli øe¹it. - -V ka¾dém kroku si pamatujeme {\I prùøez} $P$ -- posloupnost úseèek aktuálnì protnutých zametací pøímkou. Tyto úseèky máme utøídìné zleva doprava. Navíc si -udr¾ujeme kalendáø $K$ budoucích událostí. Z hlediska prùseèíkù budeme na úseèky nahlí¾et jako na polopøímky. Pro sousední dvojice úseèek si -udr¾ujeme, zda se jejich smìry nìkde protnou. Algoritmus pro hledání prùnikù úseèek funguje následovnì: - -\s{Algoritmus:} - -\algo - -\:$P \leftarrow \emptyset$. -\:Do $K$ vlo¾íme zaèátky a konce v¹ech úseèek. -\:Dokud $K \ne \emptyset$: -\::Odebereme nejvy¹¹í událost. -\::Pokud je to zaèátek úseèky, zatøídíme novou úseèku do $P$. -\::Pokud je to konec úseèky, odebereme úseèku z $P$. -\::Pokud je to prùseèík, nahlásíme ho a prohodíme úseèky v $P$. -\::Navíc v¾dy pøepoèítáme prùseèíkové události, v¾dy maximálnì dvì odebereme a dvì nové pøidáme. -\endalgo - -Zbývá rozmyslet si, jaké datové struktury pou¾ijeme, abychom prùseèíky nalezli dostateènì rychle. Pro kalendáø pou¾ijeme napøíklad haldu. Prùøez si -budeme udr¾ovat ve vyhledávacím stromì. Poznamenejme, ¾e nemusíme znát souøadnice úseèek, staèí znát jejich poøadí, které se mezi jednotlivými -událostmi nemìní. Pøi pøidávání úseèek procházíme stromem a porovnáváme souøadnice v prùøezu, které prùbì¾nì dopoèítáváme. - -Kalendáø obsahuje v¾dy nejvý¹e $\O(n)$ událostí. Podobnì prùøez obsahuje v ka¾dém okam¾iku nejvý¹e $\O(n)$ úseèek. Jednu událost kalendáøe doká¾eme -o¹etøit v èase $\O(\log n)$. V¹ech událostí je $\O(n+p)$, a tedy celková slo¾itost algoritmu je $\O((n+p) \log n)$. - -Slíbili jsme, ¾e popí¹eme, jak se vypoøádat s vý¹e uvedenými podmínkami na vstup. Události kalendáøe se stejnou $y$-ovou souøadnicí budeme tøídit v -poøadí zaèátky, prùseèíky a konce úseèek. Tím nahlásíme i prùseèíky krajù úseèek a ani vodorovné úseèky nebudou vadit. Podobnì se není tøeba obávat -prùseèíkù více úseèek v jednom bodì. Úseèky jdoucí stejným smìrem, jejich¾ prùnik je úseèka, jsou komplikovanìj¹í, ale lze jejich prùseèíky o¹etøit a -vypsat tøeba souøadnice úseèky tvoøící jejich prùnik. - -Na závìr poznamenejme, ¾e Balaban vymyslel efektivnìj¹í algoritmus, který funguje v èase $\O(n \log n + p)$, ale je podstatnì komplikovanìj¹í. - -\h{Hledání nejbli¾¹ích bodù a Voroného diagramy} - -Nyní se pokusíme vyøe¹it i problém druhé strany -- pomù¾eme medvìdùm nalézt Eskymáky. - -{\I Eskymáci tráví vìt¹inu èasu doma, ve svém iglù. Takový medvìd je na své toulce zasnì¾enou krajinou, kdy¾ tu se najednou rozhodne nav¹tívit nìjakého -Eskymáka. Proto se podívá do své medvìdí mapy a nalezne nejbli¾¹í iglù. Má to ale jeden háèek, iglù jsou spousty a medvìd by dávno usnul, ne¾ by -nejbli¾¹í objevil.}\foot{Zlí jazykové by øekli, ¾e medvìdi jsou moc líní a nebo v mapách ani èíst neumí!} - -Popí¹eme si nejprve, jak vypadá medvìdí mapa. Medvìdí mapa obsahuje celou Arktidu a jsou v ní vyznaèena v¹echna iglù. Navíc obsahuje vyznaèené -oblasti tvoøené body, které jsou nejblí¾e k jednomu danému iglù. Takovému schématu se øíká {\I Voroného diagram}. Ten pro zadané body $x_1, \ldots, x_n$ -obsahuje rozdìlení roviny na oblasti $B_1, \ldots, B_n$, kde $B_i$ je mno¾ina bodù, které jsou blí¾e k $x_i$ ne¾ k ostatním bodùm $x_j$. Formálnì jsou -tyto oblasti definovány následovnì: -$$B_i = \left\{y \in {\bb R}^2\ \vert\ \forall j:\rho(x_i,y) \le \rho(x_j,y)\right\},$$ -kde $\rho(x,y)$ znaèí vzdálenost bodù $x$ a $y$. - -Uká¾eme si, ¾e Voroného diagram má pøekvapivì jednoduchou strukturu. Nejprve uva¾me, jak budou vypadat oblasti $B_a$ a $B_b$ pouze pro dva body -$a$ a $b$, jak je naznaèeno na obrázku. V¹echny body stejnì vzdálené od $a$ i $b$ le¾í na pøímce $p$ -- ose úseèky $ab$. Oblasti $B_a$ a $B_b$ -jsou tedy tvoøeny polorovinami ohranièenými osou $p$. Tedy obecnì tvoøí mno¾ina v¹ech bodù bli¾¹ích k $x_i$ ne¾ k $x_j$ nìjakou polorovinu. Oblast -$B_i$ obsahuje v¹echny body, které jsou souèasnì bli¾¹í k $x_i$ ne¾ ke v¹em ostatním bodùm $x_j$ -- tedy le¾í ve v¹ech polorovinách souèasnì. -Ka¾dá z oblastí $B_i$ je tvoøena prùnikem $n-1$ polorovin, tedy je to (mo¾ná neomezený) mnohoúhelník.\foot{Sly¹eli jste u¾ o lineárním programování? -Jak název vùbec nenapoví, {\I lineární programování} je teorii zabývající se øe¹ením a vlastnostmi soustav lineárních nerovnic. Lineární program je -popsaný lineární funkcí, kterou chceme maximalizovat za podmínek popsaných soustavou lineárních nerovnic. Ka¾dá nerovnice urèuje poloprostor, ve -kterém se pøípustná øe¹ení nachází. Proto¾e pøípustné øe¹ení splòuje v¹echny nerovnice zároveò, je mno¾ina v¹ech pøípustných øe¹ení (mo¾ná neomezený) -mnohostìn, obecnì ve veliké dimenzi ${\bb R}^d$, kde $d$ je poèet promìnných. Mno¾iny $B_i$ lze snadno popsat jako mno¾iny v¹ech pøípustných øe¹ení -lineárních programù pomocí vý¹e ukázaných polorovin. Na závìr poznamenejme, ¾e dlouho otevøená otázka, zda lze nalézt optimální øe¹ení lineárního -programu v polynomiálním èase, byla pozitivnì vyøe¹ena -- je znám polynomiální algoritmus, kterému se øíká {\I metoda vnitøního bodu}. Na druhou -stranu, pokud chceme najít pøípustné celoèíselné øe¹ení, je úloha NP-úplná a je jednoduché na ni pøevést spoustu optimalizaèních problémù. Dokázat -NP-tì¾kost není pøíli¹ tì¾ké. Na druhou stranu ukázat, ¾e tento problém le¾í v NP, není vùbec jednoduché.} -Pøíklad Voroného diagramu je naznaèen na obrázku. Zadané body jsou oznaèeny prázdnými krou¾ky a hranice oblastí $B_i$ jsou vyznaèené èernými èárami. - -\twofigures{8-geom2_2_polorovina.eps}{Body bli¾¹í k $a$ ne¾ $b$.}{1.25in}{8-geom2_3_voroneho_diagram.eps}{Voroného diagram.}{2.5in} - -Není náhoda, pokud vám hranice oblastí pøipomíná rovinný graf. Jeho vrcholy jsou body, které jsou stejnì vzdálené od alespoò tøí zadaných bodù. Jeho -stìny jsou oblasti $B_i$. Jeho hrany jsou tvoøeny èástí hranice mezi dvìma oblastmi -- body, které mají dvì oblasti spoleèné. Obecnì prùnik dvou -oblastí mù¾e být, v závislosti na jejich sousedìní, prázdný, bod, úseèka, polopøímka nebo dokonce celá pøímka. V dal¹ím textu si pøedstavme, ¾e celý -Voroného diagram uzavøeme do dostateènì velkého obdélníka,\foot{Pøeci jenom i celá Arktida je omezenì velká.} èím¾ dostaneme omezený rovinný graf. - -Poznamenejme, ¾e pøeru¹ované èáry tvoøí hrany duálního rovinného grafu s vrcholy v zadaných bodech. Hrany spojují sousední body na kru¾nicích, které -obsahují alespoò tøi ze zadaných bodù. Napøíklad na obrázku dostáváme skoro samé trojúhelníky, proto¾e vìt¹ina kru¾nic obsahuje pøesnì tøi zadané -body. Av¹ak nalezneme i jeden ètyøúhelník, jeho¾ vrcholy le¾í na jedné kru¾nici. - -Zkusíme nyní odhadnout, jak velký je rovinný graf popisující Voroného diagram. Podle slavné Eulerovy formule má ka¾dý rovinný graf nejvý¹e lineárnì -mnoho vrcholù, hran a stìn -- pro $v$ vrcholù, $e$ hran a $f$ stìn je $e \le 3v-6$ a navíc $v+f = e+2$. Tedy slo¾itost diagramu je lineární vzhledem k -poètu zadaných bodù $n=f$, $\O(n)$. Navíc Voroného diagram lze zkonstruovat v èase $\O(n \log n)$, napøíklad pomocí zametání roviny nebo metodou -rozdìl a panuj. Tím se v¹ak zabývat nebudeme,\foot{Pro zvídavé, kteøí nemají zkou¹ku druhý den ráno: Detaily naleznete v zápiscích z pøedloòského -ADSka.} místo toho si uká¾eme, jak v ji¾ spoèteném Voroného diagramu rychle hledat nejbli¾¹í body. - -\h{Lokalizace bodu uvnitø mnohoúhelníkové sítì} - -Problém medvìdù je najít v medvìdí mapì co nejrychleji nejbli¾¹í iglù. Máme v rovinì sí» tvoøenou mnohoúhelníky. Chceme pro jednotlivé body rychle -rozhodovat, do kterého mnohoúhelníku patøí. Na¹e øe¹ení budeme optimalizovat pro jeden pevný rozklad a obrovské mno¾ství rùzných dotazù, které chceme -co nejrychleji zodpovìdìt.\foot{Pøedstavujme si to tøeba tak, ¾e medvìdùm zprovozníme server. Ten jednou schroustá celou mapu a potom co nejrychleji -odpovídá na jejich dotazy. Medvìdi tak nemusí v mapách nic hledat, staèí se pøipojit na server a poèkat na odpovìï.} Nejprve pøedzpracujeme zadané -mnohoúhelníky a vytvoøíme strukturu, která nám umo¾ní rychlé dotazy na jednotlivé body. - -Uka¾me si pro zaèátek øe¹ení bez pøedzpracování. Rovinu budeme zametat pøímkou shora dolù. Podobnì jako pøi hledání prùseèíkù úseèek, udr¾ujeme si prùøez -pøímkou. V¹imnìte si, ¾e tento prùøez se mìní jenom ve vrcholech mnohoúhelníkù. Ve chvíli, kdy narazíme na hledaný bod, podíváme se, do kterého -intervalu v prùøezu patøí. To nám dá mnohoúhelník, který nahlásíme. Prùøez budeme uchovávat ve vyhledávacím stromì. Takové øe¹ení má slo¾itost $\O(n -\log n)$ na dotaz, co¾ je hroznì pomalé. - -Pøedzpracování bude fungovat následovnì. Jak je naznaèeno na obrázku pøeru¹ovanými èárami, rozøe¾eme si celou rovinu na pásy, bìhem kterých se prùøez -pøímkou nemìní. Pro ka¾dý z nich si pamatujeme stav stromu popisující, jak vypadal prùøez pøi procházení tímto pásem. Kdy¾ chceme lokalizovat nìjaký bod, -nejprve pùlením nalezneme pás, ve kterém se nachází. Poté polo¾íme dotaz na pøíslu¹ný strom. Strom procházíme a po cestì si dopoèítáme souøadnice -prùøezu, a¾ lokalizujeme správný interval v prùøezu. Dotaz doká¾eme zodpovìdìt v èase $\O(\log n)$. Hledaný bod je na obrázku naznaèen prázdným -koleèkem a nalezený interval v prùøezu je vyta¾ený tuènì. - -\figure{8-geom2_4_pasy_mnohouhelniku.eps}{Mnohoúhelníky rozøezané na pásy.}{2.5in} - -Jenom¾e na¹e øe¹ení má jeden háèek: Jak zkonstruovat jednotlivé verze stromu dostateènì rychle? K tomu napomohou {\I èásteènì perzistentní} datové -struktury. Pod perzistencí se myslí, ¾e struktura umo¾òuje uchovávat svoji historii. Èásteènì perzistentní struktury nemohou svoji historii -modifikovat. - -Popí¹eme si, jak vytvoøit perzistentní strom s pamìtí $\O(\log n)$ na zmìnu. Pokud provádíme operaci na stromì, mìní se jenom malá èást stromu. -Napøíklad pøi vkládání do stromu se mìní jenom prvky na jedné cestièce z koøene do listu (a pøípadnì rotací i na jejím nejbli¾¹ím okolí). Proto si -ulo¾íme upravenou cestièku a zbytek stromu budeme sdílet s pøedchozí verzí. Na obrázku je vyznaèena cesta, její¾ vrcholy jsou upravovány. ©edì -oznaèené podstromy navì¹ené na tuto cestu se nemìní, a proto na nì staèí zkopírovat ukazatele. Mimochodem zmìny ka¾dé operace se slo¾itostí $\O(k)$ -lze zapsat v pamìti $\O(k)$, prostì operace nemá tolik èasu, aby mohla pozmìnit pøíli¹ velikou èást stromu. - -\figure{8-geom2_5_upravy_stromu.eps}{Jedna operace mìní pouze okolí cesty -- navì¹ené podstromy se nemìní.}{2in} - -Celková èasová slo¾itost je tedy $\O(n \log n)$ na pøedzpracování Voroného diagramu a vytvoøení persistentního stromu. Kvùli persistenci potøebuje -toto pøedzpracování pamì» $\O(n \log n)$. Na dotaz spotøebujeme èas $\O(\log n)$, nebo» nejprve vyhledáme pùlením pøíslu¹ný pás a poté polo¾íme dotaz -na pøíslu¹nou verzi stromu. Rychleji to ani provést nepùjde, nebo» potøebujeme utøídit souøadnice bodù. - -\s{Lze to lépe?} Na závìr poznamenejme, ¾e se umí provést vý¹e popsaná persistence vyhledávacího stromu v amortizované pamìti $\O(1)$ na zmìnu. Ve -struènosti naznaèíme my¹lenku. Pou¾ijeme stromy, které pøi insertu a deletu provádí amortizovanì jenom konstantnì mnoho úprav své struktury. To nám -napøíklad zaruèí 2-4 stromy z pøedná¹ky a podobnou vlastnost lze dokázat i o èerveno-èerných stromech. Pøi zmìnì potom nebudeme upravovat celou cestu, -ale upravíme jenom jednotlivé vrcholy, kterých se zmìna týká. Ka¾dý vrchol stromu si v sobì bude pamatovat a¾ dvì své verze. Pokud chceme vytvoøit -tøetí verzi, vrchol zkopírujeme stranou. To v¹ak mù¾e vyvolat zmìny v jeho rodièích a¾ do koøene. Situace je naznaèena na obrázku. Pøi vytvoøení nové -verze $3$ pro vrcholu $v$ vytvoøíme jeho kopii $v'$, do které ulo¾íme tuto verzi. Av¹ak musíme také zmìnit rodièe $u$, kterému vytvoøíme novou verzi -ukazující na $v'$. Abychom dosáhli ký¾ené konstantní pamì»ové slo¾itosti, pomù¾e potenciálový argument -- zmìn se provádí amortizovanì jenom -konstantnì mnoho. 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implementation of figure naming and figure transparency -string name,tag; name := ""; tag := ""; -def updatefigname = - if tag="": filenametemplate (name & "%c.eps"); - else: filenametemplate (name & "%c_" & tag & ".eps"); - fi; -enddef; - -def figname(expr n) = name := n; updatefigname; enddef; -def figtag(expr t) = tag := t; updatefigname; enddef; - -picture transparent_picture; -color transparent_color; transparent_color := 0.9white; -def drawtransparent(expr num) = - transparent_picture := currentpicture; -endfig; - -if tag="": filenametemplate (name & "%c_transparent.eps"); -else: filenametemplate (name & "%c_" & tag & "_transparent.eps"); -fi; -beginfig(num); - draw transparent_picture withcolor transparent_color; -endfig; - -updatefigname; -enddef; - -def from(expr p,d,len) = - p+dir(d)*len -enddef; - -def dirs(expr p,d,len) = - p--from(p,d,len) -enddef; - -def drawvertices(expr s,n) = - for i:=s upto n: - draw vertex(PQ[i]); - endfor -enddef; - -def drawfvertices(expr s,n,flags) = - for i:=s upto n: - draw vertex(PQ[i]) flags; - endfor -enddef; - -path centersquare; centersquare := (-0.5,-0.5)--(0.5,-0.5)--(0.5,0.5)--(-0.5,0.5)--cycle; - -def vertex(expr p) = p withpen pencircle scaled 4pt enddef; -def drawemptyvertex(expr p) = unfill fullcircle scaled 4pt shifted p; draw fullcircle scaled 4pt shifted p; enddef; -def drawendpointvertex(expr p) = draw vertex(p) withcolor red; draw fullcircle scaled 6pt shifted p; enddef; -def createpath(expr p) = shakepath(p, 0.015cm,0.1cm) enddef; -vardef shakepath(expr p,d,l) = - save r,b; - path r; r := point(arctime 0 of p) of p; - b := -1; - for i:=l step l until arclength(p): - r := r--(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*d*b; - b := -b; - endfor - r--point(arctime arclength(p) of p) of p -enddef; - -pen normalpen; normalpen := pencircle scaled 0.6pt; -pen boldpen; boldpen := pencircle scaled 1.5pt; -pen bolderpen; bolderpen := pencircle scaled 2pt; -def dotline = withdots scaled 0.82 withpen boldpen enddef; - -vardef unclosedbubblec(expr p,c) = - bubblec((p..reverse p..cycle),c) -enddef; - -vardef bubblec(expr p,c) = - save r; - path r; r := (point(arctime 0 of p) of p)+dir(angle(direction(arctime 0 of p) of p rotated 90))*c; - for i:=0.01cm step 0.025cm until arclength(p): - r := r..(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*c; - endfor - r..(point(arctime arclength(p) of p) of p)+dir(angle(direction(arctime arclength(p) of p) of p rotated 90))*c..cycle -enddef; - -vardef bubble(expr p) = bubblec(p,0.12cm) enddef; -vardef unclosedbubble(expr p) = unclosedbubblec(p,0.12cm) enddef; diff --git a/9-fft/9-fft.tex b/9-fft/9-fft.tex deleted file mode 100644 index 3340759..0000000 --- a/9-fft/9-fft.tex +++ /dev/null @@ -1,392 +0,0 @@ -\def\scharfs{\char"19} -\input lecnotes.tex -\def\imply{\Rightarrow} -\prednaska{9}{Fourierova transformace}{\vbox{\hbox{(K.Jakubec, M.Polák - a~G.Ocsovszky,}\hbox{\ V.Tùma, M.Kozák)}}} - -Násobení polynomù mù¾e mnohým pøipadat jako pomìrnì (algoritmicky) snadný -problém. Asi ka¾dého hned napadne \uv{hloupý} algoritmus -- vezmeme -koeficienty prvního polynomu a~vynásobíme ka¾dý se v¹emi koeficienty druhého -polynomu a~pøíslu¹nì u~toho seèteme i~exponenty (stejnì jako to dìláme, kdy¾ -násobíme polynomy na~papíøe). Pokud stupeò prvního polynomu je $n$ a~druhého -$m$, strávíme tím èas $\Omega(mn)$. Pro $m=n$ je to kvadraticky pomalé. -Na~první pohled se mù¾e zdát, ¾e rychleji to prostì nejde (pøeci musíme -v¾dy vynásobit \uv{ka¾dý s~ka¾dým}). Ve skuteènosti to ale rychleji fungovat -mù¾e, ale k~tomu je potøeba znát trochu tajemný algoritmus FFT neboli {\I Fast -Fourier Transform}. - - -\ss{Trochu algebry na~zaèátek} -Celé polynomy oznaèujeme velkými písmeny, jednotlivé èleny polynomù pøíslu¹nými -malými písmeny (pø.: polynom $W$ stupnì $d$ má koeficienty $w_{0}, w_{1}, -w_{2},\ldots, w_{d}$). - -Libovolný polynom $P$ stupnì (nejvý¹e) $d$ lze reprezentovat -jednak jeho koeficienty, tedy èísly $p_{0}, p_{1}, \ldots ,p_{d}$, druhak -i~pomocí hodnot: - -\>{\bf Lemma:} Polynom stupnì nejvý¹e $d$ je jednoznaènì urèeò svými -hodnotami v~$d+1$ rùzných bodech. - -\>{\it Dùkaz:} -Polynom stupnì $d$ má maximálnì $d$ koøenù (indukcí -- je-li -$k$ koøenem $P$, pak lze $P$ napsat jako $(x-k)Q$ kde $Q$ je polynom stupnì -o~jedna men¹í, pøitom polynom stupnì 1 má jediný koøen); uvá¾íme-li -dva rùzné polynomy $P$ a~$Q$ stupnì $d$ nabývající v~daných bodech stejných -hodnot, tak $P-Q$ je polynom stupnì maximálnì $d$, ka¾dé -z $x_{0}\ldots x_{d}$ je koøenem tohoto polynomu $\imply$ spor, polynom stupnì -$d$ má $d+1$ koøenù $\imply$ $P-Q$ musí být nulový polynom $\imply$ $P=Q$. -\qed -\medskip - -Pov¹imnìme si jedné skuteènosti -- máme-li dva polynomy $A$ a~$B$ stupnì $d$ -a~body $x_{0}, \ldots, x_{n}$, dále polynom $C=A \cdot B$ (stupnì $2d$), pak -platí $C(x_{j}) = A(x_{j}) \cdot B(x_{j})$ pro $j = 0,1,2, \ldots, n$. Toto -èiní tento druhý zpùsob reprezentace polynomu velice atraktivním pro násobení --- máme-li $A$ i $B$ reprezentované hodnotami v $n \geq 2d+1$ bodech, pak -snadno (v $\Theta(n)$) spoèteme takovou reprezentaci $C$. -Problémem je, ¾e typicky máme polynom zadaný koeficienty, a~ne hodnotami -v~bodech. Tím pádem potøebujeme nìjaký hodnì rychlý algoritmus (tj. -rychlej¹í ne¾ kvadratický, jinak bychom si nepomohli oproti hloupému -algoritmu) na~pøevod polynomu z jedné reprezentace do druhé a~zase zpìt. - -\s{Idea, jak by mìl algoritmus pracovat:} -\algo -\:Vybereme $n\geq 2d+1$ bodù $x_{0}, x_{1}, \ldots , x_{n-1}$. -\:V tìchto bodech vyhodnotíme polynomy $A$ a~$B$. -\:Nyní ji¾ v~lineárním èase získáme hodnoty polynomu $C$ v~tìchto bodech: - $C(x_i) = A(x_i)\cdot B(x_i)$ -\:Pøevedeme hodnoty polynomu $C$ na~jeho koeficienty. -\endalgo - -\>Je vidìt, ¾e klíèové jsou kroky 2 a~4. -Celý trik spoèívá v~chytrém vybrání onìch bodù, ve kterých budeme polynomy -vyhodnocovat -- zvolí-li se obecná $x_j$, tak se to rychle neumí, pro speciální -$x_j$ ale uká¾eme, ¾e to rychle jde. - -\ss{Vyhodnocení polynomu metodou Rozdìl a~panuj (algoritmus FFT):} -Mìjme polynom $P$ stupnì $\leq d$ a~chtìjme jej vyhodnotit v~$n$ bodech. -Vybereme si body tak, aby byly spárované, èili $\pm x_{0}, \pm x_{1}, -\ldots , \pm x_{n/2-1} $. To nám výpoèet urychlí, proto¾e pak se druhé -mocniny $x_{j}$ shodují s~druhými mocninami $-x_{j}$. - -Polynom $P$ rozlo¾íme na~dvì èásti, první obsahuje èleny se sudými exponenty, -druhá s~lichými: -$$P(x) = (p_{0}x^{0} + p_{2}x^{2} + \ldots + p_{d-2}x^{d-2}) + (p_{1}x^{1} + - p_{3}x^{3} + \ldots + p_{d-1}x^{d-1})$$ -\>se zavedením znaèení: -$$P_s(t) = p_0t^0 + p_{2}t^{1} + \ldots + p_{d - 2}t^{d - 2\over 2}$$ -$$P_l(t) = p_1t^0 + p_3t^1 + \ldots + p_{d - 1}t^{d - 2\over 2}$$ - -\>bude $P(x) = P_s(x^{2}) + xP_l(x^{2})$ a~$P(-x) = P_s(x^{2}) - -xP_l(x^{2})$. Jinak øeèeno, vyhodnocování polynomu $P$ v~$n$ bodech se nám -smrskne na~vyhodnocení $P_s$ a~$P_l$ v~$n/2$ bodech -- oba jsou polynomy -stupnì nejvý¹e $d/2$ a~vyhodnocujeme je v~$x^{2}$ (vyu¾íváme -rovnosti $(x_{i})^{2} = (-x_{i})^{2}$). - -\s{Pøíklad:} -$3 + 4x + 6x^{2} + 2x^{3} + x^{4} + 10x^{5} = (3 + 6x^{2} + x^{4}) + x(4 + -2x^{2} + 10x^{4})$. - -Teï nám ov¹em vyvstane problém s~oním párováním -- druhá mocnina pøece nemù¾e -být záporná a~tím pádem u¾ v~druhé úrovni rekurze body spárované nebudou. -Z~tohoto dùvodu musíme pou¾ít komplexní èísla -- tam druhé mocniny záporné býti -mohou. - -% komplex -\h{Komplexní intermezzo} -\def\i{{\rm i}} -\def\\{\hfil\break} - -\s{Základní operace} - -\itemize\ibull -\:Definice: ${\bb C} = \{a + b\i \mid a,b \in {\bb R}\}$ - -\:Sèítání: $(a+b\i)\pm(p+q\i) = (a\pm p) + (b\pm q)\i$. \\ -Pro $\alpha\in{\bb R}$ je $\alpha(a+b\i) = \alpha a + \alpha b\i$. - -\:Komplexní sdru¾ení: $\overline{a+b\i} = a-b\i$. \\ -$\overline{\overline x} = x$, $\overline{x\pm y} = \overline{x} \pm -\overline{y}$, $\overline{x\cdot y} = \overline x \cdot \overline y$, $x -\cdot \overline x \in {\bb R}$. - -\:Absolutní hodnota: $\vert x \vert = \sqrt{x\cdot\overline{x}}$, tak¾e $\vert -a+b\i \vert = \sqrt{a^2+b^2}$. \\ -Také $\vert \alpha x \vert = \vert \alpha\vert \cdot \vert x \vert$. - -\:Dìlení: $x/y = (x\cdot \overline{y}) / (y \cdot \overline{y})$. -\endlist - -\s{Gau{\scharfs}ova rovina a goniometrický tvar} - -\itemize\ibull -\:Komplexním èíslùm pøiøadíme body v~${\bb R}^2$: $a+b\i \leftrightarrow (a,b)$. - -\:$\vert x\vert$ je vzdálenost od~bodu $(0,0)$. - -\:$\vert x\vert = 1$ pro èísla le¾ící na~jednotkové kru¾nici ({\I komplexní jednotky}). \\ -Pak platí $x=\cos\varphi + \i\sin\varphi$ pro nìjaké $\varphi\in\left[ -0,2\pi \right)$. - -\:Pro libovolné $x\in{\bb C}$: $x=\vert x \vert \cdot (\cos\varphi(x) + -\i\sin\varphi(x))$. \\ -Èíslu $\varphi(x)\in\left[ 0,2\pi \right)$ øíkáme {\I argument} -èísla~$x$, nìkdy znaèíme $\mathop{\rm arg} x$. - -\:Navíc $\varphi({\overline{x}}) = -\varphi(x)$. -\endlist - -\s{Exponenciální tvar} - -\itemize\ibull -\:Eulerova formule: $e^{i\varphi} = \cos\varphi + \i\sin\varphi$. - -\:Ka¾dé $x\in{\bb C}$ lze tedy zapsat jako $\vert x\vert \cdot e^{\i\cdot -\varphi(x)}$. - -\:Násobení: $xy = \left(\vert x\vert\cdot e^{\i\cdot\varphi(x)}\right) \cdot - \left(\vert y\vert\cdot e^{\i\cdot\varphi(y)}\right) = \vert x\vert - \cdot \vert y\vert \cdot e^{\i\cdot(\varphi(x) + \varphi(y))}$. \\ -(absolutní hodnoty se násobí, argumenty sèítají) - -\:Umocòování: $x^\alpha = \left(\vert x\vert\cdot e^{\i\cdot\varphi(x)}\right)^ - \alpha = {\vert x\vert}^\alpha\cdot e^{\i \alpha \varphi(x)}$. - -\:Odmocòování: $\root n\of x = {\vert x\vert}^{1/n} \cdot e^{\i\cdot -\varphi(x)/n}$. \\ -Pozor -- odmocnina není jednoznaèná: $1^4=(-1)^4=\i^4=(-\i)^4=1$. -\endlist - -\s{Odmocniny z~jednièky} - -\itemize\ibull -\:Je-li nìjaké $x\in{\bb C}$ $n$-tou odmocninou z~jednièky, musí platit: -$\vert x \vert = 1$, tak¾e $x=e^{\i\varphi}$ pro nìjaké~$\varphi$. -Proto $x^n = e^{\i\varphi n} = \cos{\varphi n} + \i\sin\varphi n = 1$. -Platí tedy $\varphi n = 2k\pi$ pro nìjaké $k\in{\bb Z}$. - -\:Z~toho plyne: $\varphi = 2k\pi/n$ \\ -(pro $k=0,\ldots,n-1$ dostáváme rùzné $n$-té odmocniny). - -\:Obecné odmocòování: $\root n \of x = {\vert x\vert}^{1/n} \cdot e^{\i\varphi - (x)/n} \cdot u$, kde $u=\root n\of 1$. - -\:Je-li $x$ odmocninou z 1, pak $\overline{x} = x^{-1}$ -- je toti¾ $1 = \vert -x\cdot \overline{x}\vert = x\cdot \overline{x}$. -\endlist - -\s{Primitivní odmocniny} - -\s{Definice:} $x$ je {\I primitivní} $k$-tá odmocnina z 1 $\equiv x^k=1 \land \forall j: 0Tuto definici splòují napøíklad èísla $\omega = e^{2\pi \i / k}$ a $\overline\omega = e^{-2\pi\i/k}$. -Platí toti¾, ¾e $\omega^j = e^{2\pi\i j/k}$, co¾ je rovno~1 právì tehdy, -je-li $j$ násobkem~$k$ (jednotlivé mocniny èísla~$\omega$ postupnì obíhají -jednotkovou kru¾nici). Analogicky pro~$\overline\omega$. - -\>Uka¾me si nìkolik pozorování fungujících pro libovolné èíslo~$\omega$, -které je primitivní $k$-tou odmocninou z~jednièky (nìkdy budeme potøebovat, -aby navíc $k$ bylo sudé): - -\itemize\ibull -\:Pro $0\leq jFFT($P$, $ \omega$) - -\>{\sl Vstup:} $p_{0}, \ldots , p_{n-1}$, koeficienty polynomu $P$ stupnì -nejvý¹e $n-1$, a~$\omega$, -$n$-tá primitivní odmocina z jedné. - -\>{\sl Výstup:} Hodnoty polynomu v~bodech $1, \omega, \omega^{2}, \ldots , -\omega^{n - 1}$, èili èísla $P(1), P(\omega), P(\omega^{2}),$ $\ldots , -P(\omega^{n - 1})$. - -\algo -\:Pokud $n = 1$, vrátíme $p_{0}$ a~skonèíme. -\:Jinak rozdìlíme $P$ na èleny se sudými a lichými exponenty (jako v pùvodní -my¹lence) a~rekurzivnì zavoláme FFT($P_s$, $\omega^{2}$) a~FFT($P_l$, -$\omega^{2}$) -- $P_l$ i~$P_s$ jsou stupnì max. $n/2-1$, $\omega^2$ je -$n/2$-tá primitivní odmocnina, a mocniny $\omega^2$ jsou stále po dvou -spárované ($n$ je mocnina dvojky, a tedy i $n/2$ je sudé; popø. $n=2$ a je to -zøejmé). -\:Pro $j = 0, \ldots , n/2 - 1$ spoèítáme: - -\:\qquad $P(\omega^{j}) = P_s(\omega^{2j}) + \omega^{j}\cdot P_l(\omega^{2j})$. - -\:\qquad $P(\omega^{j+n/2})=P_s(\omega^{2j})-\omega^{j}\cdot P_l(\omega^{2j})$. - -\endalgo - - -\s{Èasová slo¾itost:} -\>$T(n)=2T(n/2) + \Theta(n) \Rightarrow$ slo¾itost $\Theta(n \log n)$, jako -MergeSort. - - -Máme tedy algoritmus, který pøevede koeficienty polynomu na~hodnoty tohoto -polynomu v~rùzných bodech. Potøebujeme ale také algoritmus, který doká¾e -reprezentaci polynomu pomocí hodnot pøevést zpìt na~koeficienty polynomu. -K~tomu nám pomù¾e podívat se na~ná¹ algoritmus trochu obecnìji. - - -\s{Definice:} -\>{\I Diskrétní Fourierova transformace} $(DFT)$ -je zobrazení $f: { {\bb C} ^n} \rightarrow { {\bb C} ^n}$, kde $$y=f(x) \equiv -\forall j \ y_{j} = \sum \limits ^{n-1}_{k=0} x_{k} \cdot \omega ^{jk}$$ -(DFT si lze mimo jiné pøedstavit jako funkci vyhodnocující polynom -s~koeficienty $x_k$ v~bodech $\omega^j$). Takovéto zobrazení je lineární -a~tedy popsatelné maticí $\Omega$ s~prvky $\Omega_{jk} = \omega^{jk}$. -Chceme-li umìt pøevádìt z~hodnot polynomu na koeficienty, zajímá nás inverze -této matice. - - -\ss{Jak najít inverzní matici?} -Znaème $\overline{\Omega}$ matici, její¾ prvky -jsou komplexnì sdru¾ené odpovídajícím prvkùm $\Omega$, a vyu¾ijme následující -lemma: - -\ss{Lemma:}$\quad \Omega\cdot \overline{\Omega} = n\cdot E$. - -\proof $$ (\Omega\cdot \overline{\Omega})_{jk} = \sum_{l=0}^{n-1} \omega^{jl} -\cdot \overline{\omega^{lk}} = \sum \omega^{jl} \cdot \overline{\omega}^{lk} = -\sum \omega^{jl} \cdot \omega^{-lk} = \sum \omega^{l(j-k)}\hbox{.}$$ -\itemize\ibull -\:Pokud $j=k$, pak $ \sum \limits ^{n-1}_{l=0} (\omega ^{0}) ^{l} = n$. - -\:Pokud $j\neq k$, pou¾ijeme vzoreèek pro souèet geometrické øady -s kvocientem $\omega ^{(j-k) }$ a~dostaneme ${{\omega^{(j-k)n} -1} \over -{\omega^{(j-k)} -1}} ={1-1 \over \neq 0} = 0$ ( -$\omega^{j-k} - 1$ je jistì $\neq 0$, nebo» $\omega$ je $n$-tá primitivní -odmoncina a $j-kNa¹li jsme inverzi: -$\Omega({1 \over n} \overline{\Omega}) = {1 \over n}\Omega \cdot -\overline{\Omega} = E$, \quad $\Omega^{-1}_{jk} = {1 \over n}\overline{\omega^ -{jk}} = {1 \over n}\omega^{-jk} = {1 \over n} {(\omega^{-1})}^{jk}$, \quad -(pøipomínáme, $\omega^{-1}$ je $\overline{\omega}$). - -Vyhodnocení polynomu lze provést vynásobením $\Omega$, pøevod do pùvodní -reprezentace vynásobením $\Omega^{-1}$. My jsme si ale v¹imli chytrého -spárování, a vyhodnocujeme polynom rychleji ne¾ kvadraticky (proto FFT, -jako¾e {\it fast}, ne jako {\it fuj}). Uvìdomíme-li si, ¾e $\overline \omega = -\omega^{-1}$ je také $n$-tá primitivní odmocnina z 1 (má akorát -úhel s opaèným znaménkem), tak mù¾eme stejným trikem vyhodnotit i~zpìtný -pøevod -- nejprve vyhodnotíme $A$ a $B$ v $\omega^j$, poté pronásobíme -hodnoty a~dostaneme tak hodnoty polynomu $C=A\cdot B$, a pustíme na nì -stejný algoritmus s~$\omega^{-1}$ (hodnoty $C$ -vlastnì budou v~algoritmu \uv{koeficienty polynomu}). Nakonec jen získané -hodnoty vydìlíme $n$ a~máme chtìné koeficienty. - -\s{Výsledek:} Pro $n= 2^k$ lze DFT na~${\bb C}^n$ spoèítat v~èase $\Theta(n -\log n)$ a~DFT$^{-1}$ takté¾. - -\s{Dùsledek:} -Polynomy stupnì $n$ lze násobit v~èase $\Theta(n \log n)$: -$\Theta(n \log n)$ pro vyhodnocení, $\Theta(n)$ pro vynásobení a~$\Theta(n -\log n)$ pro pøevedení zpìt. - -\s{Pou¾ití FFT:} - -\itemize\ibull - -\:Zpracování signálu -- rozklad na~siny a~cosiny o~rùzných frekvencích -$\Rightarrow$ spektrální rozklad. -\:Komprese dat -- napøíklad formát JPEG. -\:Násobení dlouhých èísel v~èase $\Theta(n \log n)$. -\endlist - -\s{Paralelní implementace FFT} - -\figure{img.eps}{Pøíklad prùbìhu algoritmu na~vstupu velikosti 8}{3in} - -Zkusme si prùbìh algoritmu FFT znázornit graficky (podobnì, jako jsme kreslili -hradlové sítì). Na~levé stranì obrázku se nachází vstupní vektor $x_0,\ldots,x_{n-1}$ -(v~nìjakém poøadí), na~pravé stranì pak výstupní vektor $y_0,\ldots,y_{n-1}$. -Sledujme chod algoritmu pozpátku: Výstup spoèítáme z~výsledkù \uv{polovièních} -transformací vektorù $x_0,x_2,\ldots,x_{n-2}$ a $x_1,x_3,\ldots,x_{n-1}$. -Èerné krou¾ky pøitom odpovídají výpoètu lineární kombinace $a+\omega^kb$, -kde $a,b$ jsou vstupy krou¾ku a $k$~nìjaké pøirozené èíslo závislé na poloze -krou¾ku. Ka¾dá z~polovièních transformací se poèítá analogicky z~výsledkù -transformace velikosti $n/4$ atd. Celkovì výpoèet probíhá v~$\log_2 n$ vrstvách -po~$\Theta(n)$ operacích. - -Jeliko¾ operace na~ka¾dé vrstvì probíhají na sobì nezávisle, mù¾eme je poèítat -paralelnì. Ná¹ diagram tedy popisuje hradlovou sí» pro paralelní výpoèet FFT -v~èase $\Theta(\log n\cdot T)$ a prostoru $\O(n\cdot S)$, kde $T$ a~$S$ znaèí -èasovou a prostorovou slo¾itost výpoètu lineární kombinace dvou komplexních èísel. - -\s{Cvièení:} Doka¾te, ¾e permutace vektoru $x_0,\ldots,x_{n-1}$ odpovídá bitovému -zrcadlení, tedy ¾e na pozici~$b$ shora se vyskytuje prvek $x_d$, kde~$d$ je -èíslo~$b$ zapsané ve~dvojkové soustavì pozpátku. - -\s{Nerekurzivní FFT} - -Obvod z~pøedchozího obrázku také mù¾eme vyhodnocovat po~hladinách zleva doprava, -èím¾ získáme elegantní nerekurzivní algoritmus pro výpoèet FFT v~èase $\Theta(n\log n)$ -a prostoru $\Theta(n)$: - -\algo -\algin $x_0,\ldots,x_{n-1}$ -\:Pro $j=0,\ldots,n-1$ polo¾íme $y_k\= x_{r(k)}$, kde $r$ je funkce bitového zrcadlení. -\:Pøedpoèítáme tabulku $\omega^0,\omega^1,\ldots,\omega^{n-1}$. -\:$b\= 1$ \cmt{velikost bloku} -\:Dokud $b zaøídí, aby se odstavec neodsadil. Pomocí \I se sází kurzíva, \foot vyrobí poznámku pod èarou. +\>Je svaèveèer. {\I Lysperní jezeleni} se vírnì vrtáèejí v~mokøavì.\foot{Viz Lewis Caroll: Jabberwocky.} + +\itemize\ibull +\:Takhle. +\:Vypadají. +\:Odrá¾ky. +\endlist + +\h{Druhá kapitola} + +\s{Vìta:} Paøez není strom. + +\proof +Strom je speciálním pøípadem lesa. Les je (podle definice) graf bez kru¾nic. +Paøez obsahuje alespoò jednu kru¾nici. Proto paøez nemù¾e být les, a~tedy ani +strom. +\qed + +\s{Algoritmus:} (tøídìní posloupnosti $a_1,\ldots,a_n$ pomocí {\sc Stupidsort}u) + +\algo +\:$\pi \leftarrow \hbox{identická permutace na~mno¾inì $\{1,\ldots,n\}$}$. +\:Opakuj: +\::Ovìø, zda je posloupnost $a_{\pi(1)},a_{\pi(2)},\ldots,a_{\pi(n)}$ uspoøádána vzestupnì. + Pokud je, vra» ji jako výsledek. +\::Nahraï $\pi$ jejím lexikografickým následníkem. +\endalgo + +\bye diff --git a/old/0-demo/Makefile b/old/0-demo/Makefile new file mode 100644 index 0000000..98def69 --- /dev/null +++ b/old/0-demo/Makefile @@ -0,0 +1,3 @@ +P=0-demo + +include ../Makerules diff --git a/old/10-prevody/10-prevody.tex b/old/10-prevody/10-prevody.tex new file mode 100644 index 0000000..070d102 --- /dev/null +++ b/old/10-prevody/10-prevody.tex @@ -0,0 +1,282 @@ +\input lecnotes.tex + +\prednaska{10}{Pøevody problémù}{\vbox{\hbox{(zapsali Martin Chytil, Vladimír Kudelas,}\hbox{ Michal Kozák, Vojta Tùma)}}} + +\>Na této pøedná¹ce se budeme zabývat rozhodovacími problémy a jejich obtí¾ností. +Za jednoduché budeme trochu zjednodu¹enì pova¾ovat ty problémy, na~nì¾ známe algoritmus +pracující v~polynomiálním èase. + +\s{Definice:} {\I Rozhodovací problém} je takový problém, jeho¾ výstupem je v¾dy {\sc ano}, nebo {\sc ne}. +[Formálnì bychom se na~nìj mohli dívat jako na~mno¾inu $L$ vstupù, na~které je odpovìï {\sc ano}, +a místo $L(x)=\hbox{\sc ano}$ psát prostì $x\in L$.] + +Vstupy mìjme zakódované jen pomocí nul a jednièek (obecnì je jedno, jaký základ pro soustavu +kódování zvolíme, pøevody mezi soustavami o nìjakém základu $\neq$ 1 jsou co do velikosti +zápisu polynomiální). Rozhodovací problém je tedy +$f:\ \{0,1\}^{\ast} \to \{0,1\}$, to jest funkce z mno¾iny v¹ech øetìzcù jednièek a nul +do mno¾iny $\{1,0\}$, kde 1 na výstupu znamená {\sc ano}, 0 {\sc ne}. + +\s{Pøíklad:} Je dán bipartitní graf $G$ a $k \in {\bb N}$. Existuje v $G$ +párování, které obsahuje alespoò $k$ hran? + +To, co bychom ve~vìt¹inì pøípadù chtìli, je samozøejmì nejen zjistit, zda takové párování +existuje, ale také nìjaké konkrétní najít. V¹imnìme si ale, ¾e kdy¾ umíme rozhodovat +existenci párování v~polynomiálním èase, mù¾eme ho polynomiálnì rychle i najít: + +Mìjme èernou skøíòku (fungující v polynomiálním èase), která odpoví, zda daný +graf má nebo nemá párování o~$k$ hranách. Odebereme z~grafu libovolnou hranu +a zeptáme se, jestli i tento nový graf má párovaní velikosti~$k$. Kdy¾ má, pak tato +hrana nebyla pro existenci párování potøebná, a~tak ji odstraníme. Kdy¾ naopak +nemá (hrana patøí do ka¾dého párování po¾adované velikosti), tak si danou hranu +poznamenáme a odebereme nejen ji a její vrcholy, ale také hrany, které do tìchto +vrcholù vedly. Toto je korektní krok, proto¾e v pùvodním grafu tyto vrcholy +byly navzájem spárované, a tedy nemohou být spárované s~¾ádnými jinými vrcholy. +Na~nový graf aplikujeme znovu tentý¾ postup. Výsledkem je mno¾ina hran, které patøí +do hledaného párování. Hran, a tedy i iterací na¹eho algoritmu, je polynomiálnì +mnoho a skøíòka funguje v polynomiálním èase, tak¾e celý algoritmus je polynomiální. + +A~jak ná¹ rozhodovací problém øe¹it? Nejsnáze tak, ¾e ho pøevedeme +na~{jiný,\footnote{${}^{\dag}$}{vìrni matfyzáckým vtipùm}} který +u¾ vyøe¹it umíme. Tento postup jsme (právì u~hledání párování) u¾ pou¾ili +v~kapitole o~Dinicovì algoritmu. Vytvoøili jsme vhodnou sí», pro kterou +platilo, ¾e v~ní existuje tok velikosti~$k$ právì tehdy, kdy¾ +v~pùvodním grafu existuje párování velikosti~$k$. + +Takovéto pøevody mezi problémy mù¾eme definovat obecnì: + +\s{Definice:} Jsou-li $A$, $B$ rozhodovací problémy, pak øíkáme, ¾e $A$ lze {\I +redukovat} (neboli {\it pøevést}) na $B$ (pí¹eme $A \rightarrow B$) právì tehdy, +kdy¾ existuje funkce $f$ spoèitatelná v polynomiálním èase taková, ¾e pro $\forall +x: A(x) = B(f(x))$. V¹imnìme si, ¾e $f$ pracující v polynomiálním èase vstup +zvìt¹í nejvíce polynomiálnì. + +\s{Pozorování:} $A\rightarrow B$ také znamená, ¾e problém~$B$ je alespoò tak tì¾ký +jako problém~$A$ (tím myslíme, ¾e pokud lze $B$ øe¹it v~polynomiálním èase, +lze tak øe¹it i~$A$): Nech» problém~$B$ umíme øe¹it v~èase $\O(b^k)$, kde +$b$ je délka jeho vstupu. Nech» dále funkce $f$ pøevádìjící $A$ na $B$ pracuje +v~èase $\O(a^\ell)$ pro vstup délky~$a$. Spustíme-li tedy $B(f(x))$ na~nìjaký +vstup~$x$ problému~$A$, bude mít $f(x)$ délku $\O(a^\ell)$, kde $a=|q|$; tak¾e +$B(f(x))$ pobì¾í v~èase $\O(a^\ell + (a^\ell)^k) = \O(a^{k\ell})$, co¾ je +polynomiální v~délce vstupu~$a$. + + +\s{Pozorování:} Pøevoditelnost je +\itemize\ibull +\:reflexivní (úlohu mù¾eme pøevést na tu stejnou identickým zobrazením): $A \rightarrow A$, +\:tranzitivní: Je-li $A \rightarrow B$ funkcí $f$, $B \rightarrow C$ funkcí $g$, +pak $A \rightarrow C$ slo¾enou funkcí $g \circ f$ +(slo¾ení dvou polynomiálních funkcí je zase polynomiální funkce, jak u¾ jsme zpozorovali +v~pøedchozím odstavci). +\endlist +\>Takovýmto relacím øíkáme kvaziuspoøádání -- nesplòují obecnì antisymetrii, tedy mù¾e nastat +$A\rightarrow B$ a $B\rightarrow A$. Omezíme-li se v¹ak na tøídy navzájem pøevoditelných +problémù, dostáváme ji¾ (èásteèné) uspoøádání. Existují i navzájem nepøevoditelné problémy -- +napøíklad problém v¾dy odpovídající 1 a problém v¾dy odpovídající 0. +Nyní se ji¾ podíváme na nìjaké zajímavé problémy. Obecnì to budou problémy, na které +polynomiální algoritmus není znám, a vzájemnými pøevody zjistíme ¾e jsou stejnì tì¾ké. + +\h{1. problém: SAT} +\>Splnitelnost (satisfiability) logických formulí, tj. dosazení 1 èi +0 za promìnné v logické formuli tak, aby formule dala výsledek 1. + +\>Zamìøíme se na speciální formu zadání formulí, {\I konjunktivní normální formu} (CNF), + které splòují následující podmínky: +\itemize\ibull +\:{\I formule} je zadána pomocí {\I klauzulí}\footnote{${}^{\dag}$}{bez politických konotací} oddìlených $\land$, +\:ka¾dá {\I klauzule} je slo¾ená z {\I literálù} oddìlených $\lor$, +\:ka¾dý {\I literál} je buïto promìnná nebo její negace. +\endlist +\>Formule mají tedy tvar: +$$\psi = (\ldots\lor\ldots\lor\ldots\lor\ldots) \land (\ldots\lor\ldots\lor\ldots\lor\ldots) \land \ldots $$ + +\>{\I Vstup:} Formule $\psi$ v konjunktivní normální formì. + +\>{\I Výstup:} $\exists$ dosazení 1 a 0 za promìnné takové, ¾e hodnota formule $\psi(\ldots) = 1$. + + + +\>Pøevod nìjaké obecné formule $\psi$ na jí ekvivalentní $\chi$ v~CNF mù¾e +zpùsobit, ¾e $\chi$ je exponenciálnì velká vùèi $\psi$. +Pozdìji uká¾eme, ¾e lze podniknout pøevod na takovou formuli $\chi'$ v~CNF, která sice není +ekvivalentní s $\psi$ (pøibydou nám promìnné, a ne ka¾dý roz¹íøený model +$\psi$ je modelem $\chi'$), ale je splnitelná právì tehdy, kdy¾ je splnitelná $\psi$ -- co¾ nám +pøesnì staèí -- a je lineárnì velká vùèi $\psi$. + +\h{2. problém: 3-SAT} +\s{Definice:} 3-SAT je takový SAT, v nìm¾ ka¾dá klauzule obsahuje nejvý¹e tøi literály. + +\s{Pøevod 3-SAT na SAT:} +Vstup není potøeba nijak upravovat, 3-SAT splòuje vlastnosti SATu, proto 3-SAT +$\rightarrow$ SAT (SAT je alespoò tak tì¾ký jako 3-SAT) + +\s {Pøevod SAT na 3-SAT:} +Musíme formuli pøevést tak, abychom neporu¹ili splnitelnost. + +\>Trik pro dlouhé klauzule: Ka¾dou \uv{¹patnou} klauzuli +$$(\alpha \lor \beta) \hbox{, t¾. } \vert\alpha\vert + \vert\beta\vert \ge 4 +,\ \vert\alpha\vert \geq 2,\ \vert\beta\vert\geq 2$$ +pøepí¹eme na: $$(\alpha \lor x) \land (\beta \lor \lnot x),$$ +kde $x$ je nová promìnná (pøi ka¾dém dìlení klauzule {\it jiná} +nová promìnná). +%kterou nastavíme tak, abychom neovlivnili splnitelnost formule. + +\>Tento trik opakujeme tak dlouho, dokud je to tøeba -- formuli délky $k+l$ +roztrhneme na formule délky $k+1$ a $l+1$. Pokud klauzule pùlíme, dostaneme +polynomiální èas (strom rekurze má logaritmicky pater -- formule délky alespoò 6 +se nám pøi rozdìlení zmen¹í na dvì instance velikosti maximálnì $2/3$ pùvodní, krat¹í +formule nás netrápí; na ka¾dém patøe se vykoná tolik co na pøedchozím + $2^{hloubka}$ +za pøidané formule). Velikost výsledné formule je tím pádem polynomiální vùèi pùvodní: +v ka¾dém kroku se pøidají jen dva literály, tedy celkem {\it èas na pøevod}$\cdot +2$ nových. + +\>Platí-li: +\itemize\ibull +\:$\alpha \Rightarrow$ zvolíme $x = 0$ (zajistí splnìní druhé poloviny nové formule), +\:$\beta \Rightarrow$ zvolíme $x = 1$ (zajistí splnìní první poloviny nové formule), +\:$\alpha ,\beta / \lnot\alpha ,\lnot\beta \Rightarrow$ zvolíme $x = 0/1$ (je nám to + jedno, celkové øe¹ení nám to neovlivní). +\endlist + +Nabízí se otázka, proè mù¾eme pøidanou promìnnou $x$ nastavovat, jak se nám zlíbí. +Vysvìtlení je prosté -- promìnná $x$ nám pùvodní formuli nijak neovlivní, proto¾e +se v ní nevyskytuje, proto ji mù¾eme nastavit tak, jak chceme. + +\s{Poznámka:} U~3-SAT lze vynutit právì tøi literály, pro krátké klauzule +pou¾ijeme stejný trik: +$$(\alpha) \rightarrow (\alpha \vee \alpha) \rightarrow (\alpha \lor x) \land (\alpha \lor \lnot x).$$ + +\h{3. problém: Hledání nezávislé mno¾iny v grafu} + +\>Existuje nezávislá mno¾ina vrcholù z~$G$ velikosti alespoò $k$? + +\s{Definice:} {\I Nezávislá mno¾ina} (NzMna) budeme øíkat ka¾dé mno¾inì vrcholù grafu +takové, ¾e mezi nimi nevede ¾ádná hrana. + +\figure{nezmna.eps}{Pøíklad nezávislé mno¾iny}{1in} + +\>{\I Vstup:} Neorientovaný graf $G$, $k \in {\bb N}$. + +\>{\I Výstup:} $\exists A \subseteq V(G)$, $\vert A \vert \ge k$: $\forall u,v \in A \Rightarrow uv \not\in E(G)$? + +\s{Poznámka:} Ka¾dý graf má minimálnì jednu nezávislou mno¾inu, a tou je prázdná mno¾ina. Proto je potøeba zadat i minimální velikost hledané mno¾iny. + +\>Uká¾eme, jak na~tento probém pøevést 3-SAT. + +\s{Pøevod 3-SAT na NzMna:} Z ka¾dé klauzule vybereme jeden literál, jeho¾ nastavením se klauzuli +rozhodneme splnit. Samozøejmì tak, abychom v~rùzných klauzulích nevybírali +konfliktnì, tj.~$x$ a~$\lnot x$. + +\s{Pøíklad:} +$(x \lor y \lor z) \land (x \lor \lnot y \lor \lnot z) \land (\lnot x \lor \lnot y \lor p) $. + +\>Pro ka¾dou klauzuli sestrojíme graf (trojúhelník) a pøidáme \uv{konfliktní} +hrany, tj. $x$ a $\lnot x$. Poèet vrcholù grafu odpovídá poètu literálù ve formuli, +poèet hran je maximálnì kvadratický a pøevod je tedy polynomiální. + +Existuje-li v grafu nezávislá mno¾ina velikosti $k$, pak z~ka¾dého z~$k$ trojúhelníkù +vybere právì jeden vrchol, a pøitom ¾ádné dva vrcholy nebudou odpovídat literálu a +jeho negaci -- tedy dostaneme ohodnocení promìnných splòujících alespoò $k$ klauzulí. +Na druhou stranu, existuje-li ohodnocení $k$ klauzulí, pak pøímo odpovídá nezávislé +mno¾inì velikosti $k$ (v ka¾dém trojúhelníku zvolíme právì jednu z ohodnocených +promìnných, nemù¾e se stát ¾e zvolíme vrcholy konfliktní hrany). Ptáme-li se tedy +na nezávislou mno¾inu velikosti odpovídající +poètu klauzulí, dostaneme odpovìï {\sc ano} právì tehdy, kdy¾ je formule splnitelná. + +Jsou-li ve formuli i klauzule krat¹í ne¾ 3, mù¾eme je buïto prodlou¾it metodou vý¹e +popsanou; nebo si v grafu necháme dvoj- a jedno-úhelníky, které ¾ádné z na¹ich úvah +vadit nebudou. + +\figure{nezmna_graf.eps}{Ukázka pøevodu 3-SAT na nezávislou mno¾inu}{3in} + +\s{Pøevod NzMna na SAT:} + +\itemize\ibull +\:Poøídíme si promìnné $v_1, \ldots, v_n$ odpovídající vrcholùm grafu. Promìnná $v_i$ bude + indikovat, zda se $i$-tý vrchol vyskytuje v~nezávislé mno¾inì (tedy pøíslu¹né ohodnocení + promìnných bude vlastnì charakteristická funkce nezávislé mno¾iny). +\:Pro ka¾dou hranu $ij \in E(G)$ pøidáme klauzuli $(\lnot v_i \lor \lnot v_j)$. Tyto klauzule + nám ohlídají, ¾e vybraná mno¾ina je vskutku nezávislá. +\:Je¹tì potøebujeme zkontrolovat, ¾e je mno¾ina dostateènì velká, tak¾e si její prvky + oèíslujeme èísly od~1 do~$k$. Oèíslování popí¹eme maticí promìnných $x_{ij}$, pøièem¾ + $x_{ij}$ bude pravdivá právì tehdy, kdy¾ v~poøadí $i$-tý prvek nezávislé mno¾iny je vrchol~$v_j$ +-- pøidáme tedy klauzule, které nám øeknou, ¾e vybrané do nezávislé mno¾iny jsou právì + ty vrcholy, které jsou touto maticí oèíslované: $\forall i,j$, $x_{ij} \Rightarrow v_j$ + (jen dodejme, ¾e $a\Rightarrow b$ je definované jako $\neg a\vee b$). +\:Je¹tì potøebujeme zajistit, aby byla v~ka¾dém øádku i sloupci nejvý¹e jedna jednièka: + $\forall j,i,i^{'}, i\ne i^{'} : x_{ij} \Rightarrow \lnot x_{i^{'}j}$ a + $\forall i,j,j^{'}, j\ne j^{'} : x_{ij} \Rightarrow \lnot x_{ij^{'}}$. +\:A~nakonec si ohlídáme, aby v~ka¾dém øádku byla alespoò jedna jednièka, klauzulí $\forall i : + x_{i1} \lor x_{i2} \lor \ldots \lor x_{in}$. +\endlist +Tímto vynutíme NzMnu $\geq k$, co¾ jsme pøesnì chtìli. Takovýto pøevod je zøejmì polynomiální. + +\s{Pøíklad matice:} Jako pøíklad pou¾ijeme nezávislou mno¾inu z ukázky nezávislé mno¾iny. +Nech» jsou vrcholy grafu oèíslované zleva a zeshora. Hledáme nezávislou mno¾inu velikosti $2$. +Matice pak bude vypadat následovnì: +$$ \pmatrix{1&0&0&0&0 \cr 0&0&0&1&0}$$ +\s{Vysvìtlení:} Jako první vrchol mno¾iny bude vybrán vrchol $v_1$, proto v prvním +øádku a v prvním sloupci bude $1$. Jako druhý vrchol mno¾iny bude vybrán +vrchol $v_4$, proto na druhém øádku a ve ètvrtém sloupci bude $1$. Na ostatních místech bude $0$. + +\h{4. problém: Klika} + +\>{\I Vstup:} Graf $G, k \in N$. + +\>{\I Výstup:} $\exists$ úplný podgraf grafu $G$ na $k$ vrcholech? +\figure{klika.eps}{Pøíklad kliky}{2in} + +\s{Pøevod:} Prohodíme v grafu $G$ hrany a nehrany $\Rightarrow$ (hledání nezávislé mno¾iny $\leftrightarrow$ hledání kliky). + +\s{Dùvod:} Pokud existuje úplný graf na $k$ vrcholech, tak v~komplementárním grafu tyto vrcholy nejsou spojeny hranou, tj. tvoøí nezávislou mno¾inu, + a naopak. + +\figure{doplnek_nm.eps}{Prohození hran a nehran}{2in} + + +\h{5. problém: 3,3-SAT} +\s{Definice:} 3,3-SAT je speciální pøípad 3-SATu, kde ka¾dá promìnná se vyskytuje v~maximálnì tøech literálech. + +\s{Pøevod 3-SAT na 3,3-SAT:} +Pokud se promìnná $x$ vyskytuje v~$k > 3$ literálech, tak nahradíme výskyty novými promìnnými $x_1, \ldots , x_k$ a pøidáme klauzule: +$$ +(\lnot x_1 \lor x_2), +(\lnot x_2 \lor x_3), +(\lnot x_3 \lor x_4), +\ldots, +(\lnot x_{k-1} \lor x_k), +(\lnot x_k \lor x_1), +$$ + +co¾ odpovídá: + +$$ +(x_1 \Rightarrow x_2), +(x_2 \Rightarrow x_3), +(x_3 \Rightarrow x_4), +\ldots, +(x_{k-1} \Rightarrow x_k), +(x_k \Rightarrow x_1). +$$ + +Tímto zaruèíme, ¾e v¹echny nové promìnné budou mít stejnou hodnotu. + +Mimochodem, mù¾eme rovnou zaøídit, ¾e ka¾dý literál se vyskytuje nejvíce dvakrát (tedy ¾e +ka¾dá promìnná se vyskytuje alespoò jednou pozitivnì a alespoò jednou negativnì). Pokud by +se nìjaká promìnná objevila ve~tøech stejných literálech, mù¾eme na~ni +také pou¾ít ná¹ trik a nahradit ji tøemi promìnnými. V~nových klauzulích se pak bude +vyskytovat jak pozitivnì, tak negativnì. + +\h{6. problém: 3D párování (3D matching)} + +\>{\I Vstup:} Tøi mno¾iny, napø. $K$ (kluci), $H$ (holky), $Z$ (zvíøátka) a mno¾ina kompatibilních trojic (tìch, kteøí se spolu snesou). + +\>{\I Výstup:} Perfektní podmno¾ina trojic -- tj. taková podmno¾ina trojic, která obsahuje v¹echna $K$, $H$ a $Z$. + +\>Uká¾eme, jak na tento problém pøevést 3,3-SAT (ov¹em to a¾ na dal¹í pøedná¹ce). + +\figure{3d_parovani.eps}{Ukázka 3D párování}{3in} + +\s{Závìr:} Obrázek ukazuje problémy, jimi¾ jsme se dnes zabývali, a vztahy mezi tìmito problémy. +\figure{prevody.eps}{Pøevody mezi problémy}{3in} + +\bye diff --git a/old/10-prevody/3d_parovani.eps b/old/10-prevody/3d_parovani.eps new file mode 100644 index 0000000..22c2f66 --- /dev/null +++ b/old/10-prevody/3d_parovani.eps @@ -0,0 +1,2166 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: 0.45pre1 +%%Pages: 1 +%%Orientation: Portrait +%%BoundingBox: 0 0 504 241 +%%HiResBoundingBox: 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+closepath +eofill +grestore +0 0 0 setrgbcolor +[] 0 setdash +1.25 setlinewidth +0 setlinejoin +0 setlinecap +newpath +5.77 0 moveto +-2.88 5 lineto +-2.88 -5 lineto +5.77 0 lineto +closepath +stroke +grestore +grestore +grestore +showpage +%%EOF diff --git a/old/10-prevody/prevody.svg b/old/10-prevody/prevody.svg new file mode 100644 index 0000000..248553f --- /dev/null +++ b/old/10-prevody/prevody.svg @@ -0,0 +1,264 @@ + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + SAT + + + Nezávislámnožina + + + 3 SAT + + + 3,3 SAT + + + Klika + + + 3d párování + + + + + + + + diff --git a/old/11-np/11-np.tex b/old/11-np/11-np.tex new file mode 100644 index 0000000..c81d913 --- /dev/null +++ b/old/11-np/11-np.tex @@ -0,0 +1,334 @@ +\input lecnotes.tex + +\prednaska{11}{NP-úplné problémy}{\vbox{\hbox{(zapsali F. Kaèmarik, R. Krivák, D. Remi¹} + \hbox{ Michal Kozák, Vojta Tùma)}}} + +Dosud jsme zkoumali problémy, které se nás ptaly na to, jestli nìco existuje. +Napøíklad jsme dostali formuli a problém splnitelnosti se nás ptal, zda +existuje ohodnocení promìnných takové, ¾e formule platí. Nebo v~pøípadì +nezávislých mno¾in jsme dostali graf a èíslo $k$ a ptali jsme se, jestli +v~grafu existuje nezávislá mno¾ina, která obsahuje alespoò~$k$ vrcholù. +Tyto otázky mìly spoleèné to, ¾e kdy¾ nám nìkdo napovìdìl nìjaký objekt, umìli +jsme efektivnì øíci, zda je to ten, který hledáme. Napøíklad pokud dostaneme +ohodnocení promìnných logické formule, staèí jen dosadit a spoèítat, kde +formule dá \ nebo \. Zjistit, ¾e nìjaký objekt je ten, který +hledáme, umíme efektivnì. Tì¾ké na tom je takový objekt najít. Co¾ vede +k~definici obecných vyhledávacích problémù, kterým se øíká tøída problémù NP. +Nadefinujeme si ji poøádnì, ale nejdøíve zaèneme tro¹ièku jednodu¹¹í tøídou. + +\s{Definice:} P je {\I tøída rozhodovacích problémù}, které jsou øe¹itelné +v~polynomiálním èase. Jinak øeèeno, problém +$L \in {\rm P} \Leftrightarrow \exists $ polynom $f$ a~$\exists$ algoritmus~$A$ +takový, ¾e $\forall x: L(x)=A(x)$ a $A(x)$ dobìhne v~èase $\O(f(x))$. + +Tøída P tedy odpovídá tomu, o èem jsme se shodli, ¾e umíme efektivnì øe¹it. +Nadefinujme nyní tøídu NP: + +\s{Definice:} NP je {\I tøída rozhodovacích problémù} takových, ¾e $L \in {\rm NP}$ právì tehdy, kdy¾ $\exists $ problém +$K\in{\rm P}$ a $\exists$ polynom $g$ takový, ¾e pro +$\forall x$ platí $L(x)=1 \Leftrightarrow \exists $ nápovìda $ y: \vert y \vert \leq g(\vert x \vert)$ a souèasnì $K(x,y)=1$. + +\s{Pozorování:} Splnitelnost logických formulí je v~NP. Staèí si toti¾ nechat napovìdìt, jak +ohodnotit jednotlivé promìnné a pak ovìøit, jestli je formule splnìna. Nápovìda je polynomiálnì +velká (dokonce lineárnì), splnìní zkontrolujeme také v~lineárním èase. Odpovíme tedy ano právì +tehdy, existuje-li nápovìda, která nás pøesvìdèí, èili pokud je formule splnitelná. + +\s{Pozorování:} Tøída P le¾í uvnitø NP. +V~podstatì øíkáme, ¾e kdy¾ máme problém, který umíme øe¹it v~polynomiálním èase +bez nápovìdy, tak to zvládneme v~polynomiálním èase i s~nápovìdou. + +Problémy z minulé pøedná¹ky jsou v¹echny v NP (napø. pro nezávislou +mno¾inu je onou nápovìdou pøímo mno¾ina vrcholù deklarující nezávislost), +o jejich pøíslu¹nosti do P ale nevíme nic. +Brzy uká¾eme, ¾e to jsou v jistém smyslu nejtì¾¹í problémy v~NP. +Nadefinujme si: + +\s{Definice:} Problém $L$ je NP-{\I tì¾ký} právì tehdy, kdy¾ je na~nìj pøevoditelný +ka¾dý problém z~NP (viz definici pøevodù z minulé pøedná¹ky). + +Rozmyslete si, ¾e pokud umíme øe¹it nìjaký NP-tì¾ký problém v~polynomiálním èase, +pak umíme vyøe¹it v~polynomiálním èase v¹e v~NP, a tedy ${\rm P}={\rm NP}$. + +My se budeme zabývat problémy, které jsou NP-tì¾ké a samotné jsou v~NP. Takovým problémùm se øíká NP-úplné. + +\s{Definice:} Problém $L$ je NP-{\I úplný} právì tehdy, kdy¾ $L$ je NP-tì¾ký a $L \in {\rm NP}$. + +NP-úplné problémy jsou tedy ve~své podstatì nejtì¾¹í problémy, které le¾í v~NP. +Kdybychom umìli vyøe¹it nìjaký NP-úplný problém v~polynomiálním èase, pak +v¹echno v~NP je øe¹itelné v~polynomiálním èase. Bohu¾el to, jestli nìjaký +NP-úplný problém lze øe¹it v~polynomiálním èase, se neví. Otázka, jestli +${\rm P}={\rm NP}$, je asi nejznámìj¹í otevøený problém v~celé teoretické +informatice. + +Kde ale nìjaký NP-úplný problém vzít? K~tomu se nám bude velice hodit následující vìta: + +\s{Vìta (Cookova):} SAT je NP-úplný. + +\>Dùkaz je znaènì technický, pøibli¾nì ho naznaèíme pozdìji. Pøímým dùsledkem +Cookovy vìty je, ¾e cokoli v~NP je pøevoditelné na SAT. +K dokazování NP-úplnosti dal¹ích problémù pou¾ijeme následující vìtièku: + +\s{Vìtièka:} Pokud problém $L$ je NP-úplný a $L$ se dá pøevést na nìjaký problém $M\in{\rm NP}$, pak $M$ je také NP-úplný. + +\proof +Tuto vìtièku staèí dokázat pro NP-tì¾kost, NP-úplnost plyne okam¾itì z~toho, ¾e +problémy jsou NP-tì¾ké a le¾í v~NP (podle pøedpokladu). + +Víme, ¾e $L$ se dá pøevést na~$M$ nìjakou funkcí~$f$. Jeliko¾ $L$ je NP-úplný, +pak pro ka¾dý problém $Q\in{\rm NP}$ existuje nìjaká funkce~$g$, která pøevede +$Q$ na~$L$. Staèí tedy slo¾it funkci~$f$ s~funkcí~$g$, èím¾ získáme funkci pracující +opìt v~polynomiálním èase, která pøevede~$Q$ na~$M$. Ka¾dý problém z~NP se tedy +dá pøevést na problém~$M$. +\qed + +\s{Dùsledek:} Cokoliv, na co jsme umìli pøevést SAT, je také NP-úplné. +Napøíklad nezávislá mno¾ina, rùzné varianty SATu, klika v~grafu~\dots + +Jak taková tøída NP vypadá? Pøedstavme si v¹echny problémy tøídy NP, jakoby seøazené +shora dolu podle obtí¾nosti problémù (tedy navzdor gravitaci), kde porovnání dvou +problémù urèuje pøevoditelnost (viz obrázek). + +\figure{p-np.eps}{Struktura tøídy NP}{2.5cm} + +Obecnì mohou nastat dvì situace, proto¾e nevíme, jestli ${\rm P}={\rm NP}$. +Jestli ano, pak v¹echno je jedna a ta samá tøída. To by bylo v nìkterých +pøípadech nepraktické, napø. ka¾dá ¹ifra by byla jednodu¹e rozlu¹titelná. +\foot{Poznámka o ¹ifrách -- libovolnou funkci vyèíslitelnou v polynomiálním +èase bychom umìli v polynomiálním èase také invertovat.} +Jestli ne, NP-úplné problémy urèitì nele¾í v P, tak¾e P a NP-úplné problémy +jsou dvì disjunktní èásti NP. Také se dá dokázat (to dìlat nebudeme, ale je +dobré to vìdìt), ¾e je¹tì nìco le¾í mezi nimi, tedy ¾e existuje problém, který +je v~NP, není v~P a není NP-úplný (dokonce je takových problémù nekoneènì mnoho, +v nekoneènì tøídách). + +\s{Katalog NP-úplných problémù} + +Uká¾eme si nìkolik základních NP-úplných problémù. O~nìkterých jsme to dokázali +na~minulé pøedná¹ce, o~dal¹ích si to doká¾eme nyní, zbylým se na~zoubek podíváme +na~cvièeních. + +\itemize\ibull +\:{\I logické:} + \itemize\ibull + \:SAT (splnitelnost logických formulí v~CNF) + \:3-SAT (ka¾dá klauzule obsahuje max.~3 literály) + \:3,3-SAT (a navíc ka¾dá promìnná se vyskytuje nejvý¹e tøikrát) + \:SAT pro obecné formule (nejen CNF) + \:Obvodový SAT (není to formule, ale obvod) + \endlist +\:{\I grafové:} + \itemize\ibull + \:Nezávislá mno¾ina (existuje mno¾ina alespoò~$k$ vrcholù taková, ¾e ¾ádné dva nejsou propojeny hranou?) + \:Klika (existuje úplný podgraf na~$k$ vrcholech?) + \:3D párování (tøi mno¾iny se zadanými trojicemi, existuje taková mno¾ina disjunktních trojic, ve~které jsou v¹echny prvky?) + \:Barvení grafu (lze obarvit vrcholy $k$~barvami tak, aby vrcholy stejné barvy nebyly nikdy spojeny hranou? NP-úplné u¾ pro~$k=3$) + \:Hamiltonovská cesta (cesta obsahující v¹echny vrcholy [právì jednou]) + \:Hamiltonovská kru¾nice (kru¾nice, která nav¹tíví v¹echny vrcholy [právì jednou]) + \endlist +\:{\I èíselné:} + \itemize\ibull + \:Batoh (nejjednodu¹¹í verze: dána mno¾ina èísel, zjistit, zda existuje podmno¾ina se zadaným souètem) + \:Batoh -- optimalizace (podobnì jako u pøedchozího problému, ale místo mno¾iny èísel máme mno¾inu + pøedmìtù s vahami a cenami, chceme co nejdra¾¹í podmno¾inu její¾ váha nepøesáhne zadanou kapacitu + batohu) + \:Loupe¾níci (lze rozdìlit danou mno¾inu èísel na~dvì podmno¾iny se stejným souètem) + \:$Ax=b$ (soustava celoèíslených lineárních rovnic; $x_i$ mohou být pouze 0 nebo 1; NP-úplné i pokud $A_{ij}\in\{0,1\}$ a $b_i\in\{0,1\}$) + \:Celoèíselné lineární programování (existuje vektor nezáporných celoèísených $x$ takový, ¾e $Ax \leq b$ ?) + \endlist +\endlist + +Nyní si uká¾eme, jak pøevést SAT na nìjaký problém. Kdy¾ chceme ukázat, ¾e na +nìco se dá pøevést SAT, potøebujeme obvykle dvì vìci: +konstrukci, která bude simulovat promìnné, tedy nìco, co nabývá dvou stavù +\/\; a nìco, co bude reprezentovat klauzule a umí zaøídit, aby +ka¾dá klauzule byla splnìna alespoò jednou promìnnou. +Roz¹iøme tedy ná¹ katalog problémù o následující: +\h{3D párování (3D matching)} + +\>{\I Vstup:} Tøi mno¾iny, napø. $K$ (kluci), $H$ (holky), $Z$ (zvíøátka) a +mno¾ina $T$ kompatibilních trojic (tìch, kteøí se spolu snesou), + tj. $T \subseteq K\times H\times Z$. + +\>{\I Výstup:} Perfektní podmno¾ina trojic $P\subseteq K\times H \times Z$ -- + tj. taková podmno¾ina trojic, ¾e $(\forall k\in K\ \exists !p\in P: k\in p) + \wedge(\forall h\in H\ \exists !p\in P: h\in p) + \wedge(\forall z\in Z\ \exists !p\in P: z\in p)$ -- tedy ka¾dý byl vybrán + právì jednou. + + +\h{ Uká¾eme, jak na 3D-párování pøevést 3,3-SAT } + +Uva¾ujme takovouto konfiguraci: + +\fig{3d.eps}{4cm} + +\>4 zvíøátka, 2 kluci, 2 dívky a 4 trojice, které oznaèíme $A, B, C, D$. +Je¹tì pøedpokládáme, ¾e zvíøátka + se mohou úèastnit nìjakých jiných trojic, ale +tito ètyøi lidé se vyskytují pouze v~tìchto ètyøech trojicích a~nikde jinde. +V¹imneme si, ¾e existují právì dvì mo¾nosti, jak tento obrázek spárovat. +Abychom spárovali kluka $k_1$, tak si musíme vybrat $A$ nebo $B$. Kdy¾ si +vybereme $A$, $k_1$ i $d_2$ u¾ jsou spárovaní tak¾e si nesmíme vybrat $B$ ani +$D$. Pak jediná mo¾nost, jak spárovat $d_1$ a~$k_2$ je $C$. Jedna mo¾nost je +tedy vybrat si $A$ a $C$ a jeliko¾ je obrázek symetrický, tak kdy¾ vybereme +místo $A$ trojici $B$, dostaneme $B$ a~$D$. V¾dy si tedy vybereme dvì protìj¹í +trojice v~obrázku. + +Tyto konfigurace budeme pou¾ívat k~reprezentaci promìnných. Pro ka¾dou +promìnnou si nakreslíme takový obrázek a~to, ¾e $A$ bude spárované s~$C$, bude +odpovídat tomu, ¾e $x=1$, a~spárování $B$ a~$D$ bude odpovídat + $x=0$. Pokud jsme +pou¾ili $A$ a~$C$, zvíøata se sudými èísly, tj. $z_2$ a~$z_4$, horní a~dolní, +jsou nespárovaná a~pokud jsme pou¾ili $B$ a~$D$, zvíøátka $z_1$ a~$z_3$ zùstala +nespárovaná. Pøes tato nespárovaná zvíøátka mù¾eme pøedávat informaci, jestli +promìnná $x$ má hodnotu \ nebo \ do dal¹ích èástí vstupu. + +Zbývá vymyslet, jak reprezentovat klauzule. Klauzule jsou trojice popø. dvojice +literálù, napø. $\kappa = (x \lor y \lor \lnot r)$, kde +potøebujeme zajistit, aby $x$ bylo nastavené na $1$ nebo $y$ bylo nastavené +na~$1$ nebo $r$ na $0$. + +\fig{klauzule.eps}{4cm} + +\>Pro takovouto klauzuli si poøídíme dvojici kluk-dívka, kteøí budou figurovat +ve tøech trojicích se tøemi rùznými zvíøátky, co¾ mají být volná zvíøátka +z~obrázkù pro pøíslu¹né promìnné (podle toho, má-li se promìnná vyskytnout +s negací nebo ne). A~zaøídíme to tak, aby ka¾dé zvíøátko bylo +pou¾ité maximálnì v~jedné takové trojici, co¾ jde proto, ¾e ka¾dý literál se +vyskytuje maximálnì dvakrát a~pro ka¾dý literál máme dvì volná zvíøátka, +z~èeho¾ plyne, ¾e zvíøátek je dost pro v¹echny klauzule. Pro dvojice se postupuje +obdobnì. + +Je¹tì nám ale urèitì zbude $2p-k$ zvíøátek, kde $p$ je poèet promìnných, $k$ +poèet klauzulí -- ka¾dá promìnná vyrobí 4 zvíøátka, klauzule zba¹tí jedno +a samotné ohodnocení 2 zvíøátka -- tak pøidáme je¹tì $2p-k$ párù +kluk-dìvèe, kteøí milují +v¹echna zvíøátka, a~ti vytvoøí zbývající trojice. + +Pokud formule byla splnitelná, pak ze splòujícího ohodnocení mù¾eme vyrobit +párování s~na¹í konstrukcí. Obrázek pro ka¾dou promìnnou spárujeme podle +ohodnocení, tj. promìnná je $0$ nebo $1$ a~pro ka¾dou klauzuli si vybereme +nìkterou z~promìnných, kterými je ta klauzule splnìna. Funguje to ale +i~opaènì: Kdy¾ nám nìkdo dá párovaní v~na¹í konstrukci, pak z nìho doká¾eme +vyrobit splòující ohodnocení dané formule. Podíváme se, v~jakém stavu je +promìnná, a~to je v¹echno. Z~toho, ¾e jsou správnì spárované klauzule, u¾ +okam¾itì víme, ¾e jsou v¹echny splnìné. + +Zbývá ovìøit, ¾e na¹e redukce funguje v~polynomiálním èase. Pro ka¾dou klauzuli +spotøebujeme konstantnì mnoho èasu, $2p-k$ je také polynomiálnì mnoho a~kdy¾ v¹e +seèteme, máme polynomiální èas vzhledem k~velikosti vstupní formule. Tím je +pøevod hotový a~mù¾eme 3D-párování zaøadit mezi NP-úplné problémy. + + +%RK + + +\h{Náznak dùkazu Cookovy vìty} + +Abychom mohli budovat teorii NP-úplnosti, potøebujeme alespoò jeden problém, +o kterém doká¾eme, ¾e je NP-úplný, z definice. Cookova vìta øíká o NP-úplnosti +SAT-u, ale nám se to hodí dokázat o tro¹ku jiném problému -- {\I obvodovém SAT-u}. + +\>{\I Obvodový SAT} je splnitelnost, která nepracuje s~formulemi, ale s~booleovskými +obvody (hradlovými sítìmi). Ka¾dá formule se dá pøepsat do booleovského obvodu, +který ji poèítá, tak¾e dává smysl zavést splnitelnost i pro obvody. Na¹e obvody +budou mít nìjaké vstupy a~jenom jeden výstup. Budeme se ptát, jestli se vstupy +tohoto obvodu dají nastavit tak, abychom na výstupu dostali \. + +\>Nejprve doká¾eme NP-úplnost {\I obvodového SAT-u} a~pak uká¾eme, ¾e se dá +pøevést na obyèejný SAT v~CNF. Tím bude dùkaz Cookovy vìty hotový. Zaènìme +s pomocným lemmatem. + +\s{Lemma:} Nech» $L$ je problém v P. Potom existuje polynom $p$ a algoritmus, +který pro $\forall n \ge 0$ spoète v èase $p(n)$ hradlovou sí» $Bn$ s $n$ vstupy +a 1 výstupem takovou, ¾e $\forall x \in \{ 0, 1 \}^n : Bn(x) = L(x)$. + +\proof +Náznakem. Na základì zku¹eností z Principù poèítaèù intuitivnì chápeme poèítaèe +jako nìjaké slo¾ité booleovské obvody, jejich¾ stav se mìní v~èase. Uva¾me tedy +nìjaký problém $L \in {\rm P}$ a polynomiální algoritmus, který ho øe¹í. Pro vstup +velikosti~$n$ dobìhne v~èase~$T$ polynomiálním v~$n$ a spotøebuje $\O(T)$ bunìk pamìti. +Staèí nám tedy \uv{poèítaè s~pamìtí velkou $\O(T)$}, co¾ je nìjaký booleovský obvod +velikosti polynomiální v~$T$, a~tedy i v~$n$. Vývoj v~èase o¹etøíme tak, ¾e sestrojíme~$T$ +kopií tohoto obvodu, ka¾dá z~nich bude odpovídat jednomu kroku výpoètu a bude +propojena s~\uv{minulou} a \uv{budoucí} kopií. Tím sestrojíme booleovský obvod, +který bude øe¹it problém~$L$ pro vstupy velikosti~$n$ a bude polynomiálnì velký +vzhledem k~$n$. + +\s{Poznámka:} +Pro dùkaz následující vìty si dovolíme drobnou úpravu v~definici tøídy NP. +Budeme chtít, aby nápovìda +mìla pevnou velikost, závislou pouze na~velikosti vstupu (tedy: $\vert y \vert += g(\vert x \vert)$ namísto $\vert y \vert \le g(\vert x \vert)$). Proè je taková +úprava BÚNO? Jistì si dovedete pøedstavit, +¾e pùvodní nápovìdu doplníme na po¾adovanou délku nìjakými \uv{mezerami}, které +program ignoruje. (Tedy upravíme program tak, aby mu nevadilo, ¾e dostane na +konci nápovìdy nìjak kódované mezery.) + +\s{Vìta:} Obvodový SAT je NP-úplný. + +\proof +Máme tedy nìjaký problém $L$ z~NP a~chceme dokázat, ¾e $L$ se dá pøevést +na~obvodový SAT (tj. NP-tì¾kost). Kdy¾ nám nìkdo pøedlo¾í nìjaký vstup $x$ +(chápeme jako posloupnost $x_1, x_2, \ldots, x_n$), +spoèítáme velikost nápovìdy $g(n)$. Víme, ¾e kontrolní +algoritmus~$K$ (který kontroluje, zda nápovìda je správnì) je v~P. Vyu¾ijeme +pøedchozí lemma, abychom získali obvod, který pro konkrétní velikost vstupu +$x$ poèítá to, co kontrolní algoritmus $K$. Na vstupu tohoto obvodu bude $x$ +(vstup problému $L$) a~nápovìda~$y$. Na výstupu nám øekne, jestli je nápovìda +správná. Velikost vstupu tohoto obvodu bude tedy $p(g(n))$, co¾ je také polynom. + +\fig{kobvod.eps}{2.3cm} + +\>V tomto obvodu zafixujeme vstup $x$ (na místa vstupu dosadíme konkrétní hodnoty z $x$). Tím získáme obvod, jeho¾ vstup je jen $y$ a~ptáme se, zda za $y$ mù¾eme dosadit nìjaké hodnoty tak, aby na výstupu bylo \. Jinými slovy, ptáme se, zda je tento obvod splnitelný. + +\>Pro libovolný problém z~NP tak doká¾eme sestrojit funkci, která pro ka¾dý vstup~$x$ v~polynomiálním èase vytvoøí obvod, který je splnitelný pravì tehdy, kdy¾ odpovìï tohoto problému na vstup $x$ má být \. Tedy libovolný problém z~NP se dá +v~polynomiálním èase pøevést na obvodový SAT. + +\>Obvodový SAT je v NP triviálnì -- staèí si nechat poradit vstup, sí» +topologicky setøídit a v~tomto poøadí poèítat hodnoty hradel. +\qed + +\s{Lemma:} Obvodový SAT se dá pøevést na 3-SAT. + +\proof +Budeme postupnì budovat formuli v~konjunktivní normální formì. Ka¾dý booleovský obvod se dá pøevést v~polynomiálním èase na~ekvivalentní obvod, ve~kterém se vyskytují jen hradla {\sc and} a {\sc not}, tak¾e staèí najít klauzule odpovídající tìmto hradlùm. Pro ka¾dé hradlo v~obvodu zavedeme novou promìnnou popisující jeho výstup. Pøidáme klauzule, které nám kontrolují, ¾e toto hradlo máme ohodnocené konzistentnì. + +\>{\I Pøevod hradla \sc not}: na vstupu hradla budeme mít nìjakou promìnnou $x$ (která pøi¹la buïto pøímo ze~vstupu toho celého obvodu nebo je to promìnná, která vznikla na výstupu nìjakého hradla) a na výstupu promìnnou $y$. Pøidáme klauzule, které nám zaruèí, ¾e jedna promìnná bude negací té druhé: +$$\matrix{ (x \lor y), \cr + (\neg{x} \lor \neg{y}). \cr } + \hskip 0.2\hsize +\vcenter{\hbox{\epsfxsize=0.7cm\epsfbox{not.eps}}} +$$ + +\>{\I Pøevod hradla \sc and}: Hradlo má vstupy $x, y$ a~výstup $z$. Potøebujeme pøidat klauzule, které nám popisují, jak se má hradlo {\sc and} chovat. Tyto vztahy pøepí¹eme do~konjunktivní normální formy: +$$ +\left. \matrix{ + x\ \&\ y \Rightarrow z \cr + \neg{x} \Rightarrow \neg{z} \cr + \neg{y} \Rightarrow \neg{z} \cr +} +\ \quad + \right\} +\quad +\matrix{ + (z \lor \neg{x} \lor \neg{y}) \cr + (\neg{z} \lor x) \cr + (\neg{z} \lor y) \cr + } + \hskip 0.1\hsize +\vcenter{\hbox{\epsfxsize=0.7cm\epsfbox{and.eps}}} +$$ + +\>Kdy¾ chceme pøevádìt obvodový SAT na 3-SAT, obvod nejdøíve pøelo¾íme na takový, ve~kterém jsou jen hradla {\sc and} a~{\sc not}, a~pak hradla tohoto obvodu pøelo¾íme na klauzule. Formule vzniklá z~takovýchto klauzulí je splnitelná pravì tehdy, kdy¾ je splnitelný daný obvod. Pøevod pracuje v polynomiálním èase. +\qed + +\s{Poznámka:} +Kdy¾ jsme zavádìli SAT, omezili jsme se jen na formule, které jsou +v~konjunktivní normální formì (CNF). Teï u¾ víme, ¾e splnitelnost obecné +booleovské formule doká¾eme pøevést na obvodovou splnitelnost a tu pak +pøevést na 3-SAT. Opaèný pøevod je samozøejmì triviální, tak¾e obecný SAT +je ve~skuteènosti ekvivalentní s~na¹ím \uv{standardním} SATem pro CNF. + +\bye + diff --git a/old/11-np/3d.eps b/old/11-np/3d.eps new file mode 100644 index 0000000..755fbcc --- /dev/null +++ b/old/11-np/3d.eps @@ -0,0 +1,641 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: 0.45.1 +%%Pages: 1 +%%Orientation: Portrait +%%BoundingBox: 1 1 234 233 +%%HiResBoundingBox: 1.5714774 1.6 233.87693 232.675 +%%EndComments +%%Page: 1 1 +0 842 translate +0.8 -0.8 scale +0 0 0 setrgbcolor +[] 0 setdash +1 setlinewidth +0 setlinejoin +0 setlinecap +gsave [1 0 0 1 0 0] concat +gsave +0 0 0 setrgbcolor +newpath +147.56738 996.36218 moveto +142.78136 988.61727 lineto +137.5 980.14292 lineto +147.03962 980.12535 lineto +157.5 980.0699 lineto +152.7464 987.83238 lineto +147.56738 996.36218 lineto +closepath +fill +grestore +gsave +0 0 0 setrgbcolor +newpath +147.43262 812.0699 moveto +152.21864 819.81481 lineto +157.5 828.28916 lineto +147.96038 828.30673 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b/old/12-apx/12-apx.tex new file mode 100644 index 0000000..d2583f3 --- /dev/null +++ b/old/12-apx/12-apx.tex @@ -0,0 +1,299 @@ +\input lecnotes.tex +\prednaska{12}{Aproximaèní algoritmy}{\vbox{\hbox{(F. Ha¹ko, J. Menda, M. Mare¹,} + \hbox{ Michal Kozák, Vojta Tùma)}}} + +\>Na~minulých pøedná¹kách jsme se zabývali rùznými tì¾kými rozhodovacími +problémy. Tato se zabývá postupy, jak se v~praxi vypoøádat s~øe¹ením tìchto +problémù. + +\h{Co dìlat, kdy¾ potkáme NP-úplný problém} +\algo +\:Nepanikaøit. +\:Spokojit se s~málem. +\:Rozmyslet, jestli opravdu potøebujeme obecný algoritmus. Mnohdy potøebujeme pouze +speciálnìj¹í pøípady, které mohou být øe¹itelné v~polynomiálním èase. +\:Spokojit se s~pøibli¾ným øe¹ením, (pou¾ít aproximaèní algoritmus). +\:Pou¾ít heuristiku -- napøíklad genetické algoritmy nebo randomizované algoritmy. +Velmi pomoci mù¾e i jen výhodnìj¹í poøadí pøi~prohledávání èi oøezávání nìkterých +napohled nesmyslných vìtví výpoètu. +\endalgo + +\h{První zpùsob: Speciální pøípad} + +\>Èasto si vystaèíme s~vyøe¹ením speciálního pøípadu NP-úplného problému, který +le¾í v~$P$. Napøíklad pøi øe¹ení grafové úlohy nám mù¾e staèit øe¹ení +pro~speciální druh grafù (stromy, bipartitní grafy, \dots). Barvení grafu je lehké +napø. pro~dvì barvy èi pro intervalové grafy. 2-SAT, jako speciální pøípad SATu, +se dá øe¹it v~lineárním èase. + +\>Uká¾eme si dva takové pøípady (budeme øe¹ení hledat, nejen rozhodovat, zda existuje) + +\s{Problém: Maximální nezávislá mno¾ina ve~stromì (ne rozhodovací)} + +\>{\I Vstup:} Zakoøenìný strom~$T$. + +\>{\I Výstup:} Maximální (co do~poètu vrcholù) nezávislá mno¾ina vrcholù~$M$~v~$T$. + +\>BÚNO mù¾eme pøedpokládat, ¾e v~$M$ jsou v¹echny listy $T$. Pokud by nìkterý +list $l$ v~$M$ nebyl, tak se podíváme na~jeho otce: +\itemize\ibull +\:Pokud otec není v~$M$, tak list $l$ pøidáme do~$M$, èím¾ se nezávislost +mno¾iny zachovala a velikost stoupla o~1. +\:Pokud tam otec je, tak ho z~$M$ vyjmeme a na~místo nìho vlo¾íme $l$. +Nezávislost ani velikost $M$ se nezmìnily. +\endlist +\>Nyní listy spolu s~jejich otci z~$T$ odebereme a postup opakujeme. $T$ se +mù¾e rozpadnout na~les, ale to nevadí $\to$ tentý¾ postup aplikujeme na~v¹echny stromy v~lese. + +\s{Algoritmus:} +\>MaxNz$(T)$ +\algo +\:Polo¾íme $L$:=$\{$listy stromu $T\}$. +\:Polo¾íme $O$:=$\{$otcové vrcholù z~$L\}$. +\:Vrátíme $L \cup$ MaxNz$(T\setminus(O \cup L))$. +\endalgo +\>{\I Poznámka:} Toto doká¾eme naprogramovat v~$\O(n)$ (udr¾ujeme si frontu listù). + +\s{Problém: Batoh} + +\>Je daná mno¾ina $n$~pøedmìtù s~hmotnostmi $h_1,\ldots,h_n$ +a cenami $c_1,\ldots,c_n$ a~batoh, který unese hmostnost~$H$. Najdìte takovou +podmno¾inu pøedmìtù, jejich¾ celková hmotnost je maximálnì $H$ a celková cena +je maximální mo¾ná. + +\>Tento problém je zobecnìním problému batohu z~minulé pøedná¹ky dvìma smìry: +Jednak místo rozhodovacího problému øe¹íme optimalizaèní, jednak pøedmìty +mají ceny (pøedchozí verze odpovídala tomu, ¾e ceny jsou rovny hmotnostem). +Uká¾eme si algoritmus pro øe¹ení tohoto obecného problému, jeho¾ èasová +slo¾itost bude polynomiální v~poètu pøedmìtù~$n$ a souètu v¹ech cen~$C=\sum_i +c_i$. + +\>Pou¾ijeme dynamické programování. Pøedstavme si problém omezený na~prvních~$k$ +pøedmìtù. Oznaème si $A_k(c)$ (kde $0\le c\le C$) minimální hmotnost +podmno¾iny, její¾ cena je právì~$c$. Tato $A_k$ spoèteme indukcí podle~$k$: +Pro $k=0$ je urèitì $A_0(0)=0$, $A_0(c)=infty$ pro $c>0$. Pokud ji¾ známe +$A_{k-1}$, spoèítáme $A_k$ následovnì: $A_k(c)$ odpovídá nìjaké podmno¾inì +pøedmìtù z~$1,\ldots,k$. V~této podmno¾inì jsme buïto $k$-tý pøedmìt nepou¾ili +(a pak je $A_k(c)=A_{k-1}(c)$), nebo pou¾ili a tehdy bude $A_k(c) = +A_{k-1}(c-c_k) + h_k$ (to samozøejmì jen pokud $c\ge c_k$). Z~tìchto dvou +mo¾ností si vybereme tu, která dává mno¾inu s~men¹í hmotností. Tedy: +$$ +A_k(c) = \min (A_{k-1}(c), A_{k-1}(c-c_k) + h_k). +$$ +Tímto zpùsobem v~èase $\O(C)$ spoèteme $A_k(c)$ pro fixní $k$ a v¹echna $c$, +v~èase $\O(nC)$ pak v¹echny $A_k(c)$. + +\>Podle $A_n$ snadno nalezneme maximální cenu mno¾iny, která se vejde do~batohu. +To bude nejvìt¹í~$c^*$, pro nì¾ je $A_n(c^*) \le H$. Jeho nalezení nás stojí +èas $\O(C)$. + +\>A~jak zjistit, které pøedmìty do~nalezené mno¾iny patøí? Upravíme algoritmus, +aby si pro ka¾dé $A_k(c)$ pamatoval $B_k(c)$, co¾ bude index posledního pøedmìtu, +který jsme do~pøíslu¹né mno¾iny pøidali. Pro nalezené $c^*$ tedy bude $i=B_n(c^*)$ +poslední pøedmìt v~nalezené mno¾inì, $i'=B_{i-1}(c^*-c_i)$ ten pøedposlední +a tak dále. Takto v~èase $\O(n)$ rekonstruujeme celou mno¾inu od~posledního +prvku k~prvnímu. + +\>Ukázali jsme tedy algoritmus s~èasovou slo¾itostí $\O(nC)$, který vyøe¹í +problém batohu. Jeho slo¾itost není polynomem ve~velikosti vstupu ($C$~mù¾e +být a¾ exponenciálnì velké vzhledem k~velikosti vstupu), ale pouze ve~velikosti +èísel na~vstupu. Takovým algoritmùm se øíká {\I pseudopolynomiální.} Ani takové +algoritmy ale nejsou k dispozici pro v¹echny problémy (napø. u problému obchodního +cestujícího nám vùbec nepomù¾e, ¾e váhy hran budou malá èísla). + +\s{Verze bez cen:} Na verzi s~cenami rovnými hmotnostem se dá pou¾ít +i jiný algoritmus zalo¾ený na~dynamickém programování: poèítáme mno¾iny +$Z_k$ obsahující v¹echny hmotnosti men¹í ne¾~$H$, kterých nabývá +nìjaká podmno¾ina prvních~$k$ prvkù. Pøitom $Z_0=\{0\}$, $Z_k$ +spoèteme ze~$Z_{k-1}$ --- udr¾ujme si $Z_{k-1}$ jako setøídìný spojový seznam, +výpoèet dal¹ího seznamu udìláme slitím dvou seznamù $Z_{k-1}$ a $Z_{k-1}$ se +v¹emi prvky zvý¹enými o hmotnost $k$ zahazujíce duplicitní a pøíli¹ velké hodnoty --- +a ze~$Z_n$ vyèteme výsledek. V¹echny tyto mno¾iny +mají nejvý¹e $H$ prvkù, tak¾e celková èasová slo¾itost algoritmu je~$\O(nH)$. + +\h{Druhý zpùsob: Aproximace} + +\>V pøedcházejících problémech jsme se zamìøili na~speciální pøípady. Obèas v¹ak +takové ¹tìstí nemáme a musíme vyøe¹it celý NP-úplný problém. Mù¾eme si v¹ak +pomoct tím, ¾e se ho nebudeme sna¾it vyøe¹it optimálnì -- namísto optimálního +øe¹ení najdeme nìjaké, které je nejvý¹e $c$-krát hor¹í pro nìjakou konstantu $c$. + +\s{Problém: Obchodní cestující} + +\>{\I Vstup:} Neorientovaný graf~$G$, ka¾dá hrana +je ohodnocená funkcí $w: E(G)\rightarrow {\bb R }^+_0$. + +\>{\I Výstup:} Hamiltonovská kru¾nice (obsahující v¹echny vrcholy grafu), a~to ta nejkrat¹í +(podle ohodnocení). + +\>Tento problém je hned na~první pohled nároèný -- u¾ sama existence +hamiltonovské kru¾nice je NP-úplná. Najdeme aproximaèní algoritmus nejprve za pøedpokladu, +¾e vrcholy splòují trojúhelníkovou nerovnost (tj. $\forall x,y,z \in V: w(xz)\le +w(xy)+w(yz)$), potom uká¾eme, ¾e v úplnì obecném pøípadé by samotná existence +aproximaèního algoritmu implikovala ${\rm P=NP }$. + +\>{\I a) trojúhelníková nerovnost:} + +Existuje pìkný algoritmus, který najde hamiltonovskou kru¾nici o délce $\leq +2\cdot opt$, kde $opt$ je délka nejkrat¹í hamiltonovské kru¾nice. +Vedle pøedpokladu trojúhelníkové +nerovnosti budeme potøebovat, aby ná¹ graf byl úplný. Souhrnnì mù¾eme +pøedpokládat, ¾e úlohu øe¹íme v nìjakém metrickém protoru, ve kterém jsou obì +podmínky podle definice splnìny. + +Najdeme nejmen¹í kostru grafu a obchodnímu cestujícímu poradíme, a» jde po~ní -- kostru +zakoøeníme a projdeme jako strom do hloubky, pøièem¾ se zastavíme a¾ v koøeni po projití +v¹ech vrcholù. Problém v¹ak je, ¾e prùchod po kostøe obsahuje +nìkteré vrcholy i hrany vícekrát, a proto musíme nahradit nepovolené vracení se. +Máme-li na nìjaký vrchol vstoupit podruhé, prostì ho ignorujeme a pøesuneme se +rovnou na dal¹í nenav¹tívený -- dovolit si to mù¾eme, graf je úplný a obsahuje +hrany mezi v¹emi dvojicemi vrcholù +(jinak øeèeno, poøadí vrcholù kru¾nice bude preorder výpis prùchodem do hloubky). +Pokud platí trojúhelníková nerovnost, tak si tìmito zkratkami neu¹kodíme. +Nech» minimální kostra má váhu~$T$. Pokud bychom pro¹li celou kostru, bude mít +sled váhu~$2T$ (ka¾dou hranou kostry jsme ¹li tam a zpátky), a pøeskakování +vrcholù celkovou váhu nezvìt¹uje (pøi pøeskoku +nahradíme cestu $xyz$ jedinou hranou $xz$, pøièem¾ z trojúhelníkové nerovnosti +máme $xz \leq xy + xz$), tak¾e váha nalezené +hamiltonovské kru¾nice bude také nanejvý¹ $2T$. + +Kdy¾ máme hamiltonovskou kru¾nici $C$ a z~ní vy¹krtneme hranu, dostaneme kostru +grafu~$G$ s~váhou men¹í ne¾ $C$ -- ale ka¾dá kostra je alespoò tak tì¾ká +jako minimální kostra $T$. Tedy optimální hamiltonovská kru¾nice je urèitì tì¾¹í +ne¾ minimální kostra $T$. Kdy¾ tyto dvì nerovnosti slo¾íme +dohromady, algoritmus nám vrátí hamiltonovskou kru¾nici $T'$ s~váhou nanejvý¹ +dvojnásobnou vzhledem k optimální hamiltonovské kru¾nici ($T' \leq 2T < 2C$). Takovéto +algoritmy se nazývají {\I 2-aproximaèní}, kdy¾ øe¹ení je maximálnì dvojnásobné +od~optimálního.\foot{Hezkým trikem se v obecných metrických prostorech umí +$1{,}5$-aproximace. Ve~nìkterých metrických prostorech (tøeba v euklidovské +rovinì) se aproximaèní pomìr dá dokonce srazit na +libovolnì blízko k 1. Zaplatíme ale na èase -- èím pøesnìj¹í výsledek +po algoritmu chceme, tím déle to bude trvat.} + +\>{\I b) bez~trojúhelníkové nerovnosti:} + +Zde se budeme naopak sna¾it ukázat, ¾e ¾ádný polynomiální aproximaèní +algoritmus neexistuje. + +\s{Vìta:} Pokud pro~libovolné~$\varepsilon>0$ existuje polynomiální +$(1+\varepsilon)$-aproximaèní algoritmus pro~problém obchodního cestujícího bez~trojúhelníkové nerovnosti, tak ${\rm P = NP }$. + +\proof Uká¾eme, ¾e v~takovém pøípadì doká¾eme v~polynomiálním èase zjistit, +zda v grafu existuje hamiltonovská kru¾nice. + +\>Dostali jsme graf~$G$, ve~kterém hledáme hamiltonovskou kru¾nici. Doplníme +$G$ na~úplný graf~$G'$ a~váhy hran~$G'$ nastavíme takto: +\itemize\ibull +\: $w(e) = 1$, kdy¾ $e \in E(G)$ +\: $w(e) = c \gg 1$, kdy¾ $e \not\in E(G)$ +\endlist +\>Konstantu $c$ potøebujeme zvolit tak velkou, abychom jasnì poznali, jestli +je ka¾dá hrana z nalezené hamiltonovské kru¾nice hranou grafu $G$ (pokud by +nebyla, bude kru¾nice obsahovat aspoò jednu hranu s váhou $c$, která vy¾ene +souèet poznatelnì vysoko). Pokud existuje hamiltonovská kru¾nice v~$G'$ slo¾ená jen +z~hran, které byly +pùvodnì v~$G$, pak optimální øe¹ení bude mít váhu~$n$, jinak bude urèitì +minimálnì $n-1+c$. Kdy¾ máme aproximaèní algoritmus s~pomìrem~$1+\varepsilon$, +musí tedy být +$$ +\eqalign{ +(1+\varepsilon)\cdot n &< n-1+c \cr +\varepsilon n+1 &< c +} +$$ +\>Kdyby takový algoritmus existoval, máme polynomiální algoritmus +na~hamiltonovskou kru¾nici. +\qed + +\s{Poznámka:} O existenci pseudopolynomiálního algoritmu +platí analogická vìta, a doká¾e se analogicky -- existující hrany budou +mít váhu 1, neexistující váhu 2. + +\h{Aproximaèní schéma pro problém batohu} + +Ji¾ víme, jak optimalizaèní verzi problému batohu vyøe¹it v~èase $\O(nC)$, +pokud jsou hmotnosti i ceny na~vstupu pøirozená èísla a $C$ je souèet v¹ech cen. +Jak si poradit, pokud je~$C$ obrovské? Kdybychom mìli ¹tìstí a v¹echny +ceny byly dìlitelné nìjakým èíslem~$p$, mohli bychom je tímto èíslem +vydìlit. Tím bychom dostali zadání s~men¹ími èísly, jeho¾ øe¹ením by byla +stejná mno¾ina pøedmìtù jako u~zadání pùvodního. + +Kdy¾ nám ¹tìstí pøát nebude, mù¾eme pøesto zkusit ceny vydìlit a výsledky +nìjak zaokrouhlit. Øe¹ení nové úlohy pak sice nebude pøesnì odpovídat optimálnímu +øe¹ení té pùvodní, ale kdy¾ nastavíme parametry správnì, bude alespoò jeho dobrou aproximací. + +\s{Základní my¹lenka:} + +Oznaèíme si $c_{max}$ maximum z~cen~$c_i$. Zvolíme si nìjaké pøirozené èíslo~$M < c_{max}$ +a zobrazíme interval cen $[0, c_{max}]$ na $[0,M]$ (tedy ka¾dou cenu znásobíme +$M/c_{max}$). +Jak jsme tím zkreslili výsledek? V¹imnìme si, ¾e efekt je stejný, jako kdybychom jednotlivé +ceny zaokrouhlili na~násobky èísla $c_{max}/M$ (prvky z intervalu +$[i\cdot c_{max}/M,(i+1)\cdot c_{max}/M)$ se zobrazí na stejný prvek). Ka¾dé $c_i$ jsme tím +tedy zmìnili o~nejvý¹e $c_{max}/M$, celkovou cenu libovolné podmno¾iny pøedmìtù pak +nejvý¹e o~$n\cdot c_{max}/M$. Teï si je¹tì v¹imnìme, ¾e pokud ze~zadání odstraníme +pøedmìty, které se samy nevejdou do~batohu, má optimální øe¹ení pùvodní úlohy cenu $OPT\ge c_{max}$, +tak¾e chyba v~souètu je nejvý¹e $n\cdot OPT/M$. Má-li tato chyba být shora omezena +$\varepsilon\cdot OPT$, musíme zvolit $M\ge n/\varepsilon$.\foot{Pøipomìòme, ¾e toto je¹tì není dùkaz, nebo» velkoryse pøehlí¾íme chyby dané zaokrouhlováním. Dùkaz provedeme ní¾e.} + +\s{Algoritmus:} +\algo +\:Odstraníme ze~vstupu v¹echny pøedmìty tì¾¹í ne¾~$H$. +\:Spoèítáme $c_{max}=\max_i c_i$ a zvolíme $M=\lceil n/\varepsilon\rceil$. +\:Kvantujeme ceny: $\forall i: \hat{c}_i \leftarrow \lfloor c_i \cdot M/c_{max} \rfloor$. +\:Vyøe¹íme dynamickým programováním problém batohu pro upravené ceny $\hat{c}_1, \ldots, \hat{c}_n$ +a pùvodní hmotnosti i kapacitu batohu. +\:Vybereme stejné pøedmìty, jaké pou¾ilo optimální øe¹ení kvantovaného zadání. +\endalgo + +\>Kroky 1--3 a 5 jistì zvládneme v~èase $\O(n)$. Krok~4 øe¹í problém batohu +se souètem cen $\hat{C}\le nM = \O(n^2/\varepsilon)$, co¾ stihne v~èase $\O(n\hat{C})=\O(n^3/\varepsilon)$. +Zbývá dokázat, ¾e výsledek na¹eho algoritmu má opravdu relativní chybu nejvý¹e~$\varepsilon$. + +Nejprve si rozmyslíme, jakou cenu budou mít pøedmìty které daly optimální øe¹ení +v pùvodním zadání (tedy mají v pùvodním zadání dohromady cenu $OPT$), +kdy¾ jejich ceny nakvantujeme (mno¾inu indexù tìchto pøedmìtù si oznaèíme~$Y$): +$$ +\eqalign{ +\widehat{OPT} &= \sum_{i\in Y} \hat{c}_i = +\sum_i \left\lfloor c_i\cdot {M\over c_{max}} \right\rfloor \ge +\sum_i \left( c_i\cdot {M\over c_{max}} - 1 \right) \ge \cr +&\ge +\biggl(\sum_i c_i \cdot {M\over c_{max}}\biggr) - n = +OPT \cdot {M\over c_{max}} - n. +} +$$ +Nyní spoèítejme, jak dopadne optimální øe¹ení~$Q$ nakvantovaného problému pøi pøepoètu +na~pùvodní ceny (to je výsledek na¹eho algoritmu): +$$ +\eqalign{ +ALG &= \sum_{i\in Q} c_i \ge +\sum_i \hat{c}_i \cdot {c_{max}\over M} = +\biggl(\sum_i \hat{c}_i\biggr) \cdot {c_{max}\over M} \ge^* +\widehat{OPT} \cdot {c_{max}\over M}. +} +$$ +Nerovnost $\ge^*$ platí proto, ¾e $\sum_{i\in Q} \hat{c}_i$ je optimální øe¹ení +kvantované úlohy, zatímco $\sum_{i\in Y} \hat{c}_i$ je nìjaké dal¹í øe¹ení té¾e úlohy, +které nemù¾e být lep¹í. Teï u¾ staèí slo¾it obì nerovnosti a dosadit za~$M$: +$$ +\eqalign{ +ALG &\ge \biggl( { OPT \cdot M\over c_{max}} - n\biggr) \cdot {c_{max}\over M} \ge +OPT - {n\cdot c_{max}\over n / \varepsilon} \ge OPT - \varepsilon c_{max} \ge \cr +&\ge OPT - \varepsilon OPT = (1-\varepsilon)\cdot OPT. +} +$$ +Algoritmus tedy v¾dy vydá øe¹ení, které je nejvý¹e $(1-\varepsilon)$-krát hor¹í ne¾ optimum, +a~doká¾e to pro libovolné~$\varepsilon$ v~èase polynomiálním v~$n$. Takovému algoritmu øíkáme +{\I polynomiální aproximaèní schéma} (jinak té¾ PTAS\foot{Polynomial-Time Approximation Scheme}). +V~na¹em pøípadì je dokonce slo¾itost polynomiální i v~závislosti na~$1/\varepsilon$, tak¾e +schéma je {\I plnì polynomiální} (øeèené té¾ FPTAS\foot{Fully Polynomial-Time Approximation +Scheme}). U nìkterých problémù se stává, ¾e aproximaèní schéma závisí na +$1/\varepsilon$ exponenciálnì, co¾ tak pøíjemné není. Shròme, co jsme zjistili, do následující vìty: + +\s{Vìta:} +Existuje algoritmus, který pro ka¾dé $\varepsilon > 0$ nalezne +{\I $(1 - \varepsilon)$-aproximaci} problému batohu s $n$ pøedmìty v èase +$\O(n^3/\varepsilon)$. + +\bye diff --git a/old/12-apx/Makefile b/old/12-apx/Makefile new file mode 100644 index 0000000..11f9d14 --- /dev/null +++ b/old/12-apx/Makefile @@ -0,0 +1,3 @@ +P=12-apx + +include ../Makerules diff --git a/old/2-dinic/2-dinic.tex b/old/2-dinic/2-dinic.tex new file mode 100644 index 0000000..244427c --- /dev/null +++ b/old/2-dinic/2-dinic.tex @@ -0,0 +1,223 @@ +\input lecnotes.tex + +\prednaska{2}{Dinicùv algoritmus}{(zapsala Markéta Popelová)} + + +Na~minulé pøedná¹ce jsme si~ukázali Fordùv-Fulkersonùv algoritmus. Tento algoritmus hledal maximální tok tak, ¾e zaèal s~tokem nulovým a~postupnì ho zvìt¹oval. Pro~ka¾dé zvìt¹ení potøeboval v~síti najít cestu, na~které mají v¹echny hrany kladnou rezervu (po takovéto cestì mù¾eme poslat více, ne¾ po~ní aktuálnì teèe). Ukázali jsme, ¾e pokud takováto cesta existuje, jde tok vylep¹it (zvìt¹it). Zároveò pokud tok jde vylep¹it, pak takováto cesta existuje. Dokázali jsme, ¾e pro~racionální kapacity je algoritmus koneèný a~najde maximální tok. + +Fordùv-Fulkersonùv algoritmus má ov¹em znaèné nevýhody. Funguje pouze pro~racionální kapacity a~je pomìrnì pomalý. Nyní si~uká¾eme jiný algoritmus, který nevylep¹uje tok pomocí cest, ale pomocí tokù\dots Budeme k~tomu potøebovat sí» rezerv. + +\s{Definice:} {\I Sí» rezerv} k~toku~$f$ v~síti $S=(V,E,z,s,c)$ je sí» $R=(S,f)=(V,E,z,s,r)$, kde~$r(e)$ je rezerva hrany~$e$ v~toku~$f$. + +\s{Konvence:} Pro~hranu~$e$ znaèí~$\overleftarrow{e}$ hranu opaènou. Napø. pokud $e=uv$, tak $\overleftarrow{e}=vu$. + +Je dùle¾ité si~uvìdomit, ¾e sí» rezerv je závislá jak na~pùvodní síti~$S$, tak na~nìjakém toku~$f$ v~síti~$S$. Sí» rezerv~$R$ se~pak od sítì~$S$ li¹í pouze kapacitami -- sí»~$R$ má jako kapacitu hrany rezervu hrany v pùvodní síti. Pro~pøipomenutí: rezervu hrany~$e$ v~síti $S=(V,E,z,s,c)$ s~tokem~$f$ jsme si~definovali jako $r(e)=c(e) - f(e) + f(\overleftarrow{e})$. + +Ne¾ si~uká¾eme samotný algoritmus, doká¾eme si~následující lemma. + +\s{Lemma:} Pro~ka¾dý tok~$f$ v~síti~$S$ a~pro~ka¾dý tok~$g$ v~síti $R=(S,f)$ lze v~èase $\O(m)$ nalézt tok~$f'$ v~síti~$S$ takový, ¾e $\vert f' \vert = \vert f \vert + \vert g \vert$. + +\proof + +Dùkaz rozdìlíme do~tøí krokù. V~prvním kroku si~uká¾eme, jak budeme tok~$f'$ v~síti~$S$ konstruovat. V~druhém kroku doká¾eme, ¾e takto zkonstruované~$f'$ je opravdu tok. A~nakonec uká¾eme, ¾e splòuje po¾adovanou vlastnost, tedy ¾e jeho velikost je souèet velikostí tokù~$f$ a~$g$. + +\>{\it 1. konstrukce~$f'$} + +\noindent +Pro ka¾dou dvojici hran~$e, \overleftarrow{e}$ urèíme~$f'(e)$ a~$f'(\overleftarrow{e})$ následovnì: + +\itemize\ibull +\:Pokud~$g(e) = g(\overleftarrow{e}) = 0$, pak nastavme: + \itemize\ibull + \:$f'(e) := f(e)$, + \:$f'(\overleftarrow{e}) := f(\overleftarrow{e})$. + \endlist + +\:Pokud~$g(e) > 0$ a~$g(\overleftarrow{e}) = 0$, pak polo¾me: + \itemize\ibull + \:$\varepsilon := \min (g(e), f(\overleftarrow{e}))$, + \:$f'(e) := f(e) + g(e) - \varepsilon$, + \:$f'(\overleftarrow{e}) := f(\overleftarrow{e}) - \varepsilon$. + \endlist + +\:Pøípad~$g(e) = 0$ a~$g(\overleftarrow{e}) > 0$ vyøe¹íme obdobnì. + +\:Pokud~$g(e) > 0$ a~$g(\overleftarrow{e}) > 0$, pak odeèteme od toku~$g$ cirkulaci po cyklu tvoøeném hranami~$e$ a $\overleftarrow{e}$: + \itemize\ibull + \:$\delta := \min (g(e),g(\overleftarrow{e}))$, + \:$g'(e) := g(e) - \delta$, + \:$g'(\overleftarrow{e}) := g(\overleftarrow{e}) - \delta$. + \endlist + + Tok $g'$ nyní spadá pod nìkterý z~pøedchozích pøípadù, které u¾ umíme vyøe¹it. + +\endlist + +\>{\it 2. $f'$ je tok} + +\numlist{\ndotted} + +\:Nejdøíve ovìøme první podmínku: $\forall e \in E: 0 \leq f(e) \leq c(e)$. Vezmìme libovolnou hranu~$e \in E$. Podle toho, co teèe po~hranách~$e$ a~$\overleftarrow{e}$ v~toku~$g$, jsme rozdìlili konstrukci toku na~tøi pøípady: + + \numlist{\ndotted} + + \:Pokud po~hranách~$e$ a~$\overleftarrow{e}$ netekl ¾ádný tok~$g$, pak jsme nastavili $f'(e) := f(e)$ a~$f'(\overleftarrow{e}) := f(\overleftarrow{e})$. Tedy pokud~$f$ dodr¾oval kapacity, tak pro~$f'$ musí platit to samé. + + + \:Pokud po~hranì~$e$ tekl tok~$g$ nenulový a~po opaèné nulový, tak jsme zvolili: $f'(e) := f(e) + g(e) - \varepsilon$. Víme, ¾e jsme si~$\varepsilon$ vybrali tak, ¾e $\varepsilon \leq g(e)$. Proto $f'(e) \geq 0$. + + Teï ovìøme, ¾e $f'(e) \leq c(e)$. V~pøípadì, ¾e $\varepsilon = g(e)$, tak $f'(e) = f(e) \leq c(e)$. V~opaèném pøípadì platí, ¾e $\varepsilon = f(\overleftarrow{e})$. Pak ov¹em + $$f'(e) = f(e) + g(e) - f(\overleftarrow{e}) \leq $$ + $$\leq f(e) + \left[ c(e) - f(e) + f(\overleftarrow{e}) \right] - f(\overleftarrow{e}) = c(e).$$ + Vyu¾ili jsme, ¾e~$g$ je tok v~síti rezerv, tedy $g(e) \leq c(e) - f(e) + f(\overleftarrow{e})$. + + Pro tok $f'(\overleftarrow{e})$ platí, ¾e $\varepsilon \leq f(\overleftarrow{e})$. Proto $f'(\overleftarrow{e}) = f(\overleftarrow{e}) - \varepsilon \geq 0$. Zároveò $f'(\overleftarrow{e}) \leq f(\overleftarrow{e}) \leq c(\overleftarrow{e})$. + + Tím jsme dokázali, ¾e~$f'(e)$ i~$f'(\overleftarrow{e})$ dodr¾ují kapacity. + + \:V posledním pøípadì tekl po~obou hranách kladný tok~$g$. Men¹í tok z~$g(e)$ a~$g(\overleftarrow{e})$ jsme vynulovali a~od vìt¹ího odeèetli ten men¹í. Tok~$g'(e)$ a~$g'(\overleftarrow{e})$ tedy zùstal korektní a~tok~$f'$ u¾ konstruujeme podle pøedchozího pøípadu. + + \endlist + +\:Teï musíme je¹tì dokázat, ¾e nový tok neporu¹uje Kirchhoffovy zákony: $$\forall v~\in V \setminus \{z,s\}: f'^\Delta(v)=0.$$ + % neboli $$\forall v~\in V \setminus \{z,s\}: \sum_{u: uv \in E}{f'(uv)}=\sum_{u: vu \in E}{f'(vu)}.$$ + + Vezmìme si~libovolnou hranu~$e = uv \in E$. Uvìdomme si, ¾e pøi~pøechodu z~$f(e)$ na~$f'(e)$ a~z~$f(\overleftarrow{e})$ na~$f'(\overleftarrow{e})$ bylo: + \itemize\idot + \:$f^\Delta(u)$ sní¾eno o~$g(e)$ + \:$f^\Delta(v)$ zvý¹eno o~$g(e)$. + \endlist + Seèteme-li úpravy na v¹ech hranách, dostaneme: $$f'^\Delta(v) = f^\Delta(v) + \sum_{u:uv \in E} g(uv) - \sum_{u:vu \in E} g(vu) =$$ $$= f^\Delta(v) + g^+(v) - g^-(v) = f^\Delta(v) + g^\Delta(v).$$ + + Jeliko¾~$f$ byl tok, tak $f^\Delta(v) = 0$ a jeliko¾~$g$ byl tok, tak $g^\Delta(v) = 0$. Proto $f'^\Delta(v) = f^\Delta(v) + g^\Delta(v) = 0$. + +\endlist + +Tím jsme dokázali, ¾e~$f'$ je tok v~síti~$S$. + +\>{\it 3. $\vert f' \vert = \vert f \vert + \vert g \vert$} + +Pou¾ijme vztah pro souèet pøebytkù z pøedchozího kroku: +$$\vert f' \vert = f'^\Delta(s) = f^\Delta(s) + g^\Delta(s) = \vert f \vert + \vert g \vert.$$ +\qed + + +Pro algoritmus budeme potøebovat vybírat kvalitní toky~$g$ v~síti rezerv. Pokud se~nám to bude daøit, bude se~tok~$f'$ rychle zvìt¹ovat, a¾ bychom mohli dojít k~maximálnímu toku. Nejlépe by se~nám hodily co nejvìt¹í toky v~síti rezerv. Kdybychom si~dali za cíl najít v¾dy maximální tok v~síti rezerv, výsledek by byl sice krásný (dostali bychom tak rovnou i~maximální tok v~pùvodní síti), ale problém hledání maximálního toku bychom pouze pøenesli na~jinou sí». Na¹e po¾adavky na~tento tok budou tedy takové, aby byl dostateènì velký, ale abychom bìhem jeho hledání nestrávili moc èasu. Podívejme se, jak se~s~tímto problémem vyrovná {\I Dinicùv algoritmus}. Nejdøíve si~ale zadefinujme nìkolik pojmù. + +\s{Definice:} Tok~$f$ je {\I blokující}, jestli¾e pro~ka¾dou orientovanou cestu~$P$ ze~$z$ do~$s$ existuje hrana~$e \in P$ taková, ¾e $f(e) = c(e)$. + +\s{Definice:} Sí» je {\I vrstevnatá (proèi¹tìná)}, kdy¾ v¹echny vrcholy a~hrany le¾í na~nejkrat¹ích cestách ze~$z$ do~$s$. + +Dinicùv algoritmus zaèíná s~nulovým tokem. Potom v¾dy podle toku~$f$ sestrojí sí» rezerv a~v~ní vyma¾e hrany s~nulovou rezervou. Pokud v~této promazané síti rezerv neexistuje cesta ze~zdroje do~stoku, tak skonèí a~prohlásí tok~$f$ za maximální. Jinak proèistí sí» rezerv tak, aby se~z ní stala vrstevnatá sí» (rozdìlí vrcholy do~vrstev podle vzdálenosti od zdroje a~odstraní pøebyteèné hrany). Ve~vrstevnaté síti najde blokující tok, pomocí nìho¾ zlep¹í tok~$f$. Pak opìt pokraèuje sestrojením sítì rezerv na~tomto vylep¹eném toku~$f$ atd. + +\figure{dinic-cistasit.eps}{Proèi¹tìná sí» rozdìlená do~vrstev}{0.4\hsize} + +\s{Algoritmus (Dinicùv)} + +\algo +\:$f \leftarrow$ nulový tok. +\:Sestrojíme sí» rezerv~$R$ a~sma¾eme $e: r(e) = 0$. +\:$l \leftarrow$ délka nejkrat¹í cesty ze~$z$ do~$s$ v~$R$. +\:Pokud $l = \infty$, zastavíme se~a vrátíme~$f$. +\:Proèistíme sí» $R \rightarrow$ sí»~$C$. +\:$g \leftarrow$ blokující tok v~$C$. +\:Zlep¹íme tok~$f$ pomocí~$g$. +\:GOTO 2. +\endalgo + +\s{Pozorování:} Pokud se~algoritmus zastaví, vydá maximální tok. + +\proof +Víme, ¾e~$f$ je stále korektní tok (jediné, jak ho mìníme je pøièítání toku~$g$, co¾ je, jak jsme si~dokázali, \uv{ne¹kodná operace}). Jakmile neexistuje cesta ze~$z$ do~$s$ v~$R$, tak je $f$ maximální tok, nebo» v~tuto dobu by se~zastavil (a vydal maximální tok) i~Fordùv-Fulkersonùv algoritmus, který je korektní. +\qed + +A teï je¹tì musíme ujasnit, jak budeme èistit sí» rezerv a~vybírat blokující tok~$g$. + +\s{Algoritmus proèi¹tìní sítì rezerv} + +\algo +\:Rozdìlíme vrcholy do~vrstev podle vzdálenosti od~$z$. +\:Odstraníme vrstvy za~$s$ (tedy vrcholy, které jsou od~$z$ vzdálenìj¹í ne¾~$s$), hrany do~minulých vrstev a~hrany uvnitø vrstev. +\:Odstraníme \uv{slepé ulièky}, tedy vrcholy s~$\deg^{out}(v) = 0$, a~to opakovanì pomocí fronty. (Nejdøíve zaøadíme do~fronty v¹echny vrcholy s~ $\deg^{out}(v) = 0$. Pak dokud není fronta prázdná, v¾dy vybereme vrchol~$v$ z~fronty, odstraníme~$v$ a~v¹echny hrany~$uv$. Pro~ka¾dý takový vrchol~$u$ zkontrolujeme, zda se~tím nesní¾il výstupní stupeò vrcholu~$u$ na~nulu ($\deg^{out}(u) = 0$). Pokud sní¾il, tak ho zaøadíme do~fronty.) +\endalgo + +\figure{dinic-neprocistenasit.eps}{Neproèi¹tìná sí». Obsahuje zpìtné hrany, hrany uvnitø vrstvy a~slepé ulièky.}{0.45\hsize} + +Hledání blokujícího toku zaèneme s~tokem nulovým. Pak vezmeme v¾dy orientovanou cestu ze~zdroje do~stoku v~síti~$C$. V~této cestì najdeme hranu s~nejni¾¹í hodnotou výrazu $r(e) - g(e)$ (neboli $c(e) - f(e)$ v~pùvodní síti). Tuto hodnotu oznaèíme~$\varepsilon$. Pak ke~ v¹em hranám na~této cestì pøièteme~$\varepsilon$. Pokud tok~$g$ na~nìjaké hranì dosáhne kapacity hrany, co¾ je zde~$r(e)$, tak hranu vyma¾eme. Následnì sí» doèistíme, aby splòovala podmínky vrstevnaté sítì. A~pokud je¹tì existuje nìjaká orientovaná cesta ze~zdroje do~stoku, tak opìt pokraèujeme s~touto cestou. + +\s{Algoritmus hledání blokujícího toku} + +\algo +\:$g \leftarrow$ nulový tok. +\:Dokud existuje orientovaná cesta~$P$ ze~$z$ do~$s$ v~$C$, opakuj: +\::$\varepsilon \leftarrow \min_{e \in P} (r(e) - g(e))$. +\::Pro~$\forall e \in P: g(e) \leftarrow g(e) + \varepsilon$. +\:::Pokud $g(e) = r(e)$, sma¾eme~$e$ z~$C$. +\::Doèistíme sí» zase pomocí fronty. +\endalgo + +\s{Èasová slo¾itost} Rozeberme si~jednotlivé kroky algoritmu. + +\numlist{\ndotted} +\:Inicializace toku~$f$ \dots $\O(m)$. +\:Sestrojení sítì rezerv a~smazání hran s~nulovou rezervou \dots $\O(m + n)$. +\:Najití nejkrat¹í cesty (prohledáváním do~¹íøky) \dots $\O(m + n)$. +\:Zkontrolování délky nejkrat¹í cesty \dots $\O(1)$. +\:Proèi¹tìní sítì \dots $\O(m + n)$. + \numlist{\ndotted} + \:Rozdìlení vrcholù do~vrstev -- provedlo ji¾ prohledávání do~¹íøky \dots $\O(1)$. + \:Odstranìní nìkterých hran \dots $\O(m + n)$. + \:Odstranìní \uv{slepých ulièek} pomocí fronty -- ka¾dou hranu odstraníme nejvý¹e jedenkrát, ka¾dý vrchol se~dostane do~fronty nejvý¹e jedenkrát \dots $\O(m + n)$. + \endlist +\:Najití blokujícího toku~$g$ \dots $\O(m \cdot n)$. + \numlist{\ndotted} + \:Inicializace toku~$g$ \dots $\O(m)$. + \:Najití orientované cesty v~proèi¹tìné síti rezerv (staèí vzít libovolnou cestu ze~zdroje, nebo» ka¾dá z~nich v~této síti vede do~stoku) \dots $\O(n)$. + \:Výbìr minima z~výrazu $r(e) - g(e)$ pøes v¹echny hrany cesty -- ta mù¾e být dlouhá nejvý¹e~$n$ \dots $\O(n)$. + \:Pøepoèítání v¹ech hran cesty \dots $\O(n)$. + \:Smazání hran cesty, jejich¾ tok~$g(e)$ se~zvý¹il na~hodnotu~$r(e)$ \dots $\O(n)$. + \:Doèi¹»ování vyøe¹me zvlá¹». + \endlist + + Vnitøní cyklus (kroky 2 a¾ 6) provedeme nejvý¹e~$m$ krát, nebo» pøi~ka¾dém prùchodu vyma¾eme alespoò jednu hranu (tak jsme si~volili~$\varepsilon$). + + Èi¹tìní bìhem celého hledání blokujícího toku~$g$ v~proèi¹tìné síti rezerv trvá dohromady $\O(m + n)$, nebo» ka¾dou hranu a~vrchol sma¾eme nejvý¹e jedenkrát. + + Najití blokujícího toku bude tedy trvat $\O(m \cdot n + (m + n)) = \O(m \cdot n)$. + +\:Zlep¹ení toku~$f$ pomocí toku~$g$ \dots $\O(m)$. +\:Skok na~2. krok \dots $\O(1)$. +\endlist + +Zbývá nám jen urèit, kolikrát projdeme vnìj¹ím cyklem (fází). Doká¾eme si~lemma, ¾e hodnota~$l$ vzroste mezi prùchody vnìj¹ím cyklem (fázemi) alespoò o~1. Z~toho plyne, ¾e vnìj¹ím cyklem mù¾eme projít nejvý¹e $n$-krát, nebo» cesta v síti na~$n$ vrcholech mù¾e být dlouhá nejvý¹e $n$. + +Uvìdomme si, ¾e uvnitø vnìj¹ího cyklu pøevládá èlen $\O(m \cdot n)$, tak¾e celková èasová slo¾itost bude $\O(n^2 \cdot m)$. + +\s{Lemma:} Hodnota~$l$ (délka nejkrat¹í cesty ze~$z$ do~$s$ v~proèi¹tìné síti) vzroste mezi fázemi alespoò o~1. + +\proof + +Podíváme se~na~prùbìh jednoho prùchodu vnìj¹ím cyklem. Délku aktuálnì nejkrat¹í cesty ze~zdroje do~stoku oznaème~$l$. V¹echny pùvodní cesty délky~$l$ se~bìhem prùchodu zaruèenì nasytí, proto¾e tok~$g$ je blokující. Musíme v¹ak dokázat, ¾e nemohou vzniknout ¾ádné nové cesty délky~$l$ nebo men¹í. V~síti rezerv toti¾ mohou hrany nejen +ubývat, ale i~pøibývat: pokud po¹leme tok po~hranì, po~které je¹tì nic neteklo, tak v~protismìru z~dosud nulové rezervy vyrobíme nenulovou. Rozmysleme si~tedy, jaké hrany mohou pøibývat. + +Hrany mohou pøibývat jen tehdy, kdy¾ jsme po~opaèné hranì nìco poslali. Ale my nìco posíláme po~hranách pouze z~vrstvy do~té následující. Hrany tedy pøibývají do~minulé vrstvy. + +Vznikem nových hran by proto mohly vzniknout nové cesty ze~zdroje do~stoku, které pou¾ívají zpìtné hrany. Jen¾e cesta ze~zdroje do~stoku, která pou¾ije zpìtnou hranu, musí alespoò jednou skoèit o~vrstvu zpìt a~nikdy nemù¾e skoèit o~více ne¾ jednu vrstvu dopøedu, a~proto je její délka alespoò $l+2$. Pokud cesta novou zpìtnou hranu nepou¾ije, má buï délku~$> l$, co¾ je v~poøádku, nebo má délku~$= l$, pak je zablokovaná. + +Tím je lemma dokázáno. +\qed + +\figure{dinic-cestashranouzpet.eps}{Cesta u¾ívající novou zpìtnou hranu}{0.4\hsize} + +V¹echna dokázaná tvrzení mù¾eme shrnout do~následující vìty: + +\s{Vìta:} Dinicùv algoritmus najde maximální tok v~èase $\O(n^2\cdot m)$. + +\s{Poznámka:} Algoritmus se~chová hezky na~sítích s~malými celoèíselnými kapacitami, ale kupodivu i~na~rùzných jiných sítích. Èasto se~pou¾ívá, nebo» se~chová efektivnì. A~je mnoho zpùsobù, jak ho je¹tì vylep¹ovat, èi odhadovat ni¾¹í slo¾itost na~speciálních sítích. + +\s{Poznámka:} Algoritmus nevy¾aduje racionální kapacity! Dal¹í z~dùvodù, proè maximální tok existuje i~v~síti s~iracionálními kapacitami. + + +%k,s,v,na,do,ke,pro,pøi,a,u,i,po, + +\bye diff --git a/old/2-dinic/Makefile b/old/2-dinic/Makefile new file mode 100644 index 0000000..c183fb0 --- /dev/null +++ b/old/2-dinic/Makefile @@ -0,0 +1,3 @@ +P=2-dinic + +include ../Makerules diff --git a/old/2-dinic/dinic-cestashranouzpet.eps b/old/2-dinic/dinic-cestashranouzpet.eps new file mode 100644 index 0000000..5633e61 --- /dev/null +++ b/old/2-dinic/dinic-cestashranouzpet.eps @@ -0,0 +1,472 @@ +%!PS-Adobe-2.0 EPSF-2.0 +%%Title: /home/mj/texts/ga/2-dinic/Diagram1.dia +%%Creator: Dia v0.96.1 +%%CreationDate: Tue Feb 9 16:36:37 2010 +%%For: mj +%%Orientation: Portrait +%%Magnification: 1.0000 +%%BoundingBox: 0 0 546 219 +%%BeginSetup +%%EndSetup +%%EndComments +%%BeginProlog +[ /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef +/.notdef /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef /.notdef 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5.706158 m 25.633432 6.023710 l 25.722193 5.758670 l 25.563417 5.528637 l cp s +showpage diff --git a/old/3-goldberg/3-goldberg.tex b/old/3-goldberg/3-goldberg.tex new file mode 100644 index 0000000..70544d4 --- /dev/null +++ b/old/3-goldberg/3-goldberg.tex @@ -0,0 +1,283 @@ +\input lecnotes.tex + +\prednaska{3}{Goldbergùv algoritmus}{(zapsala Markéta Popelová)} + +Pøedstavíme si~nový algoritmus pro~hledání maximálního toku v~síti, který se~uká¾e být stejnì dobrý jako {\I Dinicùv algoritmus} ($\O(MN^{2})$) a~po~nìkolika vylep¹eních bude i~lep¹í. Nejdøíve si~pøipomeòme definice, které budeme potøebovat: + +\s{Definice:} Mìjme sí» $S=(V,E,z,s,c)$, tok~$f$ a~libovolný vrchol~$v$. Pak $f^{\Delta}(v)$ nazýváme {\I pøebytek} ve~vrcholu~$v$ a~definujeme ho takto: $$f^{\Delta}(v):=\sum_{uv \in E}{f(uv)} - \sum_{vu \in E}{f(vu)}.$$ Pøebytek ve~vrcholu~$v$ je tedy souèet v¹eho, co do~vrcholu~$v$ pøiteèe, minus souèet v¹eho, co z~$v$ odteèe. + +\s{Definice:} Dále pro~libovolnou hranu~$uv \in E$ definujeme její {\I rezervu} následovnì: +$$r(uv) = c(uv) - f(uv) + f(vu).$$ Rezerva hrany znaèí, co je¹tì je mo¾no po~této hranì poslat. + +\s{Poznámka:} Dále budeme oznaèovat písmenem~$N$ poèet vrcholù a~$M$ poèet hran, tedy~$N = \vert V \vert$ a~$M = \vert E \vert$. + +Goldbergùv algoritmus na~rozdíl od~Dinicova algoritmu zaèíná s~ohodnocením hran, které pravdìpodobnì není tokem (budeme ho nazývat {\I vlna}), a~postupnì ho zmen¹uje a¾ na~korektní tok. + +\s{Definice:} Funkce $f:E \rightarrow {\bb R}_{0}^{+}$ je {\I vlna} v~síti~$(V, E, z, s, c)$ tehdy, kdy¾ jsou splnìny následující dvì podmínky: + \numlist\ndotted + \:$\forall e \in E : f(e) \leq c(e)$ (vlna na hranì nepøekroèí kapacitu hrany) + \:$ \forall v \in V \setminus \{z, s\} : f^{\Delta}(v) \geq 0$ (pøebytek ve vrcholu je nezáporný). + \endlist + +\s{Pozorování:} Ka¾dý tok~$f$ je také vlna, ale opaènì to obvykle platit nemusí. + +\s{Operace:} {\I Pøevedení pøebytku} + +Algoritmus bude potøebovat pøevádìt pøebytky z~vrcholu~$u$ na~sousední vrchol~$v$. Mìjme hranu~$uv$ s~kladnou rezervou $r(uv) > 0$ a~kladným pøebytkem ve~vrcholu~$u$: $f^\Delta(u) > 0$. Èást pøebytku budeme chtít poslat z~vrcholu~$u$ do~vrcholu~$v$. Vezmeme $\delta := \min (f^\Delta(u), r(uv))$ a~po~hranì~$uv$ po¹leme tok o velikosti~$\delta$. Výsledná situace bude vypadat následovnì: + \itemize\ibull + \:$f'^\Delta(u) = f^\Delta(u) - \delta$. + \:$f'^\Delta(v) = f^\Delta(v) + \delta$. + \:$r'(uv) = r(uv) - \delta$. + \:$r'(vu) = r(vu) + \delta$. + \endlist + +Kdybychom ov¹em nepøidali ¾ádnou jinou podmínku, ná¹ algoritmus by se~mohl krásnì zacyklit (napø. posílat pøebytek z~$u$ do~$v$ a~zase zpátky). Abychom se~tomu vyhnuli, zavedeme {\I vý¹ku vrcholu} $h: V \to {\bb N}$ a~dovolíme pøevádìt pøebytek pouze z~vy¹¹ího vrcholu~$u$ na~ni¾¹í $v$: $h(u) > h(v)$. + +\s{Shrnutí:} Podmínky pro~pøevedení pøebytku po~hranì $uv \in E$: + \numlist\ndotted + \:Ve~vrcholu~$u$ je nenulový pøebytek: $f^{\Delta}(u) > 0$. + \:Vrchol~$u$ je vý¹ ne¾ vrchol~$v$: $h(u) > h(v)$. + \:Hrana~$uv$ má nenulovou rezervu: $r(uv)>0$. + \endlist + + +\s{Operace:} Pro~vrchol~$u \in V$ definujme {\I zvednutí vrcholu}: +Pokud bìhem výpoètu narazíme ve~vrcholu~$u$ na~pøebytek, který nelze nikam pøevést, zvìt¹íme vý¹ku vrcholu~$u$ o~jednièku, tj. $h(u) \leftarrow h(u)+1$. + + +\s{Algoritmus (Goldbergùv)} + +\algo +\:$\forall v \in V: h(v)\leftarrow 0$ (v¹em vrcholùm nastavíme poèáteèní vý¹ku nula) a~$h(z)\leftarrow N$ (zdroj zvedneme do~vý¹ky~$N$). +\:$\forall e \in E: f(e)\leftarrow 0$ (po~hranách nejdøíve nenecháme protékat nic) a~$\forall zu \in E : f(zu)\leftarrow c(zu)$ (ze~zdroje pustíme maximální mo¾nou vlnu). +\:Dokud $\exists u \in V \setminus \{z,s\}: f^{\Delta}(u)>0$: +\::Pokud $\exists v \in V: uv \in E,~r(uv)>0$ a~$h(u)>h(v)$, pak pøevedeme pøebytek po~hranì z~$u$ do~$v$. +\::V~opaèném pøípadì zvedneme $u$:~$h(u) \leftarrow h(u) + 1$. +\:Vrátíme tok~$f$ jako výsledek. +\endalgo + +\noindent +Nyní bude následovat nìkolik lemmat a~invariantù, jimi¾ doká¾eme správnost a~èasovou slo¾itost Goldbergova algoritmu. + +\s{Invariant A (základní):} + \numlist \ndotted + \:Funkce~$f$ je v~ka¾dém kroku algoritmu vlna. + \:$h(v)$ nikdy neklesá pro~¾ádné~$v$. + \:$h(z)=N$ a~$h(s)=0$ po~celou dobu bìhu algoritmu. + \endlist + +\proof Indukcí dle poètu prùchodù cyklem (3. -- 5. krok algoritmu). + +Na zaèátku je v¹e v~poøádku ($f$ je nulová funkce, pøebytky v¹ech vrcholù jsou nezáporné, tedy~$f$ je vlna, $h(z)=N$ a~$h(s)=0$). V~prùbìhu se~tyto hodnoty mìní pouze pøi: + \itemize\ibull + \:Pøevedení po~hranì~$uv$: Po hranì~$uv$ se~nepo¹le více ne¾ její rezerva. Pøebytek~$u$ se~sní¾í, ale nejménì na~nulu. Pøebytek~$v$ se~zvý¹í. Tedy~$f$ zùstává vlnou. Vý¹ky se~nemìní. + \:Zvednutí vrcholu~$u$: Mìní pouze vý¹ky -- a~to vrcholù rùzných od zdroje èi stoku -- a~pouze se zvy¹ují. + \qeditem + \endlist + +\s{Invariant S (o~Spádu):} Neexistuje hrana $uv \in E: r(uv)>0$ \& $h(u) > h(v)+1$ (s~kladnou rezervou a~spádem vìt¹ím ne¾ jedna). + +\proof Indukcí dle bìhu algoritmu. + +Na zaèátku mají v¹echny hrany ze~zdroje rezervu nulovou a~v¹echny ostatní vedou mezi vrcholy s~vý¹kou 0. V~prùbìhu by se~tento invariant mohl pokazit pouze dvìma zpùsoby: + \itemize\ibull + \:Zvednutím vrcholu~$u$, ze~kterého vede hrana~$uv$ s~kladnou rezervou a~spádem 1. Tento pøípad nemù¾e nastat, nebo» hranu zvedáme pouze tehdy, kdy¾ neexistuje vrchol~$v$ takový, ¾e hrana~$uv$ má kladnou rezervu a~spád alespoò 1. Takový vrchol v~na¹em pøípadì existuje, proto se~místo zvednutí vrcholu~$u$ po¹le pøebytek po~hranì~$uv$. + \:Zvìt¹ením rezervy hrany se~spádem vìt¹ím ne¾ 1. Toto také nemù¾e nastat, nebo» rezervu bychom mohli zvìt¹it jedinì tak, ¾e bychom poslali nìco v~protismìru -- a~to nesmíme, jeliko¾ bychom poslali pøebytek z~ni¾¹ího vrcholu na~vy¹¹í. + \qeditem + \endlist + +\s{Definice:} Cestu~$P$ nazveme {\I nenasycenou}, pokud v¹echny její hrany mají kladnou rezervu. Neboli $\forall e \in P: r(e) > 0$. + +\s{Lemma K (o~Korektnosti):} Kdy¾ se~algoritmus zastaví, je~$f$ maximální tok. + +\proof Dùkaz rozlo¾me do~dvou krokù. Nejdøíve uká¾eme, ¾e~$f$ je tok, a~pak jeho maximalitu. + + \numlist\ndotted + \:Nech» se~algoritmus zastavil. Pak nemohl existovat ¾ádný vrchol~$v$ (kromì zdroje a~stoku) s~kladným pøebytkem. Tedy $\forall v \in V~\setminus \{z,s\}: f^\Delta(v) = 0$. (Víme ji¾, ¾e~$f$ je po~celou dobu vlna, tak¾e pøebytek nemù¾e být nikdy záporný.) V~tom pøípadì splòuje~$f$ podmínky toku. + \:Pro spor pøedpokládejme, ¾e tok~$f$ není maximální. Pak existuje nenasycená cesta ze~zdroje do~stoku. Vezmìme si~libovolnou takovou cestu. Zdroj je stále ve~vý¹ce~$N$ a~spotøebiè ve~vý¹ce 0 (viz invariant A). Tato cesta tedy pøekonává vý¹ku~$N$, ale mù¾e mít nejvý¹e~$N-1$ hran. Proto existuje alespoò jedna hrana se~spádem alespoò 2. Tato hrana tedy nemù¾e mít kladnou rezervu (viz invariant S). Tato cesta proto nemù¾e být zlep¹ující, co¾ je spor. Tím jsme dokázali, ¾e~$f$ je nutnì maximální tok. + \qeditem + \endlist + +\s{Lemma C (Cesta):} Mìjme vrchol $v \in V$. Pokud $f^{\Delta}(v) > 0$, pak existuje nenasycená cesta z~vrcholu~$v$ do~zdroje. + +\proof +Pro vrchol~$v \in V$ s $f^{\Delta}(v) > 0$ definujme mno¾inu $A := \{ u \in V : \exists$ nenasycená cesta z~$v$ do~$u \}$. + +Seètìme pøebytky ve~v¹ech vrcholech mno¾iny~$A$. Pøebytek ka¾dého vrcholu se~spoèítá jako souèet tokù do~nìj vstupujících minus souèet tokù z~nìj vystupujících. V¹echny hrany, jejich¾ oba vrcholy le¾í v~$A$, se~jednou pøiètou a~jednou odeètou. Proto nás budou zajímat pouze hrany mezi~$A$ a~$V \setminus A$. + + $$\sum_{u \in A}f^{\Delta}(u) = \underbrace{ \sum_{ab \in E \cap ( (V \setminus A) \times A )} f(ab) }\limits_{=0} - \underbrace{ \sum_{ab \in E \cap ( A \times (V \setminus A) )} f(ab) }\limits_{\geq 0}~\leq~0.$$ + +Uka¾me si, proè je první svorka rovna nule. Mìjme vrcholy $a \in V \setminus A$ a~$b \in A$ takové, ¾e $ab\in E$. O~nich víme, ¾e $r(ba) = 0$ (jinak by~$a$ patøilo do~$A$) $\Rightarrow f(ba) = c(ba) \Rightarrow f(ab)=0$. Proto do~$A$ nic nepøitéká. + +\figure{Goldberg01.eps}{Obrázek k dùkazu lemmatu C}{0.2\hsize} + +Proè je druhá svorka nezáporná, je zøejmé, nebo» tok na~hranì je v¾dy nezáporný a~souèet nezáporných èísel je nezáporné èíslo. + +Proto $\sum_{u \in A}{f^\Delta(u) \le 0}$. Zároveò v¹ak v~$A$ je aspoò jeden vrchol s~kladným pøebytkem, toti¾~$v$, proto v~$A$ musí být také vrchol se~záporným pøebytkem -- a~jediný takový je zdroj. Tím je dokázáno, ¾e $z \in A$, tedy ¾e vede nenasycená cesta z~vrcholu~$v$ do~zdroje. +\qed + +\s{Invariant V (Vý¹ka):} $\forall v \in V$ platí $h(v)\leq 2N$. + +\proof +Kdyby existoval vrchol~$v$ s~vý¹kou $h(v) > 2N$, tak by musel být nìkdy zvednut z~vý¹ky~$2N$. Tehdy musel mít kladný pøebytek $f^\Delta(v)>0$ (jinak by nemohl být zvednut). Dle lemmatu C musela existovat nenasycená cesta z~$v$ do~zdroje. Tato cesta mìla spád alespoò~$N$, ale mohla mít nejvý¹e~$N-1$ hran (jinak by to nebyla cesta v~síti na~$N$ vrcholech). Tudí¾ musela na~této cestì existovat hrana se~spádem alespoò 2, co¾ je spor s~invariantem S (nebo» v¹echny hrany této cesty mají z~definice nenasycené cesty kladné rezervy). +\qed + +\s{Lemma Z (poèet Zvednutí):} Poèet v¹ech zvednutí je maximálnì~$2N^{2}$. + +\proof +Staèí si~uvìdomit, ¾e ka¾dý vrchol mù¾eme zvednout maximálnì~$2N$-krát a~vrcholù je~$N$. +\qed + +Teï nám je¹tì zbývá urèit poèet provedených pøevedení. Bude se~nám hodit, kdy¾ pøevedení rozdìlíme na~dva druhy: + +\s{Definice:} Øekneme, ¾e pøevedení je {\I nasycené}, pokud po~pøevodu rezerva na~hranì~$uv$ klesla na~nulu, tedy $r(uv)=0$. V~opaèném pøípadì je {\I nenasycené}, a~tehdy urèitì klesne pøebytek ve~vrcholu~$u$ na~nulu, tedy $f^{\Delta}(u) = 0$ (pøi~nasyceném pøevedení se~to~ale mù¾e stát také). + +\s{Lemma S (naSycená pøevedení):} Poèet v¹ech nasycených pøevedení je nejvý¹~$NM$. + +\proof +Pro ka¾dou hranu~$uv$ spoèítejme poèet nasycených pøevedení (tedy takových pøevedení, ¾e po~nich klesne rezerva hrany na~nulu). Abychom dvakrát nasycenì pøevedli pøebytek (nebo jeho èást) z~vrcholu~$u$ do~vrcholu~$v$, tak jsme museli~$u$ mezitím alespoò dvakrát zvednout: + +Po~prvním nasyceném pøevedení z~vrcholu~$u$ do~vrcholu~$v$ se~vynulovala rezerva hrany~$uv$. Uvìdomme si, ¾e pøi~této operaci muselo být~$u$ vý¹e ne¾~$v$, a~dokonce víme, ¾e bylo vý¹e pøesnì o~1 (viz lemma~S). Po~této hranì tedy nemù¾eme u¾~nic více pøevést. Aby do¹lo k~druhému nasycenému pøevedení z~$u$ do~$v$, musíme nejprve opìt zvý¹it rezervu hrany~$uv$. Jediný zpùsob, jak toho lze dosáhnout, je pøevést èást pøebytku z~$v$ zpátky do~$u$. K~tomu se~musí~$v$ dostat (alespoò o~1) vý¹e ne¾~$u$. Po~pøelití bude rezerva~$uv$ opìt kladná. A~abychom provedli nasycené pøevedení znovu ve~smìru z~$u$ do~$v$, musíme zase~$u$ dostat (alespoò o~1) vý¹e ne¾~$v$. Proto musíme~$u$ alespoò o~2 zvednout -- nejprve na~úroveò~$v$ a~pak je¹tì o~1 vý¹e. + + +Ukázali jsme si~tedy, ¾e mezi ka¾dými dvìma nasycenými pøevedeními jsme vrchol~$u$ zvedli alespoò dvakrát. Nicménì libovolnou hranu mù¾eme zvednout nejvý¹e~$2N$-krát (viz invariant V). V¹ech hran je~$M$, tudí¾ poèet v¹ech nasycených pøevedení je nejvý¹e~$NM$. +\qed + +\s{Lemma N (Nenasycená pøevedení):} Poèet v¹ech nenasycených pøevedení je~$\O(N^2M)$. + +\proof +Dùkaz provedeme pomocí potenciálové metody -- nadefinujme si~následující funkci jako potenciál: + $$ \Phi := \sum_{\scriptstyle{v: f^{\Delta}(v) > 0} \atop \scriptstyle{v \ne z,s}} h(v). $$ +Nyní se~podívejme, jak se~ná¹ potenciál bìhem algoritmu vyvíjí a~jaké má vlastnosti: + + \itemize\ibull + \:Na poèátku je $ \Phi = 0 $. + \:Bìhem celého algoritmu je $ \Phi \ge 0 $, nebo» je souètem nezáporných èlenù. + \:Zvednutí vrcholu zvý¹í $\Phi$ o~jednièku (Aby byl vrchol zvednut, musel mít kladný pøebytek $\Rightarrow$ vrchol do~sumy ji¾ pøispíval, teï jen pøispìje èíslem o 1 vy¹¹ím.). Ji¾ víme, ¾e za~celý prùbìh algoritmu je v¹ech zvednutí maximálnì~$2N^2$, proto zvedáním vrcholù zvý¹íme potenciál dohromady nejvý¹e o~$2N^2$. + \:Nasycené pøevedení zvý¹í~$\Phi$ nejvý¹e o~$2N$, proto¾e buï po~pøevodu hranou~$uv$ v~$u$ zùstal nìjaký pøebytek, tak¾e se~mohl potenciál zvý¹it nejvý¹e o~$h(v) \leq 2N$, nebo je pøebytek v~$u$ po~pøevodu nulový a~potenciál se~dokonce o~jedna sní¾il. Za~celý prùbìh tak dojde k~maximálnì~$NM$ takovýmto pøevedením, díky nim¾ se~potenciál zvý¹í maximálnì o~$2N^2M$. + \:Koneènì kdy¾ pøevádíme po~hranì~$uv$ nenasycenì, tak od~potenciálu urèitì odeèteme vý¹ku vrcholu~$u$ (nebo» se~vynuluje pøebytek ve~vrcholu~$u$) a~mo¾ná pøièteme vý¹ku vrcholu~$v$. Jen¾e $h(v) = h(u) - 1$, a~proto nenasycené pøevedení potenciál v¾dy sní¾í alespoò o~jedna. + \endlist + +\>Z~tohoto rozboru chování potenciálu~$\Phi$ v~prùbìhu algoritmu získáváme, ¾e poèet v¹ech nenasycených pøevedení mù¾e být nejvý¹e $2N^2 + 2N^2M$, co¾ je $\O(N^2M)$. +\qed + +\s{Implementace:} + +Budeme si~pamatovat seznam~$P$ v¹ech vrcholù~$v \ne z,s$ s~kladným pøebytkem. Neboli +$$P = \{ v \in V \setminus \{z,s\} ~\vert~ f^{\Delta}(v) > 0 \}.$$ +Kdy¾ mìníme pøebytek nìjakého vrcholu, mù¾eme ná¹ seznam v~konstantním èase aktualizovat (napø. tak, ¾e si~ka¾dý vrchol pamatuje pozici, na~které v~seznamu~$P$ je). V~konstantním èase také umíme odpovìdìt, zda existuje nìjaký vrchol s~pøebytkem. + +Dále si~pro ka¾dý vrchol~$u \in V$ budeme pamatovat~$L(u)$-seznam hran~$uv \in E$ takových, které vedou dolù (mají spád alespoò 1) a~kladnou rezervu. Neboli +$$L(u) = \{ uv \in E ~\vert~ v \in V,~ r(uv) > 0,~ h(v) < h(u)\}.$$ +Díky tomu mù¾eme pøistupovat k~patøièným sousedùm~$u$ v~èase $\O(1)$, stejnì jako pøidávat hrany do~$L(u)$, resp. je mazat. Opìt ka¾dá hrana si~bude pamatovat pozici, na~které se~nachází v~seznamu~$L$. + +\s{Rozbor èasové slo¾itosti algoritmu:} + +\numlist\ndotted +\:Inicializace vý¹ek \dots\ $\O(N)$. +\:Inicializace vlny~$f$ \dots\ $\O(M)$. +\:Výbìr vrcholu~$u$ s~kladným pøebytkem -- vezmeme první vrchol v~$P$ \dots\ $\O(1)$. +\:Výbìr vrcholu~$v$, do~kterého vede z~$u$ hrana s~kladnou rezervou a~který je ní¾e ne¾~$u$ -- vezmeme první hranu z~$L(u)$ \dots\ $\O(1)$. + + Pøevedení pøebytku: \dots\ $\O(1)$. + \itemize\idot + \:Nasycené pøevedení \dots\ $\O(1)$. + \itemize\idot + \:Rezerva hrany~$uv$ klesne na~nulu $\Rightarrow$ hrana~$uv$ vypadne z~$L(u)$ \dots\ $\O(1)$. + \:Pøebytek vrcholu~$v$ se~zvý¹í $\Rightarrow$ pokud je¹tì nebyl v~seznamu~$P$, tak se~tam pøidá \dots\ $\O(1)$. + \:Pøebytek vrcholu~$u$ mo¾ná také klesne na~nulu $\Rightarrow$ pak by vrchol~$u$ vypadnul z~$P$ \dots\ $\O(1)$. + \endlist + \:Nenasycené pøevedení \dots\ $\O(1)$. + \itemize\idot + \:Rezerva hrany~$uv$ zùstane nezáporná $\Rightarrow$ hrana~$uv$ zùstane v~$L(u)$ \dots\ $\O(1)$. + \:Vynuluje se~pøebytek vrcholu~$u$~$\Rightarrow$ vrchol $u$ vypadne z~$P$ \dots~$\O(1)$. + \:Pøebytek vrcholu~$v$ se~zvý¹í~$\Rightarrow$ pokud je¹tì nebyl v~seznamu~$P$, tak se~tam pøidá \dots\ $\O(1)$. + \endlist + \endlist +\:Zvednutí vrcholu~$u$ \dots $\O(N)$. + +Musíme obejít v¹echny hrany do~$u$ a~z~$u$, kterých je nejvý¹e~$2N-2$, porovnat +vý¹ky a~pøípadnì tyto hrany~$uv$ odebrat ze~seznamu~$L(v)$ resp. pøidat +do~$L(u)$. Abychom pro~odebrání hrany~$uv$ ze~seznamu~$L(v)$ nemuseli procházet +celý seznam, budeme si~$\forall u \in V$ pamatovat je¹tì $L^{-1}(u) := $ seznam +ukazatelù na~hrany~$uv$ v~seznamech~$L(v)$. + +\endlist + +Vidíme, ¾e ka¾dé zvednutí je sice drahé, ale je jich zase pomìrnì málo. Naopak pøevádìní pøebytkù je èastá operace, tak¾e je výhodné, ¾e trvá konstantní èas. + +\s{Shrnutí:} + +\itemize\ibull +\:V¹ech zvednutí je $\O(N^2)$ (viz lemma Z), ka¾dé trvá $\O(N)$ \dots\ $\O(N^3).$ +\:V¹ech nasycených pøevedení je $\O(NM)$ (viz lemma S), ka¾dé trvá $\O(1)$ \dots\ $\O(NM).$ +\:V¹ech nenasycených pøevedení je $\O(N^2M)$ (viz lemma N), ka¾dé trvá $\O(1)$ \dots\ $\O(N^2M).$ +\endlist + +Dohromady má tedy Goldbergùv algoritmus èasovou slo¾itost $\O(N^2M)$. Vidíme, ¾e u¾ v~tomto obecném pøípadì to není hor¹í ne¾ Dinicùv algoritmus. Pøí¹tì si~uká¾eme, ¾e mù¾e mít i~mnohem lep¹í. Nejdøíve ale zformulujme v¹echna dokázaná tvrzení do~následující vìty: + +\s{Vìta:} Goldbergùv algoritmus najde maximální tok v~èase $\O(N^2M)$. + +\s{Pozorování:} Pokud bychom volili v¾dy nejvy¹¹í z~vrcholù s~pøebytkem, tak by se~mohl algoritmus chovat lépe. Podívejme se~na~to pozornìji a~vylep¹ený Goldebrgùv algoritmus oznaème G'. + +\s{Algoritmus (Vylep¹ený Goldbergùv algoritmus)} + +\algo +\:$\forall v \in V: h(v)\leftarrow 0$ (v¹em vrcholùm nastavíme poèáteèní vý¹ku nula) a~$h(z)\leftarrow N$ (zdroj zvedneme do~vý¹ky~$N$). +\:$\forall e \in E: f(e)\leftarrow 0$ (po~hranách nejdøíve nenecháme protékat nic) a~$\forall zu \in E : f(zu)\leftarrow c(zu)$ (ze~zdroje pustíme maximální mo¾nou vlnu). +\:Dokud $\exists u \in V \setminus \{z,s\}: f^{\Delta}(u)>0$: +\::Vybereme z~vrcholù s~pøebytkem ten s~nejvy¹¹í vý¹kou, oznaèíme ho~$u$. +\:::Pokud $\exists v \in V: uv \in E,~r(uv)>0$ a~$h(u)>h(v)$, pak pøevedeme pøebytek po~hranì z~$u$ do~$v$. +\:::V~opaèném pøípadì zvedneme $u$:~$h(u) \leftarrow h(u) + 1$. +\:Vrátíme tok~$f$ jako výsledek. +\endalgo + +Rozmysleme si, o~kolik bude vylep¹ený algoritmus G' lep¹í ne¾ ten pùvodní. Ten pùvodní mìl èasovou slo¾itost $\O(N^2M)$ a~pøevládal èlen, který odpovídal nenasyceným pøevedením. Zkusme tedy právì poèet nenasycených pøevedení odhadnout ve~vylep¹eném algoritmu o~nìco tìsnìji. + +\s{Lemma N' (Nenasycená pøevedení):} Algoritmus G' provede~$\O(N^3)$ nenasycených pøevedení. + +\proof +Dokazovat budeme opìt pomocí potenciálové metody. Zadefinujme si~potenciál {\I nejvy¹¹í hladinu s~pøebytkem}: +$$H := \max \{ h(v) \mid v \neq z,s ~\&~ f^\Delta(v) > 0\}.$$ +Rozdìlíme bìh algoritmu na~{\I fáze}. Ka¾dá fáze konèí tím, ¾e~se~$H$ zmìní. Jak se~mù¾e zmìnit? Buï se~$H$ zvý¹í, co¾ znamená, ¾e~nìjaký vrchol s~pøebytkem v~nejvy¹¹í hladinì byl o~1 zvednut, nebo se~$H$ sní¾í. My víme, ¾e zvednutí je v~celém algoritmu $\O(N^2)$. Zároveò si~mù¾eme uvìdomit, ¾e~$H$ je nezáporný potenciál, kdy sní¾ení i~zvý¹ení ho zmìní o~1, tedy poèet sní¾ení bude stejný jako poèet zvý¹ení, a~proto obojího je~$\O(N^2)$. Tudí¾ poèet fází je také~$\O(N^2)$. + +Je dùle¾ité, ¾e~bìhem jedné fáze provedeme nejvý¹e jedno nenasycené pøevedení z~ka¾dého vrcholu. Po~ka¾dém nenasyceném pøevedení po~hranì $uv$ se~toti¾ vynuluje pøebytek v~$u$ a~aby se~provedlo dal¹í nenasycené pøevedení z~vrcholu~$u$, muselo by nejdøíve být co~pøevádìt. Muselo by tedy do~$u$ nìco pøitéci. My ale víme, ¾e pøevádíme pouze shora dolù a~$u$ je v~nejvy¹¹í hladinì (to zajistí právì to vylep¹ení algoritmu), tedy nejdøíve by musel být nìjaký jiný vrchol zvednut. Tím by se~ale zmìnilo~$H$ a~skonèila by tato fáze. + +Proto poèet v¹ech nenasycených pøevedení bìhem jedné fáze je nejvý¹e~$N$. A ji¾ jsme dokázali, ¾e~fází je~$\O(N^2)$. Tedy poèet v¹ech nenasycených pøevedení je~$\O(N^3)$. +\qed + +Tento odhad je hezký, ale stále není tìsný a~algoritmus se~chová lépe. Doka¾me si~je¹tì jeden tìsnìj¹í odhad na~poèet nenasycených pøevedení. + +\s{Lemma N'' (Nenasycená pøevedení):} Poèet nenasycených pøevedení je~$\O(N^2 \sqrt{M})$. + +\s{Poznámka:} Tato èasová slo¾itost je výhodná napøíklad pro~øídké grafy. Ty mají toti¾ pomìrnì malý poèet hran. + +\proof +Rozdìlme si~fáze na~dva druhy: laciné a~drahé podle toho, kolik se~v~nich provede nenasycených pøevedení. Zvolme si~nìjaké nezáporné~$K$. Zatím nebudeme urèovat jeho hodnotu. Uvidíme, ¾e~èasová slo¾itost algoritmu bude závislá na~tomto parametru~$K$. Proto jeho hodnotu zvolíme a¾ pozdìji a~to tak, aby byla slo¾itost co nejni¾¹í. + +{\I Laciné fáze} budou ty, bìhem nich¾ se~provede nejvý¹e~$K$ nenasycených pøevedení. {\I Drahé fáze} budou ty ostatní, tedy takové, bìhem nich¾ se~provede více jak~$K$ nenasycených pøevedení. + +Teï potøebujeme odhadnout, kolik nás budou stát oba typy fází. Zaènìme s~tìmi jednodu¹¹ími -- s~lacinými. Víme, ¾e~v¹ech fází je~$\O(N^2)$. Tìch laciných bude tedy urèitì také~$O(N^2)$. Nenasycených pøevedení se~bìhem jedné laciné fáze provede nejvíce~$K$. Tedy celkem se~bìhem laciných fází provede~$\O(N^2K)$ nenasycených pøevedení. + +Pro~poèet nenasycených pøevedení v~drahých fázích si~zaveïme nový potenciál definovaný následovnì: +$$\Phi := \sum_{\scriptstyle{v \ne z,s} \atop \scriptstyle{f^{\Delta}(v) \ne 0}} {p(v) \over K},$$ +kde~$p(v)$ je poèet takových vrcholù~$u$, které nejsou vý¹e ne¾~$v$. Neboli +$$p(v) = \vert \{ u \in V \mid h(u) \leq h(v) \} \vert.$$ +Tedy platí, ¾e~$p(v)$ je v¾dy nezáporné a~nejvý¹e má hodnotu~$N$. Dále víme, ¾e~$\Phi$ bude v¾dy nezáporné (nebo» je to souèet nezáporných èlenù) a~nejvý¹e bude nabývat hodnoty~$N^2 \over K$. Rozmysleme si, jak nám ovlivní tento potenciál na¹e tøi operace: +\itemize\ibull +\:{\bf Zvednutí}: Za~ka¾dý zvednutý vrchol pøibude nejvý¹e~$N \over K$ (tento vrchol mù¾e být nadzvednut nejvý¹e nad~v¹echny ostatní vrcholy) a~mo¾ná nìco ubude (napø. kdy¾ vrchol vyzvedneme na~úroveò k~ostatním). +\:{\bf Nasycené pøevedení} po~hranì $uv$: Mù¾e vynulovat pøebytek ve~vrcholu~$u$, pak se~$\Phi$ sní¾í. Mù¾e zvý¹it pøebytek ve~$v$ z~nuly, pak se~$\Phi$ zvý¹í. Ale nejvý¹e se~zvý¹í o~$N \over K$, nebo» do~$\Phi$ pøibude jen jeden sèítanec za~vrchol $v$ a~ten pøispìje nejvý¹e hodnotou~$N \over K$ (pod ním mù¾e být nejvíce~$N$ vrcholù). +\:{\bf Nenasycená pøevedení} po~hranì $uv$ v~drahých fázích: Tato operace vynuluje pøebytek v~$u$, tedy~$\Phi$ klesne alespoò o~$p(u) \over K$. Zároveò mù¾e zvý¹it pøebytek ve~$v$ z~nuly, ale~$\Phi$ stoupne nejvý¹e o~$p(v) \over K$. Celkem tedy~$\Phi$ klesne alespoò o~$p(u) - p(v) \over K$. +\endlist +Uvìdomme si, ¾e~pokud pøevádíme po~hranì~$uv$, tak platí, ¾e~$h(u) = h(v) + 1$. Pak~$p(u) - p(v)$ je pøesnì poèet vrcholù na~hladinì~$H$. Tìch je alespoò tolik, kolik je nenasycených pøevedení bìhem jedné fáze (to jsme dokázali ji¾ v~lemmatu N'), a~my jsme si~zadefinovali, ¾e v~drahé fázi je poèet nenasycených pøevedení alespoò~$K$. Tedy~$p(u) - p(v) > K$. Proto bìhem jednoho nenasyceného pøevedení~$\Phi$ klesne alespoò o~${K \over K} = 1$. Nenasycená pøevedení potenciál nezvy¹ují. + +Potenciál~$\Phi$ se~mù¾e zvìt¹it pouze pøi~operacích zvednutí a~nasycené pøevedení. Zvednutí se~provede celkem~$\O(N^2)$ a~ka¾dé zvý¹í potenciál nejvý¹e o~$N \over K$. Nasycených pøevedení se provede celkem~$\O(NM)$ a~ka¾dé zvý¹í potenciál takté¾ nejvý¹e o~$N \over K$. Celkem se~tedy~$\Phi$ zvý¹í nejvý¹e o +$${N \over K} \O(N^2) + {N \over K} \O(NM) = \O \left({N^3 \over K} + {N^2M \over K}\right).$$ + +Teï vyu¾ijeme toho, ¾e~$\Phi$ je nezáporný potenciál, tedy kdy¾ ka¾dé nenasycené pøevdení v~drahé fázi sní¾í~$\Phi$ alespoò o~1, tak v¹ech nenasycených pøevdení v~drahých fázích je~$\O({N^3 \over K} + {N^2M \over K})$. U¾ jsme ukázali, ¾e~nenasycených pøevední v~laciných fázích je~$\O(N^2K)$. Proto celkem v¹ech nenasycených pøevedení je +$$\O \left(N^2K + {N^3 \over K} + {N^2M \over K} \right) = \O \left(N^2K + {N^2M \over K} \right)$$ +(nebo» pro~souvislé grafy platí, ¾e~$M \geq N \Rightarrow N^2M \geq N^3$). A~my chceme, aby jich bylo co nejménì. Tato funkce má minimum tehdy, kdy¾ $N^2K = {N^2M \over K}$, èili $K = \sqrt{M}$. + +Proto v¹ech nenasycených pøevedení je $\O(N^2\sqrt{M})$. +\qed +\bye diff --git a/old/3-goldberg/Goldberg01.eps b/old/3-goldberg/Goldberg01.eps new file mode 100644 index 0000000..70be6d9 --- /dev/null +++ b/old/3-goldberg/Goldberg01.eps @@ -0,0 +1,517 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: inkscape 0.46 +%%Pages: 1 +%%Orientation: Portrait +%%BoundingBox: 0 0 311 196 +%%HiResBoundingBox: 7.6535002e-09 -2.5149246e-06 310.80097 195.23405 +%%EndComments +%%Page: 1 1 +0 196 translate +0.8 -0.8 scale +0 0 0 setrgbcolor +[] 0 setdash +1 setlinewidth +0 setlinejoin +0 setlinecap +gsave [1 0 0 1 0 0] concat +gsave [1 0 0 1 -7.2922934 -15.871617] concat +gsave [1 0 0 1 -105.66802 -41.295546] concat +0 0 0 setrgbcolor +[] 0 setdash +0.80000001 setlinewidth +0 setlinejoin +0 setlinecap +newpath +327.93522 188.25911 moveto +327.93522 250.38705 279.87044 300.80972 220.64777 300.80972 curveto +161.42509 300.80972 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+grestore +grestore +grestore +showpage +%%EOF diff --git a/old/3-goldberg/Makefile b/old/3-goldberg/Makefile new file mode 100644 index 0000000..6330e4c --- /dev/null +++ b/old/3-goldberg/Makefile @@ -0,0 +1,3 @@ +P=3-goldberg + +include ../Makerules diff --git a/old/4-hradla/4-hradla.tex b/old/4-hradla/4-hradla.tex new file mode 100644 index 0000000..ccb0c50 --- /dev/null +++ b/old/4-hradla/4-hradla.tex @@ -0,0 +1,139 @@ +\input ../lecnotes.tex + +\prednaska{4}{Hradlové sítì}{(zapsal: Petr Jankovský)} + +\def\land{\mathbin{\&}} + +Výkon poèítaèù nelze zvy¹ovat donekoneèna a pøesto¾e ji¾ pìkných pár let platí, +¾e se jejich rychlost s~èasem exponenciálnì zvìt¹uje, jednou urèitì narazíme +pøinejmen¹ím na~fyzikální limity. + +Kdy¾ tedy nemù¾eme zvy¹ovat rychlost jednoho procesoru, jak poèítat rychleji? +Øe¹ením by mohlo být poøídit si procesorù víc. U¾~dnes na~bì¾ném PéCéèku máme +k~dispozici vícejádrové procesory, díky nim¾ mù¾eme vyu¾ít pararelní poèítání +a úlohu øe¹it tak, ¾e práci ¹ikovnì rozdìlíme mezi procesory (èi jádra) a +zamìstnáme je pøi~výpoètu v¹echny. + +My se podíváme na~abstraktní výpoèetní model, který je je¹tì paralelnìj¹í. +Techniky, které si uká¾eme na~tomto modelu, se v¹ak dají pøekvapivì vyu¾ít i~pøi~reálném +paralelizování na~nìkolika málo procesorech. Konec koncù i proto, ¾e vnitøní +architektura procesoru se na¹emu modelu velmi podobá. Budeme se zabývat +jednoduchým modelem paralelního poèítaèe, toti¾ hradlovou sítí. + +\h{Hradlové sítì} + +\s{Definice:} {\I Hradlo} je prvek, který umí vyhodnocovat nìjakou funkci +nad~koneènou abecedou $\Sigma$. + +Obecnì se na~hradlo díváme jako na~funkci $f: {\Sigma}^{k} \rightarrow \Sigma$, která dostane $k$ vstupù +a~vrátí jeden výstup, pøièem¾ hodnoty, nad~kterými pracuje, budou z~nìjaké koneèné +abecedy -- tedy z~nìjaké koneèné mno¾iny symbolù $\Sigma$. Písmenku $k$ zde øíkáme {\I arita +hradla}. + +\s{Pøíklad:} Èasto studujeme hradla booleovská (pracující nad abecedou $\{0,1\}$), která poèítají logické funkce. + +Z~nich nejèastìji potkáme: + +\itemize\ibull +\:nulární: to jsou konstanty (FALSE=0, TRUE=1), +\:unární: napø. negace (znaèíme~$\lnot$), +\:binární: logický souèin ({\sc and},~$\land$), souèet ({\sc or},~$\lor$), ... +\endlist + +\>Hradla kreslíme tøeba následovnì: + +\figure{hradlo_and.eps}{Binární hradlo provádící logickou operaci {\sc and}.}{1in} + +Jednotlivá hradla mù¾eme navzájem urèitým zpùsobem propojovat a vytváøet +z nich {\I hradlové sítì}. Pokud pou¾íváme pouze booleovská hradla, øíkáme takto vytvoøeným +sítím {\I booleovské obvody}. Pokud pracujeme s~operacemi nad nìjakou obecnìj¹í (ale koneènou) +mno¾inou symbolù (abecedou), nazývají se {\I kombinaèní obvody.} + +Ka¾dá hradlová sí» má nìjaké vstupy, nìjaké výstupy a uvnitø jsou propojovaná +hradla. Ka¾dý vstup hradla je pøipojen buïto na~nìkterý ze~vstupù sítì nebo +na~výstup jiného hradla. Výstupy hradel mohou být propojeny na~vstupy dal¹ích +hradel (mohou se vìtvit), nebo na výstupy sítì. Pøitom máme zakázáno vytváøet +cykly. + +Ne¾ si øekneme formální definici, podívejme se na obrázek. + +\figure{hradlova_sit.eps}{Hradlová sí» -- tøívstupová verze funkce {\I majorita}.}{3in} + +Obrazek znázoròuje hradlovou sí», která poèítá, zda je alespoò na~dvou ze~vstupù +jednièka. Pojïme si ale {\I hradlovou sí»} definovat formálnì. + +\s{Definice:} {\I Hradlová sí»} je urèena: +\itemize\ibull +\:{\I abecedou} $\Sigma$ (to je nìjaká koneèná mno¾ina symbolù, obvykle $\Sigma=\{0,1\}$); +\:po dvou disjunktními koneènými mno¾inami $I$~({\I vstupy}), $O$~({\I výstupy}) \hfil\break a~$H$~({\I hradla}); +\:acyklickým orientovaným multigrafem~$(V,E)$, kde~$V = I \cup O \cup H$; +\:zobrazením~$F$, které ka¾dému hradlu $h\in H$ pøiøadí nìjakou funkci~$F(h): + \Sigma^{a(h)} \rightarrow \Sigma$, co¾ je funkce, kterou toto hradlo vykonává. + Èíslu $a(h)$ øíkáme {\I arita} hradla~$h$; +\:zobrazením~$z: E \rightarrow {\bb N}$, které ka¾dé hranì vedoucí do~nìjakého + hradla pøiøazuje nìkterý ze vstupù tohoto hradla. +\endlist + +\>Pøitom jsou splnìny následující podmínky: + +\itemize\ibull +\:$\forall i \in I: \deg^{in}(i)=0$ (do~vstupù nic nevede); +\:$\forall o \in O: \deg^{in}(o)=1~\land~\deg^{out}(o)=0$ (z~výstupù nic nevede a do~ka¾dého vede právì jedna hrana); +\:$\forall h \in H: \deg^{in}(v)=a(v)$ (do~ka¾dého hradla vede tolik hran, kolik je jeho arita); +\:$\forall h \in H~\forall j: 1\le j\le a(h)$ existuje právì jedena hrana~$e$ taková, ¾e~$e$ konèí v~$h$ a~$z(e)=j$, + (v¹echny vstupy hradel jsou zapojeny). +\endlist + +\s{Pozorování:} Kdybychom pøipustili hradla s~libovolnì vysokým poètem vstupù, mohli +bychom libovolný problém se vstupem délky~$n$ vyøe¹it jedním hradlem o~$n$~vstupech, +kterému bychom pøiøadili funkci, která by na¹i úlohu rovnou vyøe¹ila. Tento model +v¹ak není ani realistický, ani pìkný. Proto pøijmìme omezení, ¾e~arity v¹ech hradel +budou omezeny nìjakou pevnou konstantou $k$ (uká¾e se, ¾e nám bude staèit dvojka +a~vystaèíme si tedy pouze s nulárními, unárními a binárními hradly). +Následující obrázky ukazují, jak hradla o~více vstupech nahradit dvouvstupovými: + +\twofigures{hradlo_ternor.eps}{Trojvstupové hradlo \sc or.}{0.5in}{hradlo_ternbior.eps}{Jeho nahrazení 2-vstupovými hradly.}{0.6in} + + +Nyní bychom je¹tì mìli definovat, co taková hradlová sí» vlastnì poèítá a~jak +její výpoèet probíhá. + +\s{Definice:} {\I Výpoèet sítì} probíhá po~{\I taktech.} V nultém taktu jsou definovány pouze +hodnoty na~vstupech sítì a na~výstupech hradel arity 0. Mù¾eme si to pøedstavit +tak, ¾e na~zaèátku nemá ¾ádné hradlo definovánu výstupní hodnotu (a¾ na ji¾ +zmínìná hradla nulární). V~ka¾dém dal¹ím taktu pak vydají výstup hradla, která +na~konci minulého taktu mìla definovány v¹echny hodnoty na vstupech. Jakmile +budou po~nìjakém koneèném poètu taktù definované i hodnoty v¹ech výstupù, sí» +se zastaví a~vydá výsledek. + +\s{Pozorování:} Proto¾e je sí» acyklická, je jasné, ¾e jakmile jednou nìjaké hradlo vydá +výstup, tak se tento výstup bìhem dal¹ího výpoètu sítì ji¾ nezmìní. + +\figure{vypocet_site.eps}{Výpoèet hradlové sítì.}{6cm} + +\>Podle toho, jak sí» poèítá, si ji mù¾eme rozdìlit na~vrstvy: + +\s{Definice:} {\I $i$-tá vrstva $S_i$} obsahuje právì takové vrcholy~$v$, pro~které nejdel¹í cesta ze~vstupù +sítì do $v$ má délku rovnou~$i$. + +\s{Pozorování:} V¹imnìme si, ¾e v $i$-tém taktu vydají hodnoty právì hradla z $S_i$. + +Dává tedy smysl prohlásit za~{\I èasovou slo¾itost} sítì poèet +jejích vrstev. Podobnì {\I prostorovou slo¾itost} definujeme jako poèet hradel v~síti. + +\s{Pøíklad:} Sestrojme sí», která zjistí, zda se mezi jejími~$n$ vstupy +vyskytuje alespoò jedna jednièka. + +\>{\I První øe¹ení:} zapojíme hradla za~sebe (sériovì). Èasová i prostorová +slo¾itost odpovídají~$\Theta(n)$. Zde ov¹em vùbec nevyu¾íváme toho, ¾e by mohlo poèítat více +hradel souèasnì. + +\figure{hloupy_or.eps}{Hradlová sí», která zjistí, zdali je na vstupu alespoò jedna jednièka.}{0.7in} + +\>{\I Druhé øe¹ení:} Hradla budeme spojovat do~dvojic, pak výsledky z~tìchto dvojic opìt +do~dvojic a tak dále. Díky paralelnímu zapojení dosáhneme èasové slo¾itosti $\Theta(\log n)$, +prostorová slo¾itost zùstane lineární. + +\figure{chytry_or.eps}{Chytøej¹í øe¹ení stejného problému pro vstup velikosti 16.}{3in} + +\bye diff --git a/old/4-hradla/Makefile b/old/4-hradla/Makefile new file mode 100644 index 0000000..65a25ae --- /dev/null +++ b/old/4-hradla/Makefile @@ -0,0 +1,3 @@ +P=4-hradla + +include ../Makerules diff --git a/old/4-hradla/chytry_or.eps b/old/4-hradla/chytry_or.eps new file mode 100644 index 0000000..3d88cff --- /dev/null +++ b/old/4-hradla/chytry_or.eps @@ -0,0 +1,2324 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: inkscape 0.46 +%%Pages: 1 +%%Orientation: Portrait +%%BoundingBox: 2 325 592 773 +%%HiResBoundingBox: 2.9557909 325.79571 591.83322 772.94254 +%%EndComments +%%Page: 1 1 +0 842 translate +0.8 -0.8 scale +0 0 0 setrgbcolor +[] 0 setdash +1 setlinewidth +0 setlinejoin +0 setlinecap +gsave [1 0 0 1 0 0] concat +gsave [1.086765 0 0 1.086765 -114.17957 349.28806] concat 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bitonické tøídìní}{(zapsal: Petr Jankovský)} + +Minule jsme si zavedli paralelní výpoèetní model, ve kterém si nyní nìco naprogramujeme \dots + +\h{Sèítání binárních èísel} + +Mìjme dvì èísla $x$ a $y$ zapsané ve~dvojkové soustavì. Jejich èíslice oznaème +$x_{n-1}\ldots x_0$ a $y_{n-1}\ldots y_0$, kde $i$-tý øád má váhu $2^i$. Nyní budeme chtít tato èísla +seèíst. + +K tomuto úèelu se~ihned nabízí pou¾ít starý dobrý \uv{¹kolní algoritmus}, který +funguje ve~dvojkové soustavì stejnì dobøe jako v~desítkové. Zkrátka sèítáme èísla +zprava doleva. V¾dy seèteme pøíslu¹né èíslice pod~sebou a~pøièteme pøenos z~ni¾¹ího +øádu. Tím dostaneme jednu èíslici výsledku a~pøenos do~vy¹¹ího øádu. Formálnì +bychom to mohli zapsat tøeba takto: +$$ +z_i=x_i \oplus y_i \oplus c_i, +$$ +kde $z_i$ je $i$-tá èíslice souètu, $\oplus$ znaèí operaci {\sc xor} (souèet modulo~2) a~$c_i$ je {\I pøenos} +z~$(i-1)$-ního øádu do~$i$-tého. Pøenos pøitom nastane tehdy, pokud se~nám potkají +dvì jednièky pod~sebou, nebo kdy¾ se~vyskytne alespoò jedna jednièka a~k~tomu +pøenos z~ni¾¹ího øádu. Jinými slovy tehdy, kdy¾ ze~tøí xorovaných èíslic jsou alespoò +dvì jednièky (pomocí obvodu pro majoritu z minulé pøedná¹ky lehce zkonstruujeme): +$$ +\eqalign{ +c_{0} &= 0, \cr +c_{i+1} &= (x_i~\&~y_i)\lor(x_i~\&~c_i)\lor(y_i~\&~c_i).\cr +} +$$ + +Takovéto sèítání sice perfektnì funguje, nicménì je bohu¾el pomìrnì pomalé. +Pokud bychom stavìli hradlovou sí» podle tohoto pøedpisu, byla by slo¾ená z~nìjakých +podsítí (\uv{krabièek}), které budou mít na~vstupu $x_i$, $y_i$ a~$c_i$ a jejich výstupem +bude $z_i$ a~$c_{i+1}$. + +\figure{hloupe_scitani.eps}{Sèítání ¹kolním algoritmem.}{1.5in} + +V¹imìme si, ¾e~ka¾dá krabièka závisí na~výstupu té pøedcházející. Jednotlivé +krabièky tedy musí urèitì le¾et na~rùzných hladinách. Celkovì bychom museli pou¾ít +$\Theta{(n)}$ hladin a~jeliko¾ je ka¾dá krabièka konstantnì velká, také $\Theta{(n)}$ hradel. To dává +lineární èasovou i~prostorovou slo¾itost, èili oproti sekvenènímu algoritmu jsme si nepomohli. + +Zamysleme se nad tím, jak by se proces sèítání mohl zrychlit. + +\h{Pøenosy v~blocích} +Jediné, co nás pøi sèítání brzdí, jsou pøenosy mezi jednotlivými øády. Ka¾dý øád, +aby~vydal souèet, musí poèkat na~to, a¾~dopoèítají v¹echny pøedcházející øády. +Teprve pak se~toti¾ dozví pøenos. Kdybychom ov¹em pøenosy dokázali spoèítat +nìjakým zpùsobem paralelnì, máme vyhráno. Jakmile známe v¹echny pøenosy, souèet +u¾~zvládneme dopoèítat na~konstantnì mnoho hladin -- tedy v~konstantním èase. + +Podívejme se na~libovolný {\I blok} v~na¹em souètu. Tak budeme øíkat èíslùm +$x_j\ldots x_i$ a $y_j\ldots y_i$ v~nìjakém intervalu indexù $\left$. Pøenos $c_{j+1}$ vystupující z~tohoto bloku závisí mimo hodnot sèítancù u¾ pouze na~pøenosu $c_{i}$, který do bloku vstupuje. + +\figure{blok_scitani.eps}{Blok souètu.}{3in} + +Z tohoto pohledu se mù¾eme na blok také dívat jako na nìjakou funkci, která +dostane jednobitový vstup a vydá jednobitový výstup. To je nám pøíjemné, nebo» +takových funkcí existují jenom ètyøi typy: +\numlist\ndotted +\:$f(x) = 0$, ~~~~(0) +\:$f(x) = 1$, ~~~~(1) +\:$f(x) = x$, ~~~~($<$ -- kopírování) +\:$f(x) = \neg{x}$. +\endlist +Jak se za~chvíli uká¾e, poslední pøípad, kdy by~nìjaký blok pøedával opaèný +pøenos, ne¾ do~nìj vstupuje, navíc nikdy nemù¾e nastat. Pojïme si to rozmyslet. +Jednobitové bloky se chovají velice jednodu¹e: + +\figure{bloky_1bit.eps}{Tabulka triviálních blokù.}{1.1in} + +Z prvního bloku evidentnì v¾dy vyleze 0, a»~do~nìj vstoupí jakýkoli pøenos. +Poslední blok naopak sám o~sobì pøenos vytváøí, a»~ji¾ do~nìj vleze jakýkoliv. +Bloky prostøední se chovají stejnì, a~to tak, ¾e~samy o~sobì ¾ádný pøenos nevytvoøí, +ale~pokud do~nich nìjaký pøijde, tak~také odejde. + +Mìjme nyní nìjaký vìt¹í blok~$C$ slo¾ený ze~dvou men¹ích podblokù $A$ a~$B$, jejich¾ +chování u¾ známe. Z~toho mù¾eme odvodit, jak se chová celý blok: + +\figure{tabulka_skladani_bloku.eps}{Skládání chování blokù.}{1.3in} + +Pokud vy¹¹í blok pøenos pohlcuje, pak a»~se u¾~ni¾¹í blok chová jakkoli, slo¾ení +tìchto blokù musí v¾dy pohlcovat. V~prvním øádku tabulky jsou tudí¾ nuly. Analogicky, +pokud vy¹¹í blok generuje pøenos, tak~ten ni¾¹í na~tom nic nezmìní. V~druhém +øádku tabulky jsou tedy samé jednièky. Zajímavìj¹í pøípad nastává, pokud vy¹¹í blok +kopíruje -- tehdy zále¾í èistì na~chování ni¾¹ího bloku. + +V¹imnìme si, ¾e~skládání chování blokù je vlastnì úplnì obyèejné skládání +funkcí. Nyní bychom mohli prohlásit, ¾e~budeme poèítat nad~tøíprvkovou abecedou, +a~¾e~celou tabulku doká¾eme spoèítat jedním jediným hradlem. Pojïmì si v¹ak +rozmyslet, jak~bychom takovouto vìc popsali èistì binárnì. Jak tedy tyto tøi stavy +popisovat pouze nìkolika bity? + +Evidentnì nám k tomuto binárnímu zakódování tøí stavù budou staèit bity dva. +Oznaème si je jako $p$ a $q$. Tato dvojice mù¾e nabývat hned ètyø mo¾ných hodnot, +kterým pøiøadíme tøi mo¾ná chování bloku. Toto kódování mù¾eme zvolit zcela +libovolnì, ale pokud si ho zvolíme ¹ikovnì, u¹etøíme si dále práci pøi kompozici. +Zvolme si tedy kódování takto: + +\itemize\ibull +\:$(1,*) = <$, +\:$(0,0) = 0$, +\:$(0,1) = 1$ +\endlist + +Tomu, ¾e blok kopíruje, odpovídá dvojice $p = 1$; $q =$ \. V~ostatních +pøípadech bude~$p$ nulové a~$q$ nám bude øíkat, co je na~výstupu pøíslu¹ného bloku. +Jinými slovy $p = 0$ znamená, ¾e funkce je konstanta, pøièem¾ $q$ øíká jaká; naproti +tomu $p = 1$~znamená, ¾e funkce je identita, a»~u¾~je $q$ cokoli. + +Pojïme si nyní ukázat, jak bude celé skládání blokù vypadat. Rozmysleme si, +kdy je~$p$ celého dvojbloku jednièkové, tedy kdy celý dvojblok kopíruje. To nastane +tehdy, pokud kopírují obì jeho èásti, a tedy $p = p_a~\&~p_b$. Dále $q$ bude rovno jednièce, +pokud $q = (\neg{p_a}~\&~q_a) \lor (p_a~\&~q_b)$. + +Skládání chování blokù lze tedy popsat buï ternárnì -- tabulkou, ale lze to +i~binárnì vý¹e uvedeným pøedpisem. + +Nyní si tedy mù¾eme dopøedu vypoèítat chování bloku velikosti jedna, poté + z~nich skládáním blokù velikosti dva, dál velikosti ètyøi, osm, atd \dots + +\h{Paralelní sèítání} + +Paralelní algoritmus na~sèítání u¾~zkonstruujeme pomìrnì snadno. Bez újmy +na~obecnosti budeme pøedpokládat, ¾e~poèet bitù vstupních èísel je mocnina dvojky, +jinak si vstup doplníme nulami, co¾ výsedný èas bìhu algoritmu zhor¹í maximálnì +konstanta-krát. + +\algo +\:Spoèteme chování blokù velikosti~1. ($\O(1)$ hladin) +\:Postupnì poèítáme chování blokù\foot{myslíme \uv{pøirozenì zarovnané} bloky, tedy takové, jejich¾ poloha je násobkem velikosti} velikosti 2, 4, 8, ..., $2^k$. + ($\O(\log n)$ hladin, na~nich¾ se skládají bloky) +\:$c_0 \leftarrow 0$ (pøenos do nejni¾¹ího øádu je v¾dy 0) +\:Urèíme $c_n$ podle $c_0$ a chování (jediného) bloku velikosti~$n$. +\:Postupnì dopoèítáme pøenosy na~hranicích dìlitelných $2^k$ \uv{zahu¹»ováním}: + jakmile víme $c_{2^k}$, mù¾eme dopoèítat $c_{2^k+2^{k-1}}$ podle + chování bloku $\left< 2^k, 2^k+2^{k-1}\right>$. ($\O(\log n)$ hladin, + na~nich¾ se dosazuje) +\:Seèteme: $\forall i: z_i = x_i \oplus y_i \oplus c_i$. +\endalgo + +\figure{1_9_deleni_bloku.eps}{Výpoèet pøenosu.}{2.5in} + +Algoritmus pracuje v~èase $\O(\log n)$. Hradel je pou¾ito lineárnì: na~jednotlivých +hladinách kroku~2 poèet hradel exponenciálnì klesá od~$n$ k~1, na~hladinách kroku~5 +exponenciálnì stoupá od~1 k~$n$, tak¾e dohromady se seète na~$\Theta(n)$. + +\h{Tøídìní} + +Nyní se podíváme na~druhý problém, a~to na~problém tøídìní. Ji¾ známe pomìrnì efektivní sekvenèní algoritmy, které dovedou tøídit v~èase $\O(n\log n)$. Byli bychom jistì rádi, kdybychom to zvládli je¹tì rychleji. Pojïme se podívat, zda by nám v tom nepomohlo problém paralelizovat. + +Budeme pøi tom pracovat ve~výpoèetním modelu, kterému se øíká komparátorová sí». Ta je postavená z~hradel, kterým se øíká komparátory. + +\s{Definice:} {\I Komparátorová sí»} je kombinaèní obvod, jeho¾ hradla jsou +komparátory. + +\figure{sortnet.0}{Komparátor}{0.7in} + +Komparátor umí porovnat dvì hodnoty a~rozhodnout, která z~nich je vìt¹í +a~která men¹í. Nevrací v¹ak booleovský výsledek jako bì¾né hradlo, ale má dva +výstupy, pøièem¾ na~jednom vrátí men¹í ze~vstupních hodnot a~na~druhém vìt¹í. + +Výstupy komparátorù se nevìtví. Nemù¾eme tedy jeden výstup \uv{rozdvojit} a~pøipojit ho na~dva vstupy. (Vìtvení by dokonce ani nemìlo smysl, proto¾e zatímco rozdvojit bychom mohli, slouèit u¾~ne. Pokud tedy chceme, aby sí» mìla $n$~vstupù i~$n$~výstupù, rozdvojení stejnì nesmíme provést, i kdybychom jej mìli povolené.) + +\s{Pøíklad:} {\sl Bubble sort} + +Obrázek Bubble 1 ilustruje pou¾ití komparátorù pro tøídìní Bubble sortem. +©ipky pøedstavují jednotlivé komparátory. Výpoèet v¹ak je¹tì mù¾eme vylep¹it. + +\twofigures{sortnet.1}{Bubble 1}{143pt}{sortnet.2}{Bubble 2}{143pt} + +Sna¾íme se výpoèet co nejvíce paralelizovat (viz obrázek Bubble 2). +Jak je vidìt, komparátory na sebe nemusejí èekat. Tím mù¾eme výpoèet urychlit a místo èasu $\Theta{(n^2)}$ docílit èasové slo¾itosti $\Theta{(n)}$. V obou pøípadech je zachován kvadratický prostor. + +Nyní si uká¾eme je¹tì rychlej¹í tøídící algoritmus. Pùjdeme na nìj v¹ak trochu \uv{od lesa}. Nejdøíve vymyslíme sí», která bude umìt tøídit jenom nìco -- toti¾ bitonické posloupnosti. Bez újmy na obecnosti budeme pøedpokládat, ¾e ka¾dé dva prvky na vstupu jsou navzájem rùzné. + +\medskip +\s{Definice:} Øekneme, ¾e posloupnost $x_0,\dots,x_{n-1} $ je {\I èistì bitonická} právì tehdy, kdy¾ +pro nìjaké $x_k, k\in\{0, \dots, n-1\} $ platí, ¾e~v¹echny prvky pøed ním (vèetnì jeho samotného) +tvoøí rostoucí poslopnost, kde¾to prvky stojící za~ním tvoøí poslopnost klesající. +Formálnì zapsáno musí platit, ¾e: +$$x_0\leq x_1\leq \dots \leq x_k \geq x_{k+1}\geq\dots \geq x_{n-1}.$$ + +\s{Definice:} Posloupnost $x_0 \dots x_{n-1}$ je {\I bitonická}, právì kdy¾ $\exists~j\in \{0,\dots ,n-1\}$, pro +které je rotace pùvodní posloupnosti o $j$ prvkù, tedy posloupnost +$$x_j,x_{(j+1) \bmod n},\dots, x_{(j+n-1) \bmod n},$$ èistì bitonická. + +\s{Definice:} {\I Separátor $S_n$} je sí», ve které jsou v¾dy~$i$-tý a~$(i+{n/2})$-tý prvek vstupu +(pro $i=0,\dots, {n/2}-1$) propojeny komparátorem. Minimum se pak stane~$i$-tým, +maximum $(i+{n/2})$-tým prvkem výstupu. +\figure{sortnet.3}{$(y_i, y_{i+{n/2}}) = \(x_i, x_{i+{n/2}})$} {300pt} + +\s{Lemma:} Pokud separátor dostane na~vstupu bitonickou posloupnost, pak jeho výstup $y_0, \dots, y_{n-1}$ +splòuje: + +(i) $y_0,\dots, y_{n/2 -1}$ a~$y_{n/2},\dots, y_{n-1}$ jsou +bitonické posloupnosti, + +(ii) Pro v¹echna $i,j< {n/2}$ platí $y_i < y_{j + {n/2}}$. + +Separátor nám tedy jednu bitonickou posloupnost na~vstupu rozdìlí na~dvì +bitonické posloupnosti, pøièem¾ navíc ka¾dý prvek první posloupnosti ($y_0,\dots, y_{n/2 -1}$) +je men¹í nebo roven prvkùm druhé posloupnosti ($y_{n/2}, \dots, y_{n-1}$). + +\>Ne¾ pøistoupíme k dùkazu lemmatu, uka¾me si, k èemu se nám bude hodit. + + +\s{Definice:} {\I Bitonická tøídièka $B_n$} je obvod sestavený ze separátorù, který dostane-li na vstupu bitonickou posloupnost délky $n$ (BÚNO konstruujeme tøídièku pro $n=2^k$), vydá setøídìnou zadanou posloupnost délky $n$. + +Tøídièka dostane na~vstupu bitonickou posloupnost. Separátor~$S_n$ ji pak dle lemmatu rozdìlí na~dvì bitonické posloupnosti, kdy je ka¾dý prvek z~první posloupnosti men¹í ne¾ libovolný prvek z~druhé. Tyto poloviny pak dal¹í separátory rozdìlí na~ètvrtiny, ..., a¾~na~konci zbudou pouze jednoduché posloupnosti délky jedna (zjevnì setøídìné), které mezi sebou mají po¾adovanou nerovnost -- tedy ka¾dá posloupnost (nebo spí¹e prvek) nalevo je $\leq$ ne¾ prvek napravo od~nìj. + +\centerline{\epsfbox{sortnet.5}} + +Jak je vidìt, bitonická tøídièka nám libovolnou bitonickou posloupnost délky~$n$ +setøídí na~$\Theta(\log n)$ hladin. + +Nyní se dá odvodit, ¾e pokud umíme tøídit bitonické posloupnosti, umíme setøídit v¹echno. +Vzpomeòme si na~tøídìní sléváním -- Merge sort. To funguje tak, ¾e zaène s~jednoprvkovými posloupnostmi, které jsou evidentnì setøídìné, a~poté v¾dy dvì setøídìné posloupnosti slévá do~jedné. Kdybychom nyní umìli paralelnì slévat, mohli bychom vytvoøit i~paralelní Merge sort. Jinými slovy, potøebujeme dvì rostoucí posloupnosti nìjak efektivnì slít do~jedné setøídìné. Uvìdomme si, ¾e to zvládneme jednodu¹e -- staèí druhou z~posloupností obrátit a~\uv{pøilepit} za~první, èím¾ vznikne bitonická posloupnost, kterou poté mù¾eme setøídit na¹í bitonickou tøídièkou. + +\s{Pøíklad:} {\sl Merge sort} + +Bitonická tøídièka se tedy dá pou¾ít ke~slévání setøídìných posloupností. +Uka¾me si, jak s~její pomocí sestavíme souèástky {\I slévaèky} $M_n$: + +Setøídìné posloupnosti $x_0,\dots, x_{n-1}$ a~$y_0,\dots, y_{n-1}$ spojíme do jedné bitonické +posloupnosti $x_0,\dots, x_{n-1},y_{n-1},\dots, y_0$. Z~této posloupnosti vytvoøíme pomocí bitonické tøídièky +$B_{2n}$ setøídìnou posloupnost. Vytvoøíme tedy blok $M_{2n}$, který se ov¹em sestává de~facto pouze z~bloku $B_{2n}$, jeho¾ druhá polovina vstupù je zapojena v~obráceném poøadí. +\figure{sortnet.6}{Paralelní MergeSort.}{3in} + +Nyní se pokusme odhadnout èasovou slo¾itost. Ná¹ MergeSort bude mít øádovì hloubku blokù $\log n$. +V ka¾dém bloku $M_n$ je navíc ukryta bitonická tøídièka s takté¾ logaritmickou hloubkou. +Celková hloubka tedy bude $\log 2 + \log4 + \dots + \log 2^k + \dots + \log n$. +Po seètení nakonec dostáváme výslednou èasovou slo¾itost $\Theta (\log^2 n)$. + +Dodejme je¹tì, ¾e existuje i~tøídicí algoritmus, kterému staèí jen ${\O}(\log n)$ hladin. +Jeho multiplikativní konstanta je v¹ak pøíli¹ veliká, tak¾e je v~praxi nepou¾itelný. + +Vra»me se nyní k dùkazu lemmatu, který jsme na pøedminulé stránce vynechali. + +\h{Dùkaz Lemmatu:} +(i) Nejprve nahlédneme, ¾e lemma platí, je-li vstupem èistì bitonická +posloupnost. Dále BÚNO pøedpokládejme, ¾e vrchol posloupnosti je v~první polovinì (kdyby +byl vrchol za~polovinou, staèilo by zrcadlovì obrátit posloupnost i~komparátory a~øe¹ili +bychom stejný problém). Nyní si definujme $k := \min j: x_j > x_{j+n/2}$. (Pokud +by~takové~$k$ neexistovalo, znamenalo by~to, ¾e vstupní posloupnost je monotónní. +Separátor by tedy nic nedìlal a~pouze zkopíroval vstup na~výstup, co¾ jistì lemma splòuje.) Nyní si v¹imìme, +¾e jakmile jednou zaène platit, ¾e prvek na~levé stranì je men¹í ne¾ na~pravé, bude +nám tato relace platit a¾~do~konce. Oznaème vrchol vstupní posloupnosti jako~$x_m$. +Pak~$k$ bude jistì men¹í ne¾~$m$ a~$k+{n/2}$ bude vìt¹í ne¾~$m$. Mezi~$k$ a~$m$ je tedy +vstupní posloupnost neklesající, mezi $k+{n/2}$ a~$n-1$ nerostoucí. + +Do pozice~$k$ tedy separátor bude pouze kopírovat vstup na~výstup, od~pozice~$k$ +dál u¾~jen prohazuje. Pro ka¾dé~$i$, $(k\leq i\leq {n/2}-1)$ se prvky +$x_i$ a~$x_{i+{n/2}}$ prohodí. Úsek mezi~$k$ a~${n/2}-1$ tedy +nahradíme nerostoucí posloupností, první polovina výstupu tedy bude +(dokonce èistì) bitonická. Podobnì úsek $k+{n/2}$ a¾~$n-1$ nahradíme èistì +bitonickou posloupností. Obì poloviny tedy budou bitonické. + +Dostaneme-li na vstupu obecnou bitonickou posloupnost, pøedstavíme si, +¾e je to èistì bitonická posloupnost zrotovaná o~$r$ prvkù (BÚNO +doprava). Zjistíme, ¾e v~komparátorech se porovnávají tyté¾ prvky, +jako kdyby zrotovaná nebyla. Výstup se od výstupu èistì bitonické +posloupnosti zrotovaného o~$r$ bude li¹it prohozením úsekù $x_0$ a¾ +$x_{r-1}$ a~$x_{n/2}$ a¾ $x_{{n/2}+r-1}$. Obì výstupní +posloupnosti tedy budou zrotované o~$r$ prvkù, ale na jejich +bitoniènosti se nic nezmìní. + +(ii) Z dùkazu (i) pro èistì bitonickou posloupnost víme, ¾e $y_0\dots y_{n/2-1}$ je èistì bitonická a bude rovna $x_0\dots x_{k-1},x_{k+n/2}\dots x_{n-1}$ pro vhodné $k$ a navíc bude mít maximum v $x_{k-1}$ nebo $x_k+{n/2}$. Mezi tìmito body ov¹em ve vstupní posloupnosti urèitì nele¾el ¾ádný $x_i$ men¹í ne¾ $x_k-1$ nebo $x_k+{n/2}$ (jak je vidìt z obrázku) a posloupnost $x_k \dots x_{k-1+{n/2}}$ je rovna $y_{n/2}\dots y_{n-1}$. Pro obecné bitonické posloupnosti uká¾eme stejnì jako v (i). +\qed + +\medskip + +\centerline{\epsfbox{sortnet.7}} + + +\h{Paralelní násobení} + +Podobnì jako u~sèítání si vzpomeneme na~¹kolní algoritmus -- tentokráte v¹ak pro násobení. +To fungovalo tak, ¾e~jsme si jedno ze~dvou binárních èísel na~vstupu (øíkejme mu tøeba $x$) $n$-krát posouvali. Tam kde pak byly v~èísle~$y$ jednièky, pøíslu¹né kopie $x$ jsme seèetli. Jinými slovy tedy násobení umíme pøevést na~nìjaké posuny (ty lze realizovat pouze \uv{pøedrátováním} -- nic nás nestojí), násobení jedním bitem (co¾ je +{\sc and} ) a~nakonec potøebujeme výsledných~$n$ èísel seèíst. +\figure{skolni_scitani.eps}{©kolní sèítání.}{2in} + +Jak nyní seèíst $n$ $n$-bitových èísel..? Nabízí se vyu¾ít osvìdèený \uv{stromeèek} -- sèítat dvojice èísel, výsledky pak opìt po~dvojicích seèíst, a¾ na~konci vyjde jediný výsledek. +\figure{stromecek.eps}{Stromeèek}{1.4in} + +Toto øe¹ení by v¹ak vedlo na~èasovou slo¾itost $\Theta (\log^2 n)$, nebo» sèítat umíme v~èase $\Theta (\log n)$. To je sice dle na¹ich mìøítek +docela efektivní, ale pøekvapivì to jde je¹tì lépe -- toti¾ na~$\Theta (\log n)$ hladin. Této +slo¾itosti dosáhneme malým trikem. + +Vymyslíme obvod konstantní hloubky, který na~vstupu dostane tøi èísla. Odpoví pak dvìma èísly +takovými, ¾e budou mít stejný souèet jako pùvodní tøi èísla. Jinými slovy pomocí +tohoto obvodu budeme umìt seètení tøí èísel pøevést na seètení dvou èísel. + +\figure{obvod.eps}{$x+y+z = p+q$}{0.3in} + +V¹imnìme si, ¾e~kdy¾ sèítáme tøi bity, mù¾e být pøenos do~vy¹¹ího øádu nula èi~jednièka. Vezmeme si tedy bity $x_i$, $y_i$, $z_i$ a~ty seèteme. To nám dá dvoubitový výsledek, pøièem¾ ni¾¹í bit z~tohoto výsledku po¹leme do~èísla~$p$, vy¹¹í do~èísla~$q$. + +\figure{obvod_real.eps}{Redukování sèítání}{1.2in} + +Toto zredukování sèítání nám nyní umo¾ní opìt stavit strom, by» o malièko slo¾itìj¹í. + +\figure{sl_stromecek.eps}{Slo¾itìj¹í stromeèek}{0.9in} + +Pokud jsme mìli na~zaèátku $n$ èísel, po~první redukci nám jich zbývá jen $2/3 \cdot n$ a~obecnì v~$k$-té hladiné $(2/3)^k \cdot n$. Znamená to, ¾e èísel nám ubývá exponenciálnì, tak¾e poèet hladin bude logaritmický. Redukující obvod je pøi tom jen konstantnì hluboký, tak¾e celé redukování zvládneme v~èase $\Theta (\log n)$. Na~konci Slo¾itìj¹ího stromeèku pak máme umístìnou jednu obyèejnou sèítaèku, která zbývající dvì èísla seète v~logaritmickém èase. + +Seètení v¹ech $n$ èísel tedy zabere $\Theta (\log n)$ hladin. + +Kdy¾ se nyní vrátíme k~násobení, zbývá nám vyøe¹it posouvání a~{\sc and}ování. Uvìdomme si, ¾e to je plnì paralelení a~zvládneme ho za~konstantnì mnoho hladin. 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+draw(z21--z22--z23--z24--z34--z33--z32--z31--z21); +drawarrow(z22--z12); +drawarrow(z23--z13); +drawarrow(z42--z32); +drawarrow(z43--z33); + +label.bot(btex \strut a etex,z32); +label.bot(btex \strut b etex,z33); +label.top(btex min etex,z22); +label.top(btex max etex,z23); + + +endfig; + +beginfig(1); +v:=u*5mm; +z10=(0v,0v); +z20=(2v,0v); +z30=(4v,0v); +z40=(6v,0v); +z50=(8v,0v); + +z11=(0v,1v); +z12=(0v,2v); +z13=(0v,3v); +z14=(0v,4v); +z15=(0v,5v); +z16=(0v,6v); +z17=(0v,7v); +z18=(0v,8v); +z19=(0v,9v); +z110=(0v,10v); + +z21=(2v,1v); +z22=(2v,2v); +z23=(2v,3v); +z24=(2v,4v); +z25=(2v,5v); +z26=(2v,6v); +z27=(2v,7v); +z28=(2v,8v); +z29=(2v,9v); +z210=(2v,10v); + +z31=(4v,1v); +z32=(4v,2v); +z33=(4v,3v); +z34=(4v,4v); +z35=(4v,5v); +z36=(4v,6v); +z37=(4v,7v); +z38=(4v,8v); +z39=(4v,9v); +z310=(4v,10v); + +z41=(6v,1v); +z42=(6v,2v); +z43=(6v,3v); +z44=(6v,4v); +z45=(6v,5v); +z46=(6v,6v); +z47=(6v,7v); +z48=(6v,8v); +z49=(6v,9v); +z410=(6v,10v); + +z51=(8v,1v); +z52=(8v,2v); +z53=(8v,3v); +z54=(8v,4v); +z55=(8v,5v); +z56=(8v,6v); +z57=(8v,7v); +z58=(8v,8v); +z59=(8v,9v); +z510=(8v,10v); + +z111=(0v,11v); +z211=(2v,11v); +z311=(4v,11v); +z411=(6v,11v); +z511=(8v,11v); + +z1015=(-1v,1.5v); +z1615=(9v,1.5v); +z1035=(-1v,3.5v); +z1635=(9v,3.5v); +z1065=(-1v,6.5v); +z1665=(9v,6.5v); + +pickup pencircle scaled 0.4pt; +draw(z10--z111); +draw(z20--z211); +draw(z30--z311); +draw(z40--z411); +draw(z50--z511); +drawarrow(z11--z21); +drawarrow(z13--z23); +drawarrow(z16--z26); +drawarrow(z110--z210); +drawarrow(z22--z32); +drawarrow(z25--z35); +drawarrow(z29--z39); +drawarrow(z34--z44); +drawarrow(z38--z48); +drawarrow(z47--z57); + +pickup pencircle scaled 0.7pt; +draw(z1015--z1615) dashed withdots scaled 0.7; +draw(z1035--z1635) dashed withdots scaled 0.7; +draw(z1065--z1665) dashed withdots scaled 0.7; + +label.top(btex x1 etex,z111); +label.top(btex x2 etex,z211); +label.top(btex x3 etex,z311); +label.top(btex x4 etex,z411); +label.top(btex x5 etex,z511); + +endfig; + +beginfig(2); +v:=u*5mm; +z13=(0v,3v); +z23=(2v,3v); +z33=(4v,3v); +z43=(6v,3v); +z53=(8v,3v); + +z14=(0v,4v); +z15=(0v,5v); +z16=(0v,6v); +z17=(0v,7v); +z18=(0v,8v); +z19=(0v,9v); +z110=(0v,10v); + +z24=(2v,4v); +z25=(2v,5v); +z26=(2v,6v); +z27=(2v,7v); +z28=(2v,8v); +z29=(2v,9v); +z210=(2v,10v); + +z34=(4v,4v); +z35=(4v,5v); +z36=(4v,6v); +z37=(4v,7v); +z38=(4v,8v); +z39=(4v,9v); +z310=(4v,10v); + +z44=(6v,4v); +z45=(6v,5v); +z46=(6v,6v); +z47=(6v,7v); +z48=(6v,8v); +z49=(6v,9v); +z410=(6v,10v); + +z54=(8v,4v); +z55=(8v,5v); +z56=(8v,6v); +z57=(8v,7v); +z58=(8v,8v); +z59=(8v,9v); +z510=(8v,10v); + +z111=(0v,11v); +z211=(2v,11v); +z311=(4v,11v); +z411=(6v,11v); +z511=(8v,11v); + +pickup pencircle scaled 0.4pt; +draw(z13--z111); +draw(z23--z211); +draw(z33--z311); +draw(z43--z411); +draw(z53--z511); +drawarrow(z14--z24); +drawarrow(z16--z26); +drawarrow(z18--z28); +drawarrow(z110--z210); +drawarrow(z25--z35); +drawarrow(z27--z37); +drawarrow(z29--z39); +drawarrow(z36--z46); +drawarrow(z38--z48); +drawarrow(z47--z57); + +label.top(btex x1 etex,z111); +label.top(btex x2 etex,z211); +label.top(btex x3 etex,z311); +label.top(btex x4 etex,z411); +label.top(btex x5 etex,z511); + +endfig; + + +beginfig(3); +v:=u*5mm; + +z10=(0v,0v); +z15=(0v,5v); +z16=(0v,6v); + +z20=(2v,0v); +z24=(2v,4v); +z26=(2v,6v); + +z30=(4v,0v); +z33=(4v,3v); +z36=(4v,6v); + +z40=(6v,0v); +z42=(6v,2v); +z46=(6v,6v); + +z50=(8v,0v); +z51=(8v,1v); +z56=(8v,6v); + +z60=(10v,0v); +z65=(10v,5v); +z66=(10v,6v); + +z70=(12v,0v); +z74=(12v,4v); +z76=(12v,6v); + +z80=(14v,0v); +z83=(14v,3v); +z86=(14v,6v); + +z90=(16v,0v); +z92=(16v,2v); +z96=(16v,6v); + +z100=(18v,0v); +z101=(18v,1v); +z106=(18v,6v); + +z110=(20v,0v); +z116=(20v,6v); + + +pickup pencircle scaled 0.4pt; +draw(z10--z16); +draw(z20--z26); +draw(z30--z36); +draw(z40--z46); +draw(z50--z56); +draw(z60--z66); +draw(z70--z76); +draw(z80--z86); +draw(z90--z96); +draw(z100--z106); +drawarrow(z15--z65); +drawarrow(z24--z74); +drawarrow(z33--z83); +drawarrow(z42--z92); +drawarrow(z51--z101); + +label.top(btex $x_0$ etex,z16); +label.top(btex $x_1$ etex,z26); +label.top(btex $x_2$ etex,z36); +label.top(btex \dots etex,z66); +label.top(btex $x_{n-2}$ etex,z96); +label.top(btex $x_{n-1}$ etex,z106); + +endfig; + + + +beginfig(4); +v:=u*5mm; + +z1075=(7.5v,0v); + +z10=(0v,1v); +z175=(7.5v,1v); +z115=(15v,1v); + +z20=(0v,2v); +z235=(3.5v,2v); +z211=(11.5v,2v); +z215=(15v,2v); + +z30=(0v,3v); +z335=(3.5v,3v); +z37=(7v,3v); +z38=(8v,3v); +z311=(11.5v,3v); +z315=(15v,3v); + +z40=(0v,4v); +z415=(1.5v,4v); +z455=(5.5v,4v); +z47=(7v,4v); +z48=(8v,4v); +z495=(9.5v,4v); +z413=(13.5v,4v); +z416=(15v,4v); + +z50=(0v,5v); +z515=(1.5v,5v); +z53=(3v,5v); +z54=(4v,5v); +z555=(5.5v,5v); +z57=(7v,5v); +z58=(8v,5v); +z595=(9.5v,5v); +z511=(11v,5v); +z512=(12v,5v); +z513=(13.5v,5v); +z516=(15v,5v); + +z60=(0v,6v); +z61=(1v,6v); +z62=(2v,6v); +z63=(3v,6v); +z64=(4v,6v); +z65=(5v,6v); +z66=(6v,6v); +z67=(7v,6v); +z68=(8v,6v); +z69=(9v,6v); +z610=(10v,6v); +z611=(11v,6v); +z612=(12v,6v); +z613=(13v,6v); +z614=(14v,6v); +z615=(15v,6v); + +z71=(1v,7v); +z72=(2v,7v); +z75=(5v,7v); +z76=(6v,7v); +z79=(9v,7v); +z710=(10v,7v); +z713=(13v,7v); +z714=(14v,7v); + + + + +pickup pencircle scaled 0.4pt; +draw(z10--z115--z215--z20--cycle); +draw(z30--z37--z47--z40--cycle); +draw(z38--z315--z416--z48--cycle); +draw(z50--z53--z63--z60--cycle); +draw(z54--z57--z67--z64--cycle); +draw(z58--z511--z611--z68--cycle); +draw(z512--z516--z615--z612--cycle); +drawarrow(z71--z61); +drawarrow(z72--z62); +drawarrow(z75--z65); +drawarrow(z76--z66); +drawarrow(z79--z69); +drawarrow(z710--z610); +drawarrow(z713--z613); +drawarrow(z714--z614); +drawarrow(z515--z415); +drawarrow(z555--z455); +drawarrow(z595--z495); +drawarrow(z513--z413); +drawarrow(z335--z235); +drawarrow(z311--z211); +drawarrow(z175--z1075); + +endfig; + + +beginfig(5); +v:=u*5mm; + +z075=(7.5v,8v); + +z10=(0v,7v); +z175=(7.5v,7v); +z115=(15v,7v); + +z20=(0v,6v); +z235=(3.5v,6v); +z211=(11.5v,6v); +z215=(15v,6v); + +z30=(0v,5v); +z335=(3.5v,5v); +z37=(7v,5v); +z38=(8v,5v); +z311=(11.5v,5v); +z315=(15v,5v); + +z40=(0v,4v); +z415=(1.5v,4v); +z455=(5.5v,4v); +z47=(7v,4v); +z48=(8v,4v); +z495=(9.5v,4v); +z413=(13.5v,4v); +z416=(15v,4v); + +z50=(0v,3v); +z515=(1.5v,3v); +z53=(3v,3v); +z54=(4v,3v); +z555=(5.5v,3v); +z57=(7v,3v); +z58=(8v,3v); +z595=(9.5v,3v); +z511=(11v,3v); +z512=(12v,3v); +z513=(13.5v,3v); +z516=(15v,3v); + +z60=(0v,2v); +z61=(1v,2v); +z62=(2v,2v); +z63=(3v,2v); +z64=(4v,2v); +z65=(5v,2v); +z66=(6v,2v); +z67=(7v,2v); +z68=(8v,2v); +z69=(9v,2v); +z610=(10v,2v); +z611=(11v,2v); +z612=(12v,2v); +z613=(13v,2v); +z614=(14v,2v); +z615=(15v,2v); + +% ve skutecnosti dle znaceni +% by melo byt z6* ale uz +% obsazeno +z815=(1.5v,2v); +z855=(5.5v,2v); +z895=(9.5v,2v); +z813=(13.5v,2v); + +z715=(1.5v,1v); +z755=(5.5v,1v); +z795=(9.5v,1v); +z713=(13.5v,1v); + +z9=(4v,0v); + +pickup pencircle scaled 0.4pt; +draw(z10--z115--z215--z20--cycle); +draw(z30--z37--z47--z40--cycle); +draw(z38--z315--z416--z48--cycle); +draw(z50--z53--z63--z60--cycle); +draw(z54--z57--z67--z64--cycle); +draw(z58--z511--z611--z68--cycle); +draw(z512--z516--z615--z612--cycle); +drawarrow(z075--z175); +drawarrow(z415--z515); +drawarrow(z455--z555); +drawarrow(z495--z595); +drawarrow(z413--z513); +drawarrow(z235--z335); +drawarrow(z211--z311); +drawarrow(z815--z715); +drawarrow(z855--z755); +drawarrow(z895--z795); +drawarrow(z813--z713); + +label.llft(btex $n$ etex,z075); +label.bot(btex $S_n$ etex,z175); +label.bot(btex $S_{n\over 2}$ etex,z335); +label.bot(btex $S_{n\over 2}$ etex,z311); +label.bot(btex $S_{n\over 4}$ etex,z515); +label.bot(btex $S_{n\over 4}$ etex,z555); +label.bot(btex $S_{n\over 4}$ etex,z595); +label.bot(btex $S_{n\over 4}$ etex,z513); +label.rt(btex Bitonick\'a t\v r\'\i di\v cka $B_{n}$ etex,z9); + +endfig; + + + +beginfig(6); +v:=u*5mm; + +z1075=(7.5v,0v); + +z10=(0v,1v); +z175=(7.5v,1v); +z115=(15v,1v); + +z20=(0v,2v); +z235=(3.5v,2v); +z211=(11.5v,2v); +z215=(15v,2v); + +z30=(0v,3v); +z335=(3.5v,3v); +z37=(7v,3v); +z38=(8v,3v); +z311=(11.5v,3v); +z315=(15v,3v); + +z40=(0v,4v); +z415=(1.5v,4v); +z455=(5.5v,4v); +z47=(7v,4v); +z48=(8v,4v); +z495=(9.5v,4v); +z413=(13.5v,4v); +z416=(15v,4v); + +z50=(0v,5v); +z515=(1.5v,5v); +z53=(3v,5v); +z54=(4v,5v); +z555=(5.5v,5v); +z57=(7v,5v); +z58=(8v,5v); +z595=(9.5v,5v); +z511=(11v,5v); +z512=(12v,5v); +z513=(13.5v,5v); +z516=(15v,5v); + +z60=(0v,6v); +z61=(1v,6v); +z62=(2v,6v); +z63=(3v,6v); +z64=(4v,6v); +z65=(5v,6v); +z66=(6v,6v); +z67=(7v,6v); +z68=(8v,6v); +z69=(9v,6v); +z610=(10v,6v); +z611=(11v,6v); +z612=(12v,6v); +z613=(13v,6v); +z614=(14v,6v); +z615=(15v,6v); + +z71=(1v,7v); +z72=(2v,7v); +z75=(5v,7v); +z76=(6v,7v); +z79=(9v,7v); +z710=(10v,7v); +z713=(13v,7v); +z714=(14v,7v); + + + + +pickup pencircle scaled 0.4pt; +draw(z10--z115--z215--z20--cycle); +draw(z30--z37--z47--z40--cycle); +draw(z38--z315--z416--z48--cycle); +draw(z50--z53--z63--z60--cycle); +draw(z54--z57--z67--z64--cycle); +draw(z58--z511--z611--z68--cycle); +draw(z512--z516--z615--z612--cycle); +drawarrow(z71--z61); +drawarrow(z72--z62); +drawarrow(z75--z65); +drawarrow(z76--z66); +drawarrow(z79--z69); +drawarrow(z710--z610); +drawarrow(z713--z613); +drawarrow(z714--z614); +drawarrow(z515--z415); +drawarrow(z555--z455); +drawarrow(z595--z495); +drawarrow(z513--z413); +drawarrow(z335--z235); +drawarrow(z311--z211); +drawarrow(z175--z1075); + +label.top(btex $M_8$ etex,z175); +label.top(btex $M_4$ etex,z335); +label.top(btex $M_4$ etex,z311); +label.top(btex $M_2$ etex,z515); +label.top(btex $M_2$ etex,z555); +label.top(btex $M_2$ etex,z595); +label.top(btex $M_2$ etex,z513); + +endfig; + + +beginfig(7); +v:=u*7mm; + +z12=(1v,2v); +z13=(1v,3v); +z16=(1v,6v); +z356=(3.5v,6v); +z40=(5v,1v); +z42=(5v,2v); +z47=(5v,6.5v); +z72=(9v,2v); +z74=(9v,4v); +z76=(9v,6v); + +z100=(0.5v,0.5v); +z101=(1.5v,0.5v); + +z0=whatever[z13,z356]; +z1=whatever[z356,z74]; +z1=z0+4v*right; +z2=whatever[z12,z72]; +z2=z0+whatever*down; +z3=whatever[z12,z72]; +z3=z1+whatever*down; +z4=z356+4v*right; +z5=whatever[z72,z76]; +z5=whatever[z4,z1+4v*right]; +z6=whatever[z40,z47]; +z6=z74+4v*left; + + +pickup pencircle scaled 0.4pt; +draw(z16--z12--z72--z76); +draw(z13--z356--z74); +draw(z40--z47) dashed evenly; +draw(z1--z4) dashed withdots scaled 0.7; +draw(z4--z5) dashed withdots scaled 0.7; +draw(z0--z6) dashed withdots scaled 0.7; +draw(z0--z2) dashed evenly; +draw(z1--z3) dashed evenly; + +draw(z100--z101) dashed withdots scaled 0.7; + +pickup pencircle scaled 3pt; +drawdot(z0); +drawdot(z1); + +label.bot(btex \strut 0 etex,z12); +label.bot(btex $k$ etex,z2); +label.llft(btex \strut ${n\over 2} - 1$ etex,z42); +label.bot(btex \strut $k+{n\over 2}$ etex,z3); +label.bot(btex \strut $n-1$ etex,z72); +label.rt(btex posloupnost prohozen\'a separ\'atorem etex,z101); + +endfig; + + + + + + + +end; diff --git a/old/5-addsort/sortnet.mpx b/old/5-addsort/sortnet.mpx new file mode 100644 index 0000000..9814d6d --- /dev/null +++ b/old/5-addsort/sortnet.mpx @@ -0,0 +1,548 @@ +% Written by DVItoMP, Version 0.64/color (Web2C 7.5.4) +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("a",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,-3.4869)--(4.9813,-3.4869)-- + (4.9813,8.4682)--(0,8.4682)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("b",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,-3.4869)--(5.5348,-3.4869)-- + (5.5348,8.4682)--(0,8.4682)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("min",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(16.6045,0.0000)-- + (16.6045,6.6536)--(0,6.6536)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("max",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(18.5416,0.0000)-- + (18.5416,4.2895)--(0,4.2895)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x1",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x2",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x3",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x4",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x5",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x1",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x2",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x3",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x4",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("x5",_n0,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(10.2394,0.0000)-- + (10.2394,6.4204)--(0,6.4204)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("x",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("0",_n2,1.00000,5.6939,-1.4944,); +setbounds _p to (0,-1.4944)--(10.1633,-1.4944)-- + (10.1633,4.2895)--(0,4.2895)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("x",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("1",_n2,1.00000,5.6939,-1.4944,); +setbounds _p to (0,-1.4944)--(10.1633,-1.4944)-- + (10.1633,4.2895)--(0,4.2895)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("x",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("2",_n2,1.00000,5.6939,-1.4944,); +setbounds _p to (0,-1.4944)--(10.1633,-1.4944)-- + (10.1633,4.2895)--(0,4.2895)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s(":",_n1,1.00000,0.0000,0.0000,); +_s(":",_n1,1.00000,4.4278,0.0000,); +_s(":",_n1,1.00000,8.8556,0.0000,); +setbounds _p to (0,0.0000)--(13.2834,0.0000)-- + (13.2834,1.0516)--(0,1.0516)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("x",_n1,1.00000,0.0000,0.0000,); +_n3="cmmi7"; +_s("n",_n3,1.00000,5.6939,-1.4944,); +_n4="cmsy7"; +_s(char0,_n4,1.00000,10.6188,-1.4944,); +_n2="cmr7"; +_s("2",_n2,1.00000,16.8454,-1.4944,); +setbounds _p to (0,-2.3246)--(21.3148,-2.3246)-- + (21.3148,4.2895)--(0,4.2895)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("x",_n1,1.00000,0.0000,0.0000,); +_n3="cmmi7"; +_s("n",_n3,1.00000,5.6939,-1.4944,); +_n4="cmsy7"; +_s(char0,_n4,1.00000,10.6188,-1.4944,); +_n2="cmr7"; +_s("1",_n2,1.00000,16.8454,-1.4944,); +setbounds _p to (0,-2.3246)--(21.3148,-2.3246)-- + (21.3148,4.2895)--(0,4.2895)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("n",_n1,1.00000,0.0000,0.0000,); +setbounds _p to (0,0.0000)--(5.9799,0.0000)-- + (5.9799,4.2895)--(0,4.2895)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("S",_n1,1.00000,0.0000,0.0000,); +_n3="cmmi7"; +_s("n",_n3,1.00000,6.1090,-1.4944,); +setbounds _p to (0,-1.4944)--(11.5320,-1.4944)-- + (11.5320,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("S",_n1,1.00000,0.0000,0.0000,); +_n5="cmmi5"; +_s("n",_n5,1.00000,7.3045,1.1829,); +interim linecap:=0; +vardef _r(expr _a,_w)(text _t) = + addto _p doublepath _a withpen pencircle scaled _w _t enddef; +_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); +_n6="cmr5"; +_s("2",_n6,1.00000,7.8033,-3.8949,); +setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- + (13.3857,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("S",_n1,1.00000,0.0000,0.0000,); +_n5="cmmi5"; +_s("n",_n5,1.00000,7.3045,1.1829,); +interim linecap:=0; +vardef _r(expr _a,_w)(text _t) = + addto _p doublepath _a withpen pencircle scaled _w _t enddef; +_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); +_n6="cmr5"; +_s("2",_n6,1.00000,7.8033,-3.8949,); +setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- + (13.3857,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("S",_n1,1.00000,0.0000,0.0000,); +_n5="cmmi5"; +_s("n",_n5,1.00000,7.3045,1.1829,); +interim linecap:=0; +vardef _r(expr _a,_w)(text _t) = + addto _p doublepath _a withpen pencircle scaled _w _t enddef; +_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); +_n6="cmr5"; +_s("4",_n6,1.00000,7.8033,-3.8949,); +setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- + (13.3857,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("S",_n1,1.00000,0.0000,0.0000,); +_n5="cmmi5"; +_s("n",_n5,1.00000,7.3045,1.1829,); +interim linecap:=0; +vardef _r(expr _a,_w)(text _t) = + addto _p doublepath _a withpen pencircle scaled _w _t enddef; +_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); +_n6="cmr5"; +_s("4",_n6,1.00000,7.8033,-3.8949,); +setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- + (13.3857,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("S",_n1,1.00000,0.0000,0.0000,); +_n5="cmmi5"; +_s("n",_n5,1.00000,7.3045,1.1829,); +interim linecap:=0; +vardef _r(expr _a,_w)(text _t) = + addto _p doublepath _a withpen pencircle scaled _w _t enddef; +_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); +_n6="cmr5"; +_s("4",_n6,1.00000,7.8033,-3.8949,); +setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- + (13.3857,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("S",_n1,1.00000,0.0000,0.0000,); +_n5="cmmi5"; +_s("n",_n5,1.00000,7.3045,1.1829,); +interim linecap:=0; +vardef _r(expr _a,_w)(text _t) = + addto _p doublepath _a withpen pencircle scaled _w _t enddef; +_r((7.3045,0.2491)..(11.6921,0.2491), 0.3985,); +_n6="cmr5"; +_s("4",_n6,1.00000,7.8033,-3.8949,); +setbounds _p to (0,-3.8949)--(13.3857,-3.8949)-- + (13.3857,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("Bitonic",_n0,1.00000,0.0000,0.0000,); +_s("k"&char19,_n0,1.00000,31.1333,0.0000,); +_s("a",_n0,1.00000,36.3914,0.0000,); +_s("t",_n0,1.00000,44.6936,0.0000,); +_s(char20,_n0,1.00000,48.0283,0.0000,); +_s("r",_n0,1.00000,48.5680,0.0000,); +_s(char19,_n0,1.00000,51.3631,0.0000,); +_s(char16&"di",_n0,1.00000,52.4700,0.0000,); +_s(char20,_n0,1.00000,63.2629,0.0000,); +_s("ck",_n0,1.00000,63.5397,0.0000,); +_s("a",_n0,1.00000,72.6721,0.0000,); +_n1="cmmi10"; +_s("B",_n1,1.00000,80.9743,0.0000,); +_n3="cmmi7"; +_s("n",_n3,1.00000,88.5311,-1.4944,); +setbounds _p to (0,-1.4944)--(93.9541,-1.4944)-- + (93.9541,6.9185)--(0,6.9185)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("M",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("8",_n2,1.00000,9.6652,-1.4944,); +setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- + (14.1345,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("M",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("4",_n2,1.00000,9.6652,-1.4944,); +setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- + (14.1345,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("M",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("4",_n2,1.00000,9.6652,-1.4944,); +setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- + (14.1345,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("M",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("2",_n2,1.00000,9.6652,-1.4944,); +setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- + (14.1345,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("M",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("2",_n2,1.00000,9.6652,-1.4944,); +setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- + (14.1345,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("M",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("2",_n2,1.00000,9.6652,-1.4944,); +setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- + (14.1345,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n1="cmmi10"; +_s("M",_n1,1.00000,0.0000,0.0000,); +_n2="cmr7"; +_s("2",_n2,1.00000,9.6652,-1.4944,); +setbounds _p to (0,-1.4944)--(14.1345,-1.4944)-- + (14.1345,6.8078)--(0,6.8078)--cycle; +_p endgroup +mpxbreak +begingroup save _p,_r,_s,_n; picture _p; _p=nullpicture; +string _n[]; +vardef _s(expr _t,_f,_m,_x,_y)(text _c)= + addto _p also _t infont _f scaled _m shifted (_x,_y) _c; enddef; +_n0="cmr10"; +_s("0",_n0,1.00000,0.0000,0.0000,); +setbounds _p to 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b/old/5-addsort/tabulka_skladani_bloku.svg @@ -0,0 +1,237 @@ + + + + + + + + + + + image/svg+xml + + + + + + + 0 1 <0 0 0 01 1 1 1< 0 1 < + + { + { + A + B + + + + + + A + B + + C + + + diff --git a/old/6-kmp/6-kmp.tex b/old/6-kmp/6-kmp.tex new file mode 100644 index 0000000..041c06a --- /dev/null +++ b/old/6-kmp/6-kmp.tex @@ -0,0 +1,278 @@ +\input lecnotes.tex + +\prednaska{6}{Vyhledávání v~textu}{(zapsal: Petr Jankovský)} + +Nyní se budeme vìnovat následujícímu problému: v~textu délky $S$ (senì) budeme chtít najít v¹echny výskyty hledaného slova délky $J$ (jehly). Nejprve se podívejme na~jeden primitivní algoritmus, který nefunguje. Je ale zajímavé rozmyslet si, proè. + +\h{Hloupý algoritmus} +Zaèneme prvním písmenkem hledaného slova a~budeme postupnì procházet text, a¾ najdeme první výskyt poèáteèního písmenka. Poté budeme testovat, zda souhlasí i~písmenka dal¹í. Pokud nastane neshoda, v~hledaném slovì se vrátíme na~zaèátek a~v~textu pokraèujeme znakem, ve~kterém neshoda nastala. Podívejme se na~pøíklad. + +\s{Pøíklad:} Budeme hledat slovo |jehla| v~textu |jevkupcejejehla|. Vezmeme si tedy první písmenko |j| v~hledaném slovì a~zjistíme, ¾e v~textu se nachází hned na~zaèátku. Vezmeme tedy dal¹í písmenko |e|, které se vyskytuje jako druhé i~v~textu. Pøi tøetím písmenku ale narazíme na~neshodu. V~tuto chvíli tedy zresetujeme a~opìt hledáme výskyt písmenka |j|, tentokrát v¹ak a¾ od~tøetího písmene v~textu. Takto postupujeme postupnì dál, a¾ narazíme na~dal¹í |je|, které ov¹em není následováno písmenem~|h|, tudí¾ opìt zresetujeme a~nakonec najdeme shodu s~celým hledaným øetìzcem. V~tomto pøípadì tedy algoritmus na¹el hledané slovo. + +Tento algoritmus v¹ak zjevnì mù¾e hanebnì selhat. Mù¾e se stát, ¾e zaèneme porovnávat, a¾ v~jednu chvíli narazíme na~neshodu. Celý tento kus tedy pøeskoèíme. Pøi tom se ale v~tomto kusu textu mohl vyskytovat nìjaký pøekrývající se výskyt hledané \uv{jehly}. Hledejme napøíklad øetìzec |kokos| v~textu |clanekokokosu|. Algoritmus tedy zaène porovnávat. Ve~chvíli kdy najde prefix |koko| a~na~vstupu dostane~|k|, dochází k~neshodì. Proto algoritmus zresetuje a~pokraèuje v~hledání od~tohoto znaku. Najde sice je¹tì výskyt |ko|, ov¹em s~dal¹ím písmenkem |s| ji¾ dochází k~neshodì a~algotimus sel¾e. Nesprávnì se toti¾ \uv{upnul} na~první nalezené |koko| a~s~dal¹ím~|k| pak \uv{zahodil} i~správný zaèátek. + +Máme tedy algoritmus, který i~kdy¾ je ¹patnì, tak funguje urèitì kdykoli se první písmenko hledaného slova v~tomto slovì u¾ nikde jinde nevyskytuje -- co¾ |jehla| splòovala, ale |kokos| u¾ ne. + +{\I Hloupý algoritmus} se na~ka¾dé písmenko textu podívá jednou, tudí¾ èasová slo¾itost bude lineární s~délkou textu ve~kterém hledáme -- tedy $\O(S)$. + +\h{Pomalý algoritmus} +Zkusíme algoritmus vylep¹it tak, aby fungoval správnì: pokud nastane nìjaká neshoda, vrátíme se zpátky tìsnì za~zaèátek toho, kdy se nám to zaèalo shodovat. To je ov¹em vlastnì skoro toté¾, jako brát postupnì v¹echny mo¾né zaèátky v~\uv{senì} a~pro ka¾dý z~nìj ovìøit, jestli se tam \uv{jehla} nachází èi nikoliv. + +Tento algoritmus evidentnì funguje. Bì¾í v¹ak v~èase: $S$ mo¾ných zaèátkù, krát èas potøebný na~jedno porovnání (zda se na~dané pozici nenachází \uv{jehla}), co¾ nám mù¾e trvat a¾ $J$. Proto je èasová slo¾itost $\O(SJ)$. V praxi bude algoritmus èasto rychlej¹í, proto¾e typicky velmi brzo zjistíme, ¾e se øetìzce neshodují, ale je mo¾né vymyslet vstup, kde bude potøeba porovnání opravdu tolik. + +Nyní se pokusme najít takový algoritmus, který by byl tak rychlý, jako {\I Hloupý algoritmus}, ale chytrý, jako ten {\I Pomalý}. + +\h{Chytrý algoritmus} +Ne¾ vlastní algoritmus vybudujeme, zkusíme se cestou nauèit pøemý¹let o~øetìzcích obèas trochu pøekrouceným zpùsobem. Podívejme se na~je¹tì jeden pøíklad. + +\s{Pøíklad:} Vezmìme si napøíklad staré italské pøízvisko |barbarossa|, které znamená Rudovous. Pøedstavme si, ¾e takovéto slovo hledáme v~nìjakém textu, který zaèíná |barbar|. Víme, ¾e a¾ sem se nám hledaný øetìzec shodoval. Øeknìme, ¾e dal¹í písmenko textu se shodovat pøestane -- místo |o| naèteme napøíklad opìt |b|. {\I Hloupý algoritmus} by velil vrátit se k~|a| a~od~nìj hledat dál. Uvìdomme si ale, ¾e kdy¾ se vracíme z~|barbar| do~|arbar| (tedy øetìzce, který ji¾ známe), mù¾eme si pøedpoèítat, jak dopadne hledání, kdy¾ ho pustíme na~nìj. V~pøedpoèítaném bychom tedy chtìli ukládat, ¾e kdy¾ máme øetìzec |arbar|, tak |ar| a~|r| nám do~hledaného nepasuje a~a¾~|bar| se bude shodovat. Tedy místo toho, abychom spustili nové hledání od~|a|, mù¾eme ho spustit a¾~od~|b|. Co víc, my dokonce víme, jak dopadne to -- pokud toti¾ nastane neshoda po~pøeètení |barbar|, je to stejné, jako kdybychom pøeèetli pouze |bar|, na~které se (pùvodne neshodující se) |b| u¾ navázat dá. Kdyby se nedalo navázat ani tam, tak bychom opìt zkracovali... Nejen, ¾e tedy víme, kam se máme vrátit, ale víme dokonce i~to, co tam najdeme. + +My¹lenka, ke které míøíme, je pøedpoèítat si nìjakou tabulku, která nám bude øíkat, jak se máme pøi hledání vracet a~jak to dopadne, a~pak u¾ jenom prohlédávat s~pou¾itím této tabulky. + +Aby se nám o~tìchto algoritmech lépe mluvilo a~pøedev¹ím psalo, pojïme si povìdìt nìkolik definic. + +\s{Definice:} +\itemize\ibull +\:{\I Abeceda $\Sigma$} je koneèná mno¾ina znakù\foot{Mù¾eme pøi tom jít a¾~do~extrémù. Pøíkladem extrémních abeced je binární abeceda slo¾ená pouze z~nul a~jednièek. Pøíklad z~druhého konce (který rádi dìlají lingvisti) je abeceda, která má jako abecedu v¹echna èeská slova. V¹echny èeské vìty, pak nejsou nic jiného, ne¾ slova nad touto abecedou. Pou¾itá abeceda tedy mù¾e být i~relativnì obrovská. Dal¹ím takovým pøíkladem mù¾e být Unicode. Pro na¹e potøeby ale zatím budeme pøedpokládat, ¾e abeceda je nejen konstantnì velká, ale i~rozumnì malá. Budeme si moci tedy dovolit napøíklad indexovat pole znakem abecedy (kdybychom nemohli, tak bychom místo pole pou¾ili napøíklad hashovací tabulku, èi nìco podobného\dots).}, ze~kterých tvoøíme text, øetìzce, slova. + + +\:{\I $\Sigma^*$} je mno¾ina v¹ech slov nad abecedou $\Sigma$. Èili mno¾ina v¹ech neprázdných koneèných posloupností znakù ze $\Sigma$. +\endlist +\s{Znaèení:} +Aby se nám nepletlo znaèení, budeme rozli¹ovat promìnné pro slova, promìnné pro písmenka a~promìnné pro èísla. + +\itemize\ibull +\:{\I Slova} budeme znaèit malými písmenky øecké abecedy $\alpha$, $\beta$, ... . +\:$\iota$ bude oznaèovat \uv{jehlu} +\:$\sigma$ bude oznaèovat \uv{seno} +\:{\I Znaky} oznaèíme malými písmeny latinky $a$, $b$, \dots . +\:{\I Èísla} budeme znaèit velkými písmeny $A$, $B$, \dots . +\:{\I Délka slova} $\vert \alpha \vert$ pro $\alpha \in \Sigma^*$ je poèet jeho znakù. +\:{\I Prázdné slovo} znaèíme písmenem $\varepsilon$, $\vert \varepsilon \vert = 0$. +\:{\I Zøetìzení} $\alpha\beta$ vznikne zapsáním slov $\alpha$ a~$\beta$ za sebe. Platí $\vert \alpha\beta \vert=\vert \alpha \vert+\vert \beta \vert$, $\alpha\varepsilon=\varepsilon\alpha=\alpha$. +\:$\alpha[k]$ je $k$-tý znak slova $\alpha$, indexujeme od~$0$. +\:$\alpha[k:l]$ je podslovo zaèínající $k$-tým znakem a~$l$-tý znak je první, který v~nìm není. Jedná se tedy o~podslovo skládající se z~$\alpha[k]$,$\alpha[k+1]$,\dots,$\alpha[l-1]$. Platí tedy: $\alpha[k:k]=\varepsilon$, $\alpha[k:k+1]=\alpha[k]$. Jednu (èi obì) meze mù¾eme i~vynechat, tento zápis pak bude znamenat buï \uv{od zaèátku slova a¾ nìkam}, nebo \uv{odnìkud a¾ do~konce}: +%TODO - zaøadit pod pøedchozí bod +\:$\alpha[:k]$ je {\I prefix} obsahující prvních $k$ znakù slova $\alpha$ ($\alpha[0]$,\dots,$\alpha[k-1]$). +\:$\alpha[k:]$ je {\I suffix} obsahující znaky slova $\alpha$ poèínaje $k$-tým znakem a¾ do~konce. +\:$\alpha[:] = \alpha$ +\endlist + + +V¹imnìme si, ¾e prázdné slovo je prefixem, suffixem i~podslovem jakéhokoliv slova vèetnì sebe sama. +Ka¾dé slovo je také prefixem, suffixem i~podslovem sebe sama. To se ne v¾dy hodí. Nìkdy budeme chtít øíct, ¾e nìjaké slovo je {\I vlastním} prefixem nebo suffixem. To bude znamenat, ¾e to nebude celé slovo. + +\> $\alpha$ je {\I vlastní prefix} slova $\beta \equiv \alpha$ je prefix $\beta~\&~\alpha \neq \beta$. + +\h{Vyhledávací automat (Knuth, Morris, Pratt)} +{\I Vyhledávací automat} bude graf, jeho¾ vrcholùm budeme øíkat {\I stavy}. Jejich jména budou prefixy hledaného slova a~hrany budou odpovídat tomu, jak jeden prefix mù¾eme získat z~pøedchozího prefixu pøidáním jednoho písmene. Poèáteèní stav je prázdné slovo $\varepsilon$ a~koncový je celá $\iota$. Dopøedné hrany grafu budou popisovat pøechod mezi stavy ve~smyslu zvìt¹ení délky jména stavu (dopøedná funkce $h(\alpha)$, urèující znak na~dopøedné hranì z~$\alpha$). Zpìtné hrany grafu budou popisovat pøechod (zpìtná funkce $z(\alpha)$) mezi stavem $\alpha$ a~nejdel¹ím vlastním suffixem $\alpha$, který je prefixem $\iota$, kdy¾ nastane neshoda. + +\figure{barb.eps}{Vyhledávací automat.}{4.1in} + +\s{Hledej($\sigma$):} +\algo +\:$\alpha \leftarrow \varepsilon$. +\:Pro $x\in\sigma$ postupnì: +\:$\indent$Dokud $h(\alpha) \neq x~\&~\alpha \neq \varepsilon : \alpha \leftarrow z(\alpha)$. +\:$\indent$Pokud $h(\alpha) = x: \alpha \leftarrow \alpha x$. +\:$\indent$Pokud $\alpha = \iota$, ohlásíme výskyt. +\endalgo + +\>Vstupem je $\iota$, hledané slovo (jehla) délky $J=\vert \iota \vert$ a~$\sigma$, text (seno) délky $S=\vert \sigma \vert$. +\>Výstupem jsou v¹echny výskyty hledaného slova $\iota$ v~textu $\sigma$, tedy mno¾ina $\left\{ k \mid \sigma[k:k+J]=\iota \right\}$ + +Pojïme nyní dokázat, ¾e tento algoritmus správnì ohlásí v¹echny výskyty. + +\s{Definice}: $\alpha(\tau) := $ stav automatu po~pøeètení $\tau$ + +\s{Invariant:} Pokud algoritmus pøeète nìjaký vstup, nachází se ve~stavu, který je nejdel¹ím suffixem pøeèteného vstupu, který je nìjakým stavem. +$\alpha(\tau) =$ nejdel¹í stav (nejdel¹í prefix jehly), který je suffixem $\tau$ (pøeèteného vstupu). + +Pojïme si rozmyslet, ¾e z~tohoto invariantu ihned plyne, ¾e algoritmus najde to, co má. Kdykoli toti¾ ohlásí nìjaký výskyt, tak tam tento výskyt opravdu je. Kdykoli pak má nìjaký výskyt ohlásit, tak se v~této situaci jako suffix toho právì pøeèteného textu vyskytuje hledané slovo, pøièem¾ hledané slovo je urèitì stav a~zároveò nejdel¹í ze v¹ech existujících stavù. Tak¾e invariant opravdu øíká, ¾e jsme právì v~koncovém stavu a~algoritmus nám tedy ohlásí výskyt. + +\proof {\I (invariantu)} +Indukcí podle kroku algoritmu. Na~zaèátku pro prázdný naètený vstup invariant triviálnì platí, tedy prázdný suffix $\tau$ je prefixem $\iota$. V~kroku $n$ máme naètený vstup $\tau$ a~k~nìmu pøipojíme znak $x$. Invariant nám øíká, ¾e nejdel¹í stav, který je suffixem, je nejdel¹í suffix, který je stavem. Nyní se ptáme, jaký je nejdel¹í stav, který se dá \uv{napasovat} na~konec øetìzce $\tau x$. Kdykoli v¹ak takovýto suffix máme, tak z~nìj mù¾eme $x$ na~konci odebrat, èím¾ dostaneme suffix slova $\tau$. + +\>Tedy: pokud $\beta$ je neprázdným suffixem slova $\tau x$, pak $\beta = \gamma x$, kde $\gamma$ je suffix $\tau$. + +Suffix, který máme sestrojit, tedy vznikne z~nìjakého suffixu slova $\tau$ pøipsáním~$x$. Chceme najít nejdel¹í suffix slova $\tau x$, který je stavem, tak¾e chceme najít i~nejdel¹í suffix pùvodního slova $\tau$, za který se dá pøidat $x$ tak, aby vy¹lo jméno stavu. Staèí tedy u¾ jen \uv{probírat} suffixy slova $\tau$ od~nejdel¹ího po~nejkrat¹í, zkou¹et k~nim pøidávat $x$ a~a¾ to pùjde, tak jsme na¹li nejdel¹í suffix $\tau x$. Pøesnì toto ov¹em algoritmus dìlá, nebo» zpìtná funkce mu v¾dy øekne nejbli¾¹í krat¹í suffix, který je stavem. Pokud pak nemù¾eme $x$ pøidat ani do~$\varepsilon$, pak je øe¹ením prázdný suffix. Algoritmus tedy funguje. \qed + +Nyní pojïmì zkoumat to, jak je ve~skuteènosti ná¹ algoritmus rychlý. K tomu bychom si ale nejdøív mìli øíct, jak pøesnì budeme automat reprezentovat. V~algoritmu vystupují nìjaká porovnávání stavù, pøièem¾ není úplnì jasné, jak zaøídit, aby v¹e trvalo konstantnì dlouho. Vyjde nám to ale docela snadno. K reprezentaci automatu nám toti¾ budou staèit pouze dvì pole. + +\s{Reprezentace automatu:} +Oèíslujeme si stavy délkami pøíslu¹ných prefixù, tedy èísly $0 \dots J$. Poté je¹tì potøebujeme nìjakým zpùsobem zakódovat dopøedné a~zpìtné hrany. Vzhledem k~tomu, ¾e z~ka¾dého vrcholu vede v¾dy nejvý¹e jedna dopøedná a~nejvý¹e jedna zpìtná, tak nám evidentnì staèí pamatovat si pro ka¾dý typ hran pouze jedno èíslo na~vrchol. Budeme mít tedy nìjaké pole dopøedných hran, které nám pro ka¾dý stav øekne, jakým písmenkem je nadepsaná dopøedná hrana ze stavu $I$ do~$I+1$. To jsou ale pøesnì písmenka jehly, tak¾e si staèí pamatovat jehlu samotnou. Èili z~$I$ do~$I+1$ vede hrana nadepsaná $\iota [I]$. Pro zpìtné hrany pak budeme potøebovat pole $Z$, které nám pro stav $I$ øekne èíslo stavu, do~kterého vede zpìtná hrana. Tedy $Z[I]$ je cíl zpìtné hrany ze stavu $I$. +S~touto reprezentací ji¾ doká¾eme na¹i hledací proceduru pøímoèaøe pøepsat tak, aby sahala pouze do~tìchto dvou polí: +\algo +\:$I \leftarrow 0$. +\:Pro znaky $x$ z~textu: +\:$\indent$Dokud $\iota[I] \neq x~\&~I \neq 0: I \leftarrow Z[I]$. +\:$\indent$Pokud $\iota[I] = x$, pak $I \leftarrow I + 1$. +\:$\indent$Pokud $I = J$, ohlásíme výskyt. +\endalgo + +Zatím se v~algoritmu je¹tì skrývá drobná chyba -- toti¾ algoritmus se obèas zeptá na~dopøednou hranu z~posledního stavu. Pokud jsme právì ohlásili výskyt (jsme tedy v~posledním stavu) a~pøijde nìjaký dal¹í znak, algoritmus se ptá, zda je roven tomu, co je na~dopøedné hranì z~posledního stavu. Ta ale ov¹em neexistuje. Jednodu¹e to ale napravíme tak, ¾e si pøidáme fiktivní hranu, na~které se vyskytuje nìjaké \uv{nepísmenko} -- nìco, co se nerovná ¾ádnému jinému písmenku. Zajistíme tak, ¾e se po~této hranì nikdy nevydáme. Dodefinujeme tedy $\iota[J]$ odli¹nì od~v¹ech znakù.\foot{V jazyce C se toto dodefinování provede vlastnì zadarmo, nebo» ka¾dý øetìzec je v~nìm ukonèen znakem s~kódem nula, který se ve~vstupu nevyskytne\dots Algoritmus bude tedy fungovat i~bez tohoto dodefinování. V jiných jazycích je ale tøeba na~nìj nezapomenout!} + +\s{Lemma:} Funkce {\I Hledej} bì¾í v~èase $\O(S)$. + +\proof +Funkce {\I Hledej} chodí po~dopøedných a~zpìtných hranách. Dopøedných hran projdeme urèitì maximálnì tolik, kolik je délka sena. Pro ka¾dý znak pøeètený ze sena toti¾ jdeme nejvý¹e jednou po~dopøedné hranì. Se zpìtnými hranami se to má tak, ¾e na~jeden pøeètený znak z~textu se mù¾eme po~zpìtné hranì vracet maximálnì $J$-krát. Z~tohoto by nám v¹ak vy¹la slo¾itost $\O(JS)$, èím¾ bychom si nepomohli. Zachrání nás ale pøímoèarý potenciál. Uvìdomme si, ¾e chùze po~dopøedné hranì zvý¹í $I$ o~jedna a~chùze po~zpìtné hranì $I$ sní¾í alespoò o~jedna. Vzhledem k~tomu, ¾e $I$ není nikdy záporné a~na~zaèátku je nulové, zjistíme, ¾e krokù zpìt mù¾e být maximálnì tolik, kolik krokù dopøedu. Èasová slo¾itost hledání je tedy lineární vzhledem k~délce sena. \qed + +Nyní nám zbývá na~první pohled malièkost -- toti¾ zkonstruovat automat. Zkonstruovat dopøedné hrany zvládneme zjevnì snadno, jsou toti¾ explicitnì popsané hledaným slovem. Tì¾¹í u¾ to bude pro hrany zpìtné. Vyu¾ijeme k~tomu následující pozorování: + +\s{Pozorování:} +Pøedstavme si, ¾e automat u¾ máme hotový a~tím, ¾e budeme sledovat jeho chování, chceme zjistit, jak v~nìm vedou zpìtné hrany. +Vezmìme si nìjaký stav~$\beta$. To, kam z~nìj vede zpìtná hrana zjistíme tak, ¾e spustíme automat na~øetìzec $\beta$~bez prvního písmenka a~stav, ve~kterém se automat zastaví, je pøesnì ten, kam má vést i~zpìtná hrana z~$\beta$. Jinými slovy víme, ¾e $z(\beta) = \alpha (\beta[1:])$. +Proè takováto vìc funguje? V¹imìme si, ¾e definice $z$ a~to, co nám o~$\alpha$ øíká invariant, je témìø toto¾né -- $z(\beta)$ je nejdel¹í vlastní suffix $\beta$, který je stavem, $\alpha(\beta)$ je nejdel¹í suffix $\beta$, který je stavem. Jediná odli¹nost je v~tom, ¾e definice $z$ narozdíl od~definice $\alpha$ zakazuje nevlastní suffixy. Jak nyní vylouèit suffix $\beta$, který by byl roven $\beta$ samotné? Zkrátíme $\beta$ o~první znak. Tím pádem v¹echny suffixy $\beta$ bez prvního znaku jsou stejné jako v¹echny vlastní suffixy $\beta$. + +K èemu je toto pozorování dobré? Rozmysleme si, ¾e pomocí nìj u¾ doká¾eme zkonstruovat zpìtné hrany. Není to ale trochu divné, kdy¾ pøi simulování automatu na~øetìzec bez prvního znaku u¾ zpìtné hrany potøebujeme? Není. Za chvíli zjistíme, ¾e takto mù¾eme zji¹»ovat zpìtné hrany postupnì -- a~to tak, ¾e pou¾íváme v¾dy jenom ty, které jsme u¾ sestrojili. + +Takovémuhle pøístupu, kdy pøi konstruování chtìného u¾ pou¾íváme to, co chceme sestrojit, ale pouze ten kousek, který ji¾ máme hotový, se v~angliètinì øíká {\I bootstrapping}\foot{Z~tohoto slova vzniklo i~{\I bootování} poèítaèù, kdy operaèní systém v~podstatì zavádí sám sebe. Bootstrap znamená èesky ¹truple -- tedy oèko na~konci boty, které slou¾í k~usnadnìní nazouvání. A~jak souvisí ¹truple s~algoritmem? To se zase musíme vrátit k~pøíbìhùm o~baronu Prá¹ilovi, mezi nimi¾ je i~ten, ve~kterém baron Prá¹il vypráví o~tom, jak sám sebe vytáhl z~ba¾iny za ¹truple. Stejnì tak i~my budeme algoritmus konstruovat tím, ¾e se budeme sami vytahovat za ¹truple, tedy bootstrappovat.}. +V¹imnìme si, ¾e pøi výpoètu se vstupem $\beta$ projde automat jenom prvních $\vert \beta \vert$ stavù. Automat se evidentnì nemù¾e dostat dál, proto¾e na~ka¾dý krok dopøedu (doprava) spotøebuje písmenko $\beta$. Tak¾e krokù doprava je maximálnì tolik, kolik je $\vert \beta \vert$. Jinými slovy kdybychom ji¾ mìli zkonstruované zpìtné hrany pro prvních $\vert \beta \vert$ stavù (tedy $0 \dots \vert \beta \vert - 1$), tak pøi tomto výpoètu, který potøebujeme na~zkonstruování zpìtné hrany z~$\beta$, je¹tì tuto zpìtnou hranu nemù¾eme potøebovat. Vystaèíme si s~tìmi, které ji¾ máme zkonstruované. + +Nabízí se tedy zaèít zpìtnou hranou z~prvního znaku (která vede evidentnì do~$\varepsilon$), pak postupnì brát dal¹í stavy a~pro ka¾dý z~nich si spoèítat, kdy spustíme automat na~jméno stavu bez prvního znaku a~tím získáme dal¹í zpìtnou hranu. Toto funguje, ale je to kvadratické \dots. Máme toti¾ $J$ stavù a~pro ka¾dý z~nich nám automat bì¾í v~èase a¾ lineárním s~$J$. Jak z~toho ven? + +Z~prvního stavu povede zpìtná funkce do~$\varepsilon$. Pro dal¹í stavy chceme spoèítat zpìtnou funkci. Z~druhého stavu $\iota[0:2]$ tedy automat spustíme na~$\iota[1:2]$, dále pak na~$\iota[1:3]$, $\iota[1:4]$, atd. Ty øetìzce, pro které potøebujeme spo¹tìt automat, abychom dostali zpìtné hrany, jsou tedy ve~skuteènosti takové, ¾e ka¾dý dal¹í dostaneme roz¹íøením pøedchozího o~jeden znak. To jsou ale pøesnì ty stavy, kterými projde automat pøi zpracovávání øetìzce $\iota$ od~prvního znaku dál. Jedním prùchodem automatu nad jehlou bez prvního písmenka se tím pádem rovnou dozvíme v¹echny údaje, které potøebujeme. +Z~pøedchozího pozorování plyne, ¾e nikdy nebudeme potøebovat zpìtnou hranu, kterou jsme je¹tì nezkonstruovali a~jeliko¾ víme, ¾e jedno prohledání trvá lineárnì s~délkou toho, v~èem hledáme, tak toto celé pobì¾í v~lineárním èase. Dostaneme tedy následující algoritmus: + +\s{Konstrukce zpìtné funkce:} +\algo +\:$Z[0] \leftarrow ?$, $Z[1] \leftarrow 0$. +\:$I \leftarrow 0$. +\:Pro $k = 2 \dots J$: +\::$I \leftarrow \( I , \iota [k])$. +\::$Z[k] \leftarrow I$. +\endalgo + +Zaèínáme tím, ¾e nastavíme zpìtnou hranu z~prvních dvou stavù, pøièem¾ $z[0]$ je nedefinované, proto¾e tuto zpìtnou hranu nikdy nepou¾íváme. Dále postupnì simulujeme výpoèet automatu nad slovem bez prvního znaku a~po ka¾dém kroku se dozvíme novou zpìtnou hranu. {\I Krokem} automatu pak není nic jiného ne¾ vnitøek (3. a~4. bod) na¹í hledací procedury. To, kam jsme se dostali, pak zaznamenáme jako zpìtnou funkci z~$k$. +Èili pou¹tíme automat na~jehlu bez prvního písmenka, provedeme v¾dy jeden krok automatu (pøes dal¹í písmenko jehly) a~zapamatujeme si, jakou zpìtnou funkci jsme zrovna dostali. Díky pozorováním navíc víme, ¾e zpìtné hrany konstruujeme správnì, nikdy nepou¾ijeme zpìtnou hranu, kterou jsme je¹tì nesestrojili a~víme i~to, ¾e celou konstrukci zvládneme v~lineárním èase s~délkou jehly. + +\s{Vìta:} Algoritmus KMP najde v¹echny výskyty v~èase $O(J+S)$. + +\proof +Lineární èas s~délkou jehly potøebujeme na~postavení automatu, lineární èas s~délkou sena pak potøebujeme na~samotné vyhledání. + +\h{Rabinùv-Karpùv algoritmus} + +Nyní si uká¾eme je¹tì jeden algoritmus na~hledání jedné jehly, který nebude mít v~nejhor¹ím pøípadì lineární slo¾itost, ale bude ji mít prùmìrnì. Bude daleko jednodu¹¹í a~uká¾e se, ¾e je v~praxi daleko rychlej¹í. Bude to algoritmus zalo¾ený na~hashování. + + +Pøedstavme si, ¾e máme seno délky $S$ a~jehlu délky $J$, a~vezmìme si nìjakou hashovací funkci, které dáme na~vstup $J$-tici znakù (tedy podslova dlouhá jako jehla). Tato hashovací funkce nám je pak zobrazí do~mno¾iny $\{0,\ldots,N-1\}$ pro nìjaké dost velké~$N$. Jak nám toto pomù¾e pøi hledání jehly? Vezmeme si libovolné \uv{okénko} délky $J$ a~ne¾ budeme zji¹»ovat, zda se v~nìm jehla vyskytuje, tak si spoèítáme hashovací funkci a~porovnáme ji s~hashem jehly. Èili ptáme se, jestli je hash ze sena od~nìjaké pozice $I$ do~pozice $I+J$ roven hashi jehly -- formálnì: $h(\sigma [I: I+J ]) = h(\iota)$. Teprve tehdy, kdy¾ zjistíme, ¾e se hodnota hashovací fce shoduje, zaèneme doopravdy porovnávat øetìzce. + +Není to ale nìjaká hloupost? Mù¾e nám vùbec takováto konstrukce pomoci? Není to tak, ¾e na~spoèítání hashovací funkce z~$J$ znakù, potøebujeme tìch $J$ znakù pøeèíst, co¾ je stejnì rychlé, jako rovnou øetìzce porovnávat? Pou¾ijeme trik, který bude spoèívat v~tom, ¾e si zvolíme ¹ikovnou hashovací funkci. Udìláme to tak, abychom ji mohli pøi posunutí \uv {okénka} o~jeden znak doprava v~konstantním èase pøepoèítat. Chceme umìt z~$h(x_1 \dots x_j)$ spoèítat $h(x_2 \dots x_{j+1})$. +Na~zaèátku si tedy spoèítáme hash jehly a~první $J$-tice znakù sena. Pak ji¾ jenom posouváme \uv {okénko} o~jedna, pøepoèítáme hashovací funkci a~kdy¾ se shoduje s~hashem jehly, tak porovnáme. Budeme pøitom vìøit tomu, ¾e pokud se tam jehla nevyskytuje, pak máme hashovací funkci natolik rovnomìrnou, ¾e pravdìpodobnost toho, ¾e se pøesto strefíme do~hashe jehly, je $1/N$. Jinými slovy jenom v~jednom z~øádovì $N$ pøípadù budeme porovnávat fale¹nì -- tedy provedeme porovnání a~vyjde nám, ¾e výsledek je neshoda. V~prùmìrném pøípadì tedy mù¾eme stlaèit slo¾itost a¾ témìø k~lineární. + +Podívejme se teï na~prùmìrnou èasovou slo¾itost. Budeme urèitì potøebovat èas na~projití jehly a~sena. Navíc strávíme nìjaký èas nad fale¹nými porovnáními, kterých bude v~prùmìru na~ka¾dý $N$-tý znak sena jedno porovnání s~jehlou -- tedy $SJ / N$, pøièem¾ $N$ mù¾eme zvolit dost velké na~to, abychom tento èlen dostali pod nìjakou rozumnou konstantu... Nakonec budeme potøebovat jedno porovnání na~ka¾dý opravdový výskyt, èemu¾ se nevyhneme. Pøipoèteme tedy je¹tì $J \cdot$ {\I $\sharp$výskytù}. Dostáváme tedy: $ \O(J+S+SJ/N+J \cdot$ {\I $\sharp$výskytù}). + +Zbývá malièkost -- toti¾ kde vzít hashovací funkci, která toto v¹e splòuje. Jednu si uká¾eme. Bude to vlastnì takový hezký polynom: +$$h(x_1 \dots x_j) := \left(\sum_{I=1}^{J} x_I \cdot p^{J-I}\right) \bmod N.$$ +Jinak zapsáno se tedy jedná o: +$$(x_1 \cdot p^{J-1} + x_2 \cdot p^{J-2} + \dots + x_J \cdot p^0 ) \bmod N.$$ +Po posunutí okénka o~jedna chceme dostat: +$$(x_2 \cdot p^{J-1} + x_3 \cdot p^{J-2} + \dots + x_J \cdot p^1 + x_{J+1} \cdot p^0 ) \bmod N.$$ +Kdy¾ se ale podíváme na~èleny tìchto dvou polynomù, zjistíme, ¾e se li¹í jen o~málo. Pùvodní polynom staèí pøenásobit~$p$, odeèíst první èlen s~$x_1$ a~naopak pøièíst chybìjící èlen $x_{J+1}$. Dostáváme tedy: +$$h(x_2 \dots x_{J+1}) = (p \cdot h(x_1 \dots x_J) - x_1 \cdot p^J + x_{J+1}) \bmod N.$$ +Pøepoèítání hashovací funkce tedy není nic jiného, ne¾ pøenásobení té minulé~$p$, odeètení nìjakého násobku toho znaku, který vypadl z~okénka, a~pøiètení toho znaku, o~který se okénko posunulo. Pokud tedy máme k~dispozici aritmetické operace v~konstantním èase, zvládneme konstantnì pøepoèítávat i~hashovací funkci. + +Tato hashovací funkce se dokonce nejen hezky poèítá, ale dokonce se i~opravdu \uv{hezky} chová (tedy \uv{rozumnì} náhodnì), pokud zvolíme vhodné~$p$. To bychom mìli zvolit tak, aby bylo rozhodnì nesoudìlné s~$N$ -- tedy $\(p, N) = 1$. Aby se nám navíc dobøe projevilo modulo obsa¾ené v~hashovací funkci, mìlo by být~$p$ relativnì velké (lze dopoèítat, ¾e optimum je mezi $2/3 \cdot N$ a~$3/4 \cdot N$). S~takto zvoleným~$p$ se tato hashovací funkce chová velmi pøíznivì a~v~praxi má celý algoritmus takøka lineární èasovou slo¾itost (prùmìrnou). + +\h{Hledání více øetìzcù najednou} +Nyní si zahrajeme tuté¾ hru, ov¹em v~trochu slo¾itìj¹ích kulisách. Podíváme se na~algoritmus, který si poradí i~s více ne¾ jednou jehlou. +Mìjme tedy jehly $\iota_1 \dots \iota_n$, a~jejich délky $J_i = \vert \iota_i \vert $. Dále budeme potøebovat seno $\sigma$ délky $S=\vert \sigma \vert$. + +Pøedtím, ne¾ se pustíme do~vlastního vyhledávacího algoritmu, mo¾ná bychom si mìli ujasnit, co vlastnì bude jeho výstupem. U problému hledání jedné jehly to bylo jasné -- byla to nìjaká mno¾ina pozic v~senì, na~kterých zaèínaly výskyty jehly. Jak tomu ale bude zde? Sice bychom také mohli vrátit pouze mno¾inu pozic, ale my budeme chtít malièko víc. Budeme toti¾ chtít vìdìt i~to, která jehla se na~které pozici vyskytuje. Výstup tedy bude vypadat následovnì: $V = \{(i,j)~\vert~\sigma[i:i+J_j]= \iota_j \}$. + +Zde se v¹ak skrývá jedna drobná zrada. Budeme se asi muset vzdát nadìje, ¾e najdeme algoritmus, jeho¾ slo¾itost je lineární v~celkové délce v¹ech jehel a~sena. Výstup toti¾ mù¾e být del¹í ne¾ lineární. Mù¾e se nám klidnì stát, ¾e na~jedné pozici v~senì se bude vyskytovat více rùzných jehel -- pokud bude jedna jehla prefixem jiné (co¾ jsme nikde nezakázali), tak máme povinnost ohlásit oba výskyty. Vzhledem k~tomu budeme hledat takový algoritmus, který bude lineární v~délce vstupu plus délce výstupu, co¾ je evidentnì to nejlep¹í, èeho mù¾eme dosáhnout. + +Algoritmus, který si nyní uká¾eme, vymysleli nìkdy v~roce 1975 pan Aho a~paní Corasicková. Bude to takové zobecnìní Knuthova-Morrisova-Prattova algoritmu. + +\h{Algoritmus Aho-Corasicková} + +Opìt se budeme sna¾it sestrojit nìjaký vyhledávací automat a~nìjakým zpùsobem tento automat pou¾ít k~procházení sena. Podívejme se nejprve na~pøíklad. Budeme chtít vyhledávat tato slova: |ara|, |bar|, |arab|, |baraba|, |barbara|. Mìjme tedy tìchto pìt jehel a~rozmysleme si, jak by vypadal nìjaký automat, který by tato slova umìl zatím jenom rozpoznávat. Pro jedno slovo automat vypadal jako cesta, zde u¾ to bude strom. (viz obrázek). + +\figure{ara_strom_blank.eps}{Vyhledávací automat -- strom.}{1in} + +Navíc budeme muset do~automatu zanést, kde nìjaké slovo konèí. V~pùvodním automatu pro jedno slovo to bylo jednoduché -- ono jedno jediné slovo odpovídalo poslednímu vrcholu cesty. Tady se v¹ak slova mohou vyskytovat vícekrát a~konèit nejenom v~listech ale i~v~nìjakém vnitøním vrcholu (co¾ se stane tehdy, pokud je jedno hledané slovo prefixem jiného hledaného slova). Formálnì to nebudeme dokazovat, ale snadno nahlédneme, ¾e listy stromu odpovídají hledaným slovùm, ale opaènì to neplatí. + +\figure{ara_strom_end.eps}{Vyhledávací automat s~konci slov.}{1in} + +Dále bychom mìli do~automatu pøidat zpìtné hrany. Jejich definice bude úplnì stejná jako u automatu pro hledání jednoho slova. Jinými slovy z~ka¾dého stavu pùjde zpìtná hrana do~nejdel¹ího vlastního suffixu, který je stavem. Èili kdy¾ budeme mít nìjaké jméno stavu, budeme se ho sna¾it co nejménì (ale alespoò o~znak) zkrátit zleva, abychom zase dostali jméno stavu. Z~koøene -- prázdného stavu -- pak evidentnì ¾ádná zpìtná hrana nepovede. + +\figure{ara_strom_final.eps}{Vyhledávací automat se zpìtnými hranami.}{1,25in} + +Zbývá nám je¹tì si rozmyslet, jakým zpùsobem bude ná¹ automat hlásit výstup. Opìt smìøujeme k~tomu, aby se automat po~pøeètení nìjakého kusu textu nacházel ve~stavu odpovídajícímu nejdel¹ímu mo¾nému suffixu toho textu. Zatímco u hledání jediné jehly bylo hlá¹ení výskytù jednoduché -- kdykoliv jsme se dostali na~konec \uv{automatové cestièky} tady to bude opìt slo¾itìj¹í. + +První, co se nabízí, je vyu¾ít toho, ¾e jsme si oznaèili nìjaké vrcholy, kde hledaná slova konèí. Co tedy zkusit hlásit výskyt tohoto slova v¾dy, kdy¾ pøijdeme do~nìjakého oznaèeného vrcholu? Tento zpùsob v¹ak nefunguje, pokud se uvnitø nìkteré jehly skrývá jehla vnoøená. Napøíklad po~pøeètení slova |bara|, nám ná¹ souèasný automat neøíká, ¾e bychom mìli nìjaké slovo ohlásit, a~pøitom tam evidentì konèí podøetìzec |ara|. Stejnì tak pokud pøeèteme |barbara|, u¾ si nev¹imneme toho, ¾e tam konèí zároveò i~|ara|. Pouhé \uv{hlá¹ení teèek} tedy nefunguje. + +Dále si mù¾eme v¹imnout toho, ¾e v¹echna slova, která by se mìla v~daném stavu hlásit, jsou suffixy jména tohoto stavu. Pøitom víme, ¾e zpìtná hrana jméno stavu zkracuje zleva. Tak¾e speciálnì v¹echny suffixy daného stavu, které jsou také stavy, se dají najít tak, ¾e se vydáme po~zpìtných hranách do~koøene. Nabízí se tedy v¾dy projít cestu po~zpìtných hranách a¾ do~koøene a~hlásit v¹echny \uv{teèky}. Tento zpùsob by nám v¹ak celý algoritmus znaènì zpomalil, proto¾e cesta do~koøene mù¾e být relativnì dlouhá, ale teèek na~ní obvykle bude málo. + +Mohli bychom také zkusit si pro ka¾dý stav $\beta$ pøedpoèítat mno¾inu $cache(\beta)$, která by obsahovala v¹echna slova, která máme hlásit, kdy¾ se ve~stavu $\beta$ nacházíme. Pokud pak do~tohoto stavu vstoupíme, podíváme se na~tuto mno¾inu a~vypí¹eme v¹e, co v~ní je. Výpis nám bude evidentnì trvat lineárnì k~velikosti mno¾iny, celkovì tedy lineárnì k~velikosti výstupu. Problém je ale ten, ¾e jednotlivé cache mohou být hodnì velké, tak¾e je nestihneme sestrojit v lineárním èase. (Rozmyslete si pøíklad slovníku, kdy se to stane.) + +To, co nám ale ji¾ opravdu pomù¾e, bude zavedení zkratek. V¹imli jsme si, ¾e po~zpìtných hranách mù¾eme projít do~koøene a~hlásit v¹echny nalezené teèky. Vadilo nám ale, ¾e se mù¾e stát, ¾e budeme dlouho po~cestì chodit a~pøi tom ¾ádné teèky nenalézat. Zavedeme si proto zkratky k~nejbli¾¹í teèce. + +\s{Definice} (zkratková hrana): +Budeme mít tedy nìjakou funkci $slovo(\beta) :=$ slovo, které konèí ve~stavu $\beta$ (nebo $\emptyset$, pokud ¾ádné takové slovo není). Dále pak funkci $out(\beta) :=$ nejbli¾¹í vrchol dosa¾itelný po~zpìtných hranách, èili nejdel¹í vlastní suffix stavu $\beta$, v~nìm¾ je definovaná funkce $slovo$. Trochu lid¹tìji øeèeno, ten nejbli¾¹í dosa¾itelný vrchol, ve~kterém je teèka. + +Po pøidání tìchto zkratkových hran ji¾ máme reprezentaci, ve~které opravdu umíme v~daném stavu vyjmenovat v¹echna slova, která máme vypsat, a~to v~èase lineárním s~tím, kolik tìch slov je. + +\s{Definice:} +Vyhledávací automat sestává ze stromu dopøedných hran (vrcholy jsou prefixy jehel, hrany odpovídají roz¹íøení o~písmenko), zpìtných hran ($z(\beta) :=$ nejdel¹í vlastní suffix slova $\beta$, který je stavem) a~zkratkových hran. + +Automat pak bude na~na¹em pøíkladu vypadat takto (zkratkové hrany jsou znázornìny zelenì): + +\figure{ara_strom_zkr.eps}{Vyhledávací automat se zkratkovými hranami.}{1,25in} + +Nyní u¾ nám zbývá jenom vlastní algoritmus -- nejdøív popí¹eme algoritmus, který bude hledat pomocí takového automatu, a~potom se pustíme do~toho, jak se takový automat staví. + +Nejprve si nadefinujeme, jak vypadá jeden krok automatu. Bude to vlastnì nìjaká funkce, která dostane stav a~písmenko. Ona nás pak pomocí tohoto písmenka posune po~automatu. ($f(\alpha, x)$ bude dopøedná hrana ze stavu $\alpha$ oznaèená písmenem~$x$) + +\s{Krok ($\alpha$, $x$):} +\algo +\:Dokud $f(\alpha, x) = \emptyset~\&~\alpha \neq \~~\alpha \leftarrow z(\alpha)$. +\:Pokud $f(\alpha, x) \neq \emptyset:~~\alpha \leftarrow f(\alpha, x)$. +\:Vrátíme výsledek. +\endalgo + +\s{Hledání:} +\algo +\:$\alpha \leftarrow \$. +\:Pro znaky $x$ ze slova $\sigma$: +\::$\alpha \leftarrow \(\alpha, x)$. +\::$\beta \leftarrow \alpha$ +\::Dokud $\beta \neq \emptyset$: +\:::Je-li $\(\beta) \neq \emptyset$: +\::::Ohlásíme $\(\beta)$. +\:::$\beta \leftarrow \(\beta)$. +\endalgo + +Algoritmus hledání vlastnì není nic jiného, ne¾ prosté projití po~zelených zkratkových hranách ze stavu $\alpha$, ve~kterém právì jsme, a~ohlá¹ení v¹eho, co po~cestì najdeme. + +V ka¾dém okam¾iku se automat nachází ve~stavu, který odpovídá nejdel¹ímu mo¾nému suffixu toho, co jsme u¾ pøeèetli. Dùkaz tohoto invariantu je stejný jako u verze automatu pro hledání pouze jedné jehly, nebo» vychází pouze z~definice zpìtných hran. Podobnì nahlédneme, ¾e èasová slo¾itost vyhledávací procedury je lineární v~délce sena plus to, co spotøebujeme na~hlá¹ení výskytù. Nejprve na~chvíli zapomeneme, ¾e nìjaké výskyty hlásíme a~spoèítáme jenom kroky. Ty mohou vést dopøedu a~zpátky. Krok dopøedu prodlu¾uje jméno stavu o~jedna, krok dozadu zkracuje aspoò o~jedna. Tudí¾ krokù dozadu je maximálnì tolik, co krokù dopøedu a~krokù dopøedu je maximálnì tolik, kolik je délka sena. V¹echny kroky dohromady tedy trvají $\O(S)$. Hlá¹ení výskytù pak trvá $\O(S~+ \vert V \vert)$. Celé hledání tedy trvá lineárnì v~délce vstupu a~výstupu. + +Zbývá nám u¾ jen konstrukce automatu. Opìt vyu¾ijeme faktu, ¾e zpìtná hrana ze stavu $\beta$ vede tam, kam by se dostal automat pøi hledání $\beta$ bez prvního písmenka. Tak¾e zase chceme nìco, jako simulovat výpoèet toho automatu na~slovech bez prvního písmenka a~doufat v~to, ¾e si vystaèíme s~tou èástí automatu, kterou jsme u¾ postavili. Tentokrát to v¹ak nemù¾eme dìlat jedno slovo po~druhém, proto¾e zpìtné hrany mohou vést køí¾em mezi jednotlivými vìtvemi automatu. Mohlo by se nám tedy stát, ¾e pøi hledání nìjakého slova potøebujeme zpìtnou hranu, která vede do~jiného slova, které jsme je¹tì nezkonstruovali. Tak¾e tento postup sel¾e. Mù¾eme v¹ak vyu¾ít toho, ¾e ka¾dá zpìtná hrana vede ve~stromu alespoò o~jednu hladinu vý¹. Mù¾eme tak strom konstruovat po~hladinách. Lze si to tedy pøedstavit tak, ¾e paralelnì spustíme vyhledávání v¹ech slov bez prvních písmenek a~v¾dycky udìláme jeden podkrok ka¾dého z~tìch hledání, co¾ nám dá zpìtné hrany z~dal¹ího patra stromu. + +\s{Konstrukce automatu:} +\algo +\:Zalo¾íme prázdný strom, $r \leftarrow$ jeho koøen. +\:Vlo¾íme do~stromu slova $\iota_1 \dots \iota_n$, nastavíme $slovo(*)$. +\:$z(r) \leftarrow \emptyset$, $out(r) \leftarrow \emptyset$. +\:Zalo¾íme frontu $F$ a~vlo¾íme do~ní syny koøene. +\:$\forall v~\in F:~~z(v) \leftarrow r, \(v) \leftarrow \emptyset$. +\:Dokud $F \neq \emptyset$: +\::Vybereme $u$ z~fronty $F$. +\::Pro v¹echny syny $v$ vrcholu $u$: +\:::$q \leftarrow \(z(u), \)$. +\:::$z(v) \leftarrow q$. +\:::Pokud $slovo(q) \neq \emptyset$, pak $out(v) \leftarrow q$. +\::::Jinak $out(v) \leftarrow out(q)$. +\:::Vlo¾íme $v$ do~fronty $F$. +\endalgo + +To, ¾e tento algoritmus zkonstruuje zpìtné hrany jak má, vyplývá z~toho, ¾e nedìláme nic jiného, ne¾ ¾e spou¹tíme výpoèty po~hladinách na~v¹echna hledaná slova bez prvního písmenka. Stejnì tak to, ¾e dobìhne v~lineárním èase, je takté¾ dùsledkem toho, ¾e efektivnì spou¹tíme v¹echny tyto výpoèty. Jen nìkdy udìláme najednou krok dvou èi více výpoètù (napøíklad |araba| a~|arbara| se poèítají na~zaèátku, dokud jsou stejné, jen jednou). Èasová slo¾itost této konstrukce je tedy men¹í nebo rovna souètu èasových slo¾itostí výpoètù nad v¹emi tìmi slovy. To u¾ ale víme, ¾e je lineární v~celkové délce tìchto slov. Konstrukce automatu tedy trvá nejvý¹e tolik, co hledání v¹ech $\iota_i$, co¾ je $\O(\sum_{i} \iota_i)$. + +\s{Vìta:} Algoritmus Aho-Corasicková najde v¹echny výskyty v~èase +$$\O\left(\sum_i~\iota_i~+~S~+~\sharp\\right).$$ + +Je¹tì se na~závìr zamysleme, jak bychom si takový automat ukládali do~pamìti. Urèitì se nám bude hodit si stavy nìjak oèíslovat (tøeba v~poøadí, v~jakém budou vznikat). Potom funkce pro zpìtné a~zkratkové hrany mohou být reprezentované polem indexovaným èíslem stavu. Funkce {\I Slovo}, která øíká, jaké slovo ve~stavu konèí, zase mù¾e být pole indexované stavem, které nám øekne poøadové èíslo slova ve~slovníku. Pro dopøedné hrany v~ka¾dém vrcholu pak mù¾eme mít pole indexované písmenky abecedy, které nám pro ka¾dé písmenko øekne, buï ¾e taková hrana není, nebo nám øekne, kam tato hrana vede. Je vidìt, ¾e takovéto pole se hodí pro pomìrnì malé abecedy. U¾ pro abecedu A-Z~bude velikosti 26 a~z~vìt¹iny bude prázdné, tak¾e bychom plýtvali pamìtí. V praxi se proto èasto pou¾ívá hashovací tabulka. Pøípadnì bychom mohli mít i~jen jednu velkou spoleènou hashovací tabulku, která bude reprezentovat funkci celou, ve~které budou zahashované dvojice (stav, písmenko). Tìchto dvojic je evidentnì tolik, kolik hran stromu, èili lineárnì s~velikostí slovníku, a~je to asi nejkompaktnìj¹í reprezentace. + + +\bye diff --git a/old/6-kmp/Makefile b/old/6-kmp/Makefile new file mode 100644 index 0000000..1831d14 --- /dev/null +++ b/old/6-kmp/Makefile @@ -0,0 +1,3 @@ +P=6-kmp + +include ../Makerules diff --git a/old/6-kmp/ara_strom.eps b/old/6-kmp/ara_strom.eps new file mode 100644 index 0000000..7d054ab --- /dev/null +++ b/old/6-kmp/ara_strom.eps @@ -0,0 +1,1334 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: inkscape 0.46 +%%Pages: 1 +%%Orientation: Portrait +%%BoundingBox: 163 220 443 747 +%%HiResBoundingBox: 163.64601 220.18395 442.55996 746.83829 +%%EndComments +%%Page: 1 1 +0 842 translate +0.8 -0.8 scale +0 0 0 setrgbcolor +[] 0 setdash +1 setlinewidth +0 setlinejoin +0 setlinecap +gsave [1 0 0 1 0 0] concat +gsave [0.5411688 0 0 0.5411688 203.68828 84.001202] concat +0 0 0 setrgbcolor +[] 0 setdash +1.478282 setlinewidth +2 setlinejoin +0 setlinecap +newpath 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211.71214 440.8137 218.21863 316.61606 220.87291 curveto +stroke +0 0 0 setrgbcolor +[] 0 setdash +0.99999994 setlinewidth +0 setlinejoin +0 setlinecap +newpath +711.15927 159.63036 moveto +703.71296 189.73566 643.62165 205.468 570.98179 213.59973 curveto +531.11935 218.06216 487.47795 220.2357 446.68392 221.23953 curveto +366.0505 223.2237 296.54156 220.6379 289.32855 222.12506 curveto +stroke +grestore +showpage +%%EOF diff --git a/old/6-kmp/barb.svg b/old/6-kmp/barb.svg new file mode 100644 index 0000000..e2a9bfd --- /dev/null +++ b/old/6-kmp/barb.svg @@ -0,0 +1,456 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + + image/svg+xml + + + + + + + + + + + + + + + + ε + b + ba + bar + barb + barba + barbar + barbaro + barbarossa + ... + b a r b a r o ... a + + + + + + + + + + + + + + + + + + + + diff --git a/old/7-geom/7-geom.mp b/old/7-geom/7-geom.mp new file mode 100644 index 0000000..7375c63 --- /dev/null +++ b/old/7-geom/7-geom.mp @@ -0,0 +1,276 @@ +input lib + +figname("7-geom"); + +figtag("male_obaly"); +beginfig(1); + pickup boldpen; + labeloffset:=1cm; + pair c,pos; c := (0,0); pos := c; + drawemptyvertex(c); + label.bot(btex $n=1$ etex, pos); + c := (2cm,0); + pos := pos + c; + pair A[]; + A[0] := (-0.3cm, -0.2cm)+c; A[1] := (0.2cm, 0.3cm)+c; + draw A[0]--A[1]; + drawemptyvertex(A[0]); drawemptyvertex(A[1]); + label.bot(btex $n=2$ etex, pos); + + pos := pos + c; + A[2] := (+0.3cm, -0.4cm)+c; + for i := 0 upto 2: A[i] := A[i] shifted c; endfor + draw A[0]--A[1]--A[2]--cycle; + for i := 0 upto 2: drawemptyvertex(A[i]); endfor + label.bot(btex $n=3$ etex, pos); + + pos := pos + c; + A[3] := (A[0]+A[1]+A[2])/3; + for i := 0 upto 3: A[i] := A[i] shifted c; endfor + draw A[0]--A[1]--A[2]--cycle; + for i := 0 upto 2: drawemptyvertex(A[i]); endfor + draw vertex(A[3]); + + c := (1cm,0); + pos := pos + c/2; + A[3] := A[1]+(0.3cm,-0.2cm); + for i := 0 upto 3: A[i] := A[i] shifted c; endfor + draw A[0]--A[1]--A[3]--A[2]--cycle; + for i := 0 upto 3: drawemptyvertex(A[i]); endfor + label.bot(btex $n=4$ etex, pos); +endfig; + +figtag("pridani_bodu"); +beginfig(2); + pair A[],B[],C,shift; shift := (4.5cm,0); + A[0] := (-1.7cm,1.1cm); + A[1] := (-1.2cm,1.2cm); + A[2] := (-0.4cm,1cm); + A[3] := (0.2cm,0.2cm); + A[4] := (0.4cm,-0.7cm); + A[5] := (-0.8cm,-1.3cm); + A[6] := (-1.4cm,-1.4cm); + B[0] := (-1.1cm, 0.7cm); + B[1] := (-0.6cm, 0.1cm); + B[2] := (-1.3cm, -0.6cm); + C := (1cm, 0.1cm); + + % krok 1 + pickup boldpen; + draw A[0] for i := 1 upto 6: --A[i] endfor; + for i := 1 upto 5: drawemptyvertex(A[i]); endfor + for i := 0 upto 2: draw vertex(B[i]); endfor + draw vertex(C); + drawarrow (C+(0.5cm,0)--C+(1.5cm,0)) shifted (0, -0.1cm) withpen normalpen; + for i:=0 upto 6: A[i] := A[i] shifted shift; endfor + for i:=0 upto 2: B[i] := B[i] shifted shift; endfor + C := C shifted shift; + + % krok 2 + draw A[0] for i := 1 upto 6: --A[i] endfor; + draw A[4]{dir 70}..C; + draw A[4]{dir 45}..C; + draw C--A[2] dashed evenly withpen normalpen; + draw C--A[3] dashed evenly withpen normalpen; + for i := 1 upto 5: drawemptyvertex(A[i]); endfor + for i := 0 upto 2: draw vertex(B[i]); endfor + drawemptyvertex(C); + drawarrow (C+(0.5cm,0)--C+(1.5cm,0)) shifted (0,-0.1cm) withpen normalpen; + for i:=0 upto 6: A[i] := A[i] shifted shift; endfor + for i:=0 upto 2: B[i] := B[i] shifted shift; endfor + C := C shifted shift; + + % krok 3 + draw for i := 0 upto 2: A[i]-- endfor C for i := 4 upto 6: --A[i] endfor; + for i := 1 upto 2: drawemptyvertex(A[i]); endfor + for i := 4 upto 5: drawemptyvertex(A[i]); endfor + for i := 0 upto 2: draw vertex(B[i]); endfor + draw vertex(A[3]); + drawemptyvertex(C); +endfig; + +figtag("obalky"); +beginfig(3); + labeloffset := 0.2cm; + pickup boldpen; + pair A[],B[]; + A[0] := (-7cm, 0cm); + A[1] := (-6.2cm, 0.9cm); + A[2] := (-4.6cm,1.5cm); + A[3] := (-2.4cm,1.8cm); + A[4] := (-0.8cm,1.5cm); + A[5] := (0.4cm,0.6cm); + A[6] := (0.8cm,-0.10cm); + A[7] := (-1.6cm,-1.9cm); + A[8] := (-4cm,-2.1cm); + A[9] := (-6cm, -1.5cm); + A[10] := (-7cm, 0cm); + + B[0] := (-2.2cm, 0.7cm); + B[1] := (-1.2cm, 0.1cm); + B[2] := (-2.6cm, -0.6cm); + B[3] := (-3.6cm, -0.4cm); + B[4] := (-3cm, 0.6cm); + B[5] := (-2.6cm, 1cm); + B[6] := (-1cm, -1.2cm); + B[7] := (-6.5cm, 0.2cm); + B[8] := (-5cm, 0.8cm); + B[9] := (-6cm, -0.6cm); + B[10] := (-5cm, -1.2cm); + + draw createpath(for i := 0 upto 5: A[i]-- endfor A[6]); + draw (for i := 6 upto 9: A[i]-- endfor A[10]) dashed evenly; + for i := 0 upto 9: drawemptyvertex(A[i]); endfor + for i := 0 upto 10: draw vertex(B[i]); endfor + + label(btex \font\myfont=csr10 \myfont horní obálka etex, ((-7cm+0.8cm)/2,2.2cm)); + label(btex \font\myfont=csr10 \myfont dolní obálka etex, ((-7cm+0.8cm)/2,-2.5cm)); + label.lft(btex $L$ etex, A[0]); + label.rt(btex $P$ etex, A[6]); +endfig; + +figtag("determinant"); +beginfig(4); + labeloffset := 0.1cm; + pair A[], shift; shift := (4cm,1cm); + + % det(M) > 0 + A[0] := (-2cm, 0); + A[1] := (0,-1cm); + A[2] := (1.5cm, 0cm); + A[3] := A[0] + A[2] - A[1]; + + fill A[0]--A[1]--A[2]--A[3]--cycle withcolor 0.8white; + draw A[0]--A[1]--A[2]--A[3]--cycle dashed evenly; + drawarrow A[0]--0.96[A[0],A[1]] withpen boldpen; + drawarrow A[1]--0.96[A[1],A[2]] withpen boldpen; + for i:=0 upto 2: draw vertex(A[i]); endfor + label.lft(btex $h_{k-1}$ etex, A[0]); + label.bot(btex $h_k$ etex, A[1]); + label.llft(btex $\vec u$ etex, 0.5[A[0],A[1]]); + label.lrt(btex $\vec v$ etex, 0.5[A[1],A[2]]); + label.rt(btex $b$ etex, A[2]); + label(btex $\det(M) > 0$ etex, 0.5[A[0],A[2]]); + + % det(M) = 0 + A[0] := (-1cm, -0.5cm) + shift; + A[1] := (0, -1cm) + shift; + A[2] := (1cm, -1.5cm) + shift; + drawarrow A[0]--0.96[A[0],A[1]] withpen boldpen; + drawarrow A[1]--0.96[A[1],A[2]] withpen boldpen; + for i:=0 upto 2: draw vertex(A[i]); endfor + label.lft(btex $h_{k-1}$ etex, A[0]); + label.llft(btex $h_k$ etex, A[1]); + label.llft(btex $\vec u$ etex, 0.5[A[0],A[1]]); + label.llft(btex $\vec v$ etex, 0.5[A[1],A[2]]); + label.bot(btex $b$ etex, A[2]); + label(btex $\det(M) = 0$ etex, origin) shifted (0,0.3cm) rotated -28 shifted 0.5[A[0], A[2]]; + + % det(M) < 0 + shift := (7.5cm, 1.25cm); + A[0] := (-1cm, -0.5cm) + shift; + A[1] := (1.5cm, -1cm) + shift; + A[2] := (2cm, -2.5cm) + shift; + A[3] := A[0] + A[2] - A[1]; + fill A[0]--A[1]--A[2]--A[3]--cycle withcolor 0.8white; + draw A[0]--A[1]--A[2]--A[3]--cycle dashed evenly; + drawarrow A[0]--0.96[A[0],A[1]] withpen boldpen; + drawarrow A[1]--0.96[A[1],A[2]] withpen boldpen; + for i:=0 upto 2: draw vertex(A[i]); endfor + label.lft(btex $h_{k-1}$ etex, A[0]); + label.urt(btex $h_k$ etex, A[1]); + label.top(btex $\vec u$ etex, 0.5[A[0],A[1]]); + label.rt(btex $\vec v$ etex, 0.5[A[1],A[2]]); + label.rt(btex $b$ etex, A[2]); + label(btex $\det(M) < 0$ etex, 0.5[A[0],A[2]]); +endfig; + +figtag("rybi_motivace"); +beginfig(5); + u := 0.3cm; + def draw_fish(expr pos,size,rot) = + draw ((-1.3u*size,0){dir 60}..{right}(u*size,-u*size/4)) rotated rot shifted pos; + draw ((-1.3u*size,0){dir -50}..{right}(u*size,u*size/4)) rotated rot shifted pos; + draw ((u*size,-u*size/4)--(u*size,u*size/4)) rotated rot shifted pos; + draw (-1u*size,u*size/15) rotated rot shifted pos withpen pencircle scaled (u/8); + for i:=1 upto 3: draw (dirs((u*size,-u*size/4+i*u*size/8), 180, u*size/6)) rotated rot shifted pos; endfor + enddef; + + pair A[],B[]; + A[0] := (-7cm, 0cm); + A[1] := (-6.2cm, 0.9cm); + A[2] := (-4.6cm,1.5cm); + A[3] := (-2.4cm,1.8cm); + A[4] := (-0.8cm,1.5cm); + A[5] := (0.4cm,0.6cm); + A[6] := (0.8cm,-0.10cm); + A[7] := (-1.6cm,-1.9cm); + A[8] := (-4cm,-2.1cm); + A[9] := (-6cm, -1.5cm); + A[10] := (-7cm, 0cm); + + B[0] := (-2.2cm, 0.7cm); + B[1] := (-1.2cm, 0.1cm); + B[2] := (-2.6cm, -0.6cm); + B[3] := (-3.6cm, -0.4cm); + B[4] := (-3cm, 0.6cm); + B[5] := (-2.6cm, 1cm); + B[6] := (-1cm, -1.2cm); + B[7] := (-6.5cm, 0.2cm); + B[8] := (-5cm, 0.8cm); + B[9] := (-6cm, -0.6cm); + B[10] := (-5cm, -1.2cm); + + for i:=0 upto 9: draw_fish(A[i], 1, 0); endfor; + for i:=0 upto 10: draw_fish(B[i], 1, 0); endfor; + draw createpath(for i:=0 upto 9: A[i]-- endfor cycle) scaled 1.13 shifted (0.4cm,0) withpen boldpen; +endfig; + +figtag("provazkovy_algoritmus"); +beginfig(6); + pickup boldpen; + pair A[],B[],u; u := (-3cm, 0); + for i := 0 upto 3: A[i] := u rotated (-30*i) yscaled 0.7; endfor; + A[2] := A[2] + (0,0.1cm); + draw for i:=0 upto 2: A[i]-- endfor A[3]; + drawarrow ((u/2) for i:=1 upto 3: ..u/2 rotated (-30*i) endfor) yscaled 0.7 withpen normalpen; + B[0] := (-2cm,0.5cm); + B[1] := (-1cm,1.5cm); + B[2] := (-0.5cm,0.2cm); + for i:=0 upto 2: draw vertex(B[i]); endfor + + path ub; ub := (-20cm,3cm)--(20cm,3cm); + + numeric ang[]; ang[0] = 90; ang[1] = angle(A[1]-A[0]); ang[2] = angle(A[2]-A[1]); ang[3] = angle(A[3]-A[2]); + for i:=0 upto 2: + draw reverse(dirs(A[i],ang[i],6cm) cutafter ub) withpen normalpen dashed evenly; + l := 1cm + (i-1)*0.2cm; + drawarrow from(A[i],ang[i],l)..from(A[i],(ang[i]+ang[i+1])/2,l)..from(A[i],ang[i+1],l) withpen normalpen; + endfor + + for i:=0 upto 3: drawemptyvertex(A[i]); endfor; +endfig; + +figtag("naslednik_pres_konvexni_obal"); +beginfig(7); + pair A[], C; + label.lrt(btex $Q_i$ etex, (1.5cm,-0.8cm)); + pickup boldpen; + + C := (-4cm,-0.3cm); + for i:=0 upto 6: + A[i] := (2cm,0) rotated (360*i/7+5) yscaled 0.7; + draw vertex(A[i]); + endfor; + draw for i:=0 upto 6: A[i]-- endfor cycle withpen normalpen; + draw C--A[2] dashed evenly; + + draw dirs(C, -140, 0.5cm); + drawemptyvertex(A[2]); + drawemptyvertex(C); + drawdblarrow (fullcircle scaled 2cm rotated (360*2/7-5) yscaled 0.7) cutbefore (origin--(3cm,0) rotated (360*2/7+25)) withpen normalpen; + %drawarrow C+(0,0.5cm){dir 60}..A[2]+(0,0.5cm) withpen normalpen; + %drawarrow 0.6A[5]{dir 170}..(0.6A[3] rotated -15) withpen normalpen; + %drawarrow 0.7A[6]{dir 60}..(0.5A[1] rotated 30) withpen normalpen; +endfig; +end diff --git a/old/7-geom/7-geom.tex b/old/7-geom/7-geom.tex new file mode 100644 index 0000000..d600d11 --- /dev/null +++ b/old/7-geom/7-geom.tex @@ -0,0 +1,136 @@ +\input lecnotes.tex + +\prednaska{7}{Geometrické algoritmy}{(sepsal Pavel Klavík)} + +\>Uká¾eme si nìkolik základních algoritmù na øe¹ení geometrických problémù v~rovinì. Proè zrovna v~rovinì? Inu, jednorozmìrné problémy bývají triviální +a naopak pro vy¹¹í dimenze jsou velice komplikované. Rovina je proto rozumným kompromisem mezi obtí¾ností a zajímavostí. + +Celou kapitolou nás bude provázet pohádka ze ¾ivota ledních medvìdù. Pokusíme se vyøe¹it jejich \uv{ka¾dodenní} problémy~\dots + +\h{Hledání konvexního obalu} + +{\I Daleko na severu ¾ili lední medvìdi. Ve vodách tamního moøe byla hojnost ryb a jak je známo, ryby jsou oblíbenou pochoutkou ledních medvìdù. +Proto¾e medvìdi z~na¹í pohádky rozhodnì nejsou ledajací a ani chytrost jim neschází, rozhodli se v¹echny ryby pochytat. Znají pøesná místa výskytu +ryb a rádi by vyrobili obrovskou sí», do které by je v¹echny chytili. Pomozte medvìdùm zjistit, jaký nejmen¹í obvod taková sí» mù¾e mít.} + +\figure{7-geom5_rybi_motivace.eps}{Problém ledních mìdvìdù: Jaký je nejmen¹í obvod sítì?}{3in} + +Neboli v~øeèi matematické, chceme pro zadanou mno¾inu bodù v~rovinì nalézt její konvexní obal. Co je to konvexní obal? Mno¾ina bodù je {\I konvexní}, +pokud pro ka¾dé dva body obsahuje i celou úseèku mezi nimi. {\I Konvexní obal} je nejmen¹í konvexní podmno¾ina roviny, která obsahuje v¹echny zadané +body.\foot{Pamatujete si na lineární obaly ve vektorových prostorech? Lineární obal mno¾iny vektorù je nejmen¹í vektorový podprostor, který tyto +vektory obsahuje. Není náhoda, ¾e tato definice pøipomíná definici konvexního obalu. Na druhou stranu ka¾dý vektor z~lineárního obalu lze vyjádøit +jako lineární kombinaci daných vektorù. Podobnì platí i pro konvexní obaly, ¾e ka¾dý bod z~obalu je konvexní kombinací daných bodù. Ta se li¹í od +lineární v~tom, ¾e v¹echny koeficienty jsou v~intervalu $[0,1]$ a navíc souèet v¹ech koeficientù je $1$. Tento algebraický pohled mù¾e mnohé vìci +zjednodu¹it. Zkuste si dokázat, ¾e obì definice konvexního obalu jsou ekvivalentní.} Z~algoritmického hlediska nás v¹ak bude zajímat jenom jeho +hranice, kterou budeme dále oznaèovat jako konvexní obal. + +Na¹ím úkolem je nalézt konvexní obal koneèné mno¾iny bodù. To je v¾dy konvexní mnohoúhelník, navíc s~vrcholy v~zadaných bodech. Øe¹ením problému tedy +bude posloupnost bodù, které tvoøí konvexní obal. Pro malé mno¾iny je konvexní obal nakreslen na obrázku, pro více bodù je v¹ak situace mnohem +slo¾itìj¹í. + +\figure{7-geom1_male_obaly.eps}{Konvexní obaly malých mno¾in.}{3in} + +Pro jednoduchost budeme pøedpokládat, ¾e v¹echny body mají rùzné $x$-ové souøadnice. Tedy utøídìní bodù zleva doprava je urèené jednoznaènì.\foot{To si +mù¾eme dovolit pøedpokládat, nebo» se v¹emi body staèí nepatrnì pootoèit. Tím konvexní obal urèitì nezmìníme. Av¹ak jednodu¹¹í øe¹ení je naprogramovat +tøídìní lexikograficky (druhotnì podle souøadnice $y$) a vyøadit identické body.} Tím máme zaji¹tìné, ¾e existují dva body, nejlevìj¹í a +nejpravìj¹í, pro které platí následující invariant: + +\s{Invariant:} Nejlevìj¹í a nejpravìj¹í body jsou v¾dy v~konvexním obalu. + +Algoritmus na nalezení konvexního obalu funguje na následujícím jednoduchém principu, kterému se nìkdy øíká {\I zametání roviny}. Procházíme body +zleva doprava a postupnì roz¹iøujeme doposud nalezený konvexní obal o~dal¹í body. Na zaèátku bude konvexní obal jediného bodu samotný bod. Na konci +$k$-tého kroku algoritmu známe konvexní obal prvních $k$ bodù. Kdy¾ algoritmus skonèí, známe hledaný konvexní obal. Podle invariantu musíme v~$k$-tém +kroku pøidat do obalu $k$-tý nejlevìj¹í bod. Zbývá si jen rozmyslet, jak pøesnì tento bod pøidat. + +Pøidání dal¹ího bodu do konvexního obalu funguje, jak je naznaèeno na obrázku. Podle invariantu víme, ¾e bod nejvíc vpravo je souèástí konvexního +obalu. Za nìj napojíme novì pøidávaný bod. Tím jsme získali nìjaký obal, ale zpravidla nebude konvexní. To lze v¹ak snadno napravit, staèí +odebírat body, v obou smìrech podél konvexního obalu, tak dlouho, dokud nezískáme konvexní obal. Na pøíkladu z obrázku nemusíme po smìru hodinových +ruèièek odebrat ani jeden bod, obal je v poøádku. Naopak proti smìru hodinových ruèièek musíme odebrat dokonce dva body. + +\figure{7-geom2_pridani_bodu.eps}{Pøidání bodu do konvexního obalu.}{4.5in} + +Pro pøípadnou implementaci a rozbor slo¾itosti si nyní popí¹eme algoritmus detailnìji. Aby se lépe popisoval, rozdìlíme si konvexní obal na dvì èásti +spojující nejlevìj¹í a nejpravìj¹í bod obalu. Budeme jim øíkat {\I horní obálka} a {\I dolní obálka}. + +\figure{7-geom3_obalky.eps}{Horní a dolní obálka konvexního obalu.}{3.4in} + +Obì obálky jsou lomené èáry, navíc horní obálka poøád zatáèí doprava a dolní naopak doleva. Pro udr¾ování bodù v~obálkách staèí dva zásobníky. +V~$k$-tém kroku algoritmu pøidáme zvlá¹» $k$-tý bod do horní i dolní obálky. Pøidáním $k$-tého bodu se v¹ak mù¾e poru¹it smìr, ve kterém obálka +zatáèí. Proto budeme nejprve body z~obálky odebírat a $k$-tý bod pøidáme a¾ ve chvíli, kdy jeho pøidání smìr zatáèení neporu¹í. + +\s{Algoritmus:} + +\algo + +\:Setøídíme body podle $x$-ové souøadnice, oznaème body $b_1, \ldots, b_n$. +\:Vlo¾íme do horní a dolní obálky bod $b_1$: $H = D = (b_1)$. +\:Pro ka¾dý dal¹í bod $b = b_2,\ldots,b_n$: +\::Pøepoèítáme horní obálku: +\:::Dokud $\vert H\vert \ge 2$, $H = (\ldots, h_{k-1}, h_k)$ a úhel $h_{k-1} h_k b$ je orientovaný doleva: +\::::Odebereme poslední bod $h_k$ z~obálky $H$. +\:::Pøidáme bod $b$ do obálky $H$. +\::Symetricky pøepoèteme dolní obálku (s orientací doprava). +\: Výsledný obal je tvoøen body v~obálkách $H$ a $D$. + +\endalgo + +Rozebereme si èasovou slo¾itost algoritmu. Setøídit body podle $x$-ové souøadnice doká¾eme v~èase $\O(n \log n)$. Pøidání dal¹ího bodu do obálek +trvá lineárnì vzhledem k~poètu odebraných bodù. Zde vyu¾ijeme obvyklý postup: Ka¾dý bod je odebrán nejvý¹e jednou, a tedy v¹echna odebrání trvají +dohromady $\O(n)$. Konvexní obal doká¾eme sestrojit v~èase $\O(n \log n)$, a pokud bychom mìli seznam bodù ji¾ utøídený, doká¾eme to dokonce v +$\O(n)$. + +\s{Algebraický dodatek:} Existuje jednoduchý postup, jak zjistit orientaci úhlu? Uká¾eme si jeden zalo¾ený na lineární algebøe. Budou se hodit +vlastnosti determinantu. Absolutní hodnota determinantu je objem rovnobì¾nostìnu urèeného øádkovými vektory matice. Dùle¾itìj¹í v¹ak je, ¾e znaménko +determinantu urèuje \uv{orientaci} vektorù, zda je levotoèivá èi pravotoèivá. Proto¾e ná¹ problém je rovinný, budeme uva¾ovat determinanty matic $2 +\times 2$. + +Uva¾me souøadnicový systém v~rovinì, kde $x$-ová souøadnice roste smìrem doprava a~$y$-ová smìrem nahoru. Chceme zjistit orientaci úhlu $h_{k-1} h_k +b$. Polo¾me $\vec u = (x_1, y_1)$ jako rozdíl souøadnic $h_k$ a~$h_{k-1}$ a podobnì $\vec v = (x_2, y_2)$ je rozdíl souøadnic $b$ a~$h_k$. Matice $M$ +je definována následovnì: +$$M = \pmatrix{\vec u \cr \vec v} = \pmatrix {x_1&y_1\cr x_2&y_2}.$$ +Úhel $h_{k-1} h_k b$ je orientován doleva, právì kdy¾ $\det M = x_1y_2 - x_2y_1$ je nezáporný,\foot{Neboli vektory $\vec u$ a $\vec v$ odpovídají +rozta¾ení a zkosení vektorù báze $\vec x = (1,0)$ a $\vec y = (0,1)$, pro nì¾ je determinant nezáporný.} a spoèítat hodnotu determinantu je jednoduché. +Mo¾né situace jsou nakresleny na obrázku. Poznamenejme, ¾e k~podobnému vzorci se lze také dostat pøes vektorový souèin vektorù $\vec u$ a $\vec v$. + +\figure{7-geom4_determinant.eps}{Jak vypadají determinanty rùzných znamének v~rovinì.}{4.6in} + +\s{©lo by to vyøe¹it rychleji?} Také vám vrtá hlavou, zda existují rychlej¹í algoritmy? Na závìr si uká¾eme nìco, co na pøedná¹ce nebylo.\foot{A také +se nebude zkou¹et.} Nejrychlej¹í známý algoritmus, jeho¾ autorem je T.~Chan, funguje v~èase $\O(n \log h)$, kde $h$ je poèet bodù le¾ících na +konvexním obalu, a pøitom je pøekvapivì jednoduchý. Zde si naznaèíme, jak tento algoritmus funguje. + +Algoritmus pøichází s~následující my¹lenkou. Pøedpokládejme, ¾e bychom znali velikost konvexního obalu $h$. Rozdìlíme body libovolnì do $\lceil {n +\over h} \rceil$ mno¾in $Q_1, \ldots, Q_k$ tak, ¾e $\vert Q_i \vert \le h$. Pro ka¾dou z~tìchto mno¾in nalezneme konvexní obal pomocí vý¹e popsaného +algoritmu. To doká¾eme pro jednu v~èase $\O(h \log h)$ a pro v¹echny v~èase $\O(n \log h)$. V druhé fázi spustíme hledání konvexního obalu pomocí +provázkového algoritmu a pro zrychlení pou¾ijeme pøedpoèítané obaly men¹ích mno¾in. Nejprve popí¹eme jeho my¹lenku. Pou¾ijeme následující pozorování: + +\s{Pozorování:} Úseèka spojující dva body $a$ a $b$ le¾í na konvexním obalu, právì kdy¾ v¹echny ostatní body le¾í pouze na jedné její +stranì.\foot{Formálnì je podmínka následující: Pøímka $ab$ urèuje dvì poloroviny. Úseèka le¾í na konvexním obalu, právì kdy¾ v¹echny body le¾í v jedné +z polorovin.} + +Algoritmu se øíká {\I provázkový}, proto¾e svojí èinností pøipomíná namotávání provázku podél konvexního obalu. Zaèneme s bodem, který na konvexním +obalu urèitì le¾í, to je tøeba ten nejlevìj¹í. V ka¾dém kroku nalezneme následující bod po obvodu konvexního obalu. To udìláme napøíklad tak, ¾e +projdeme v¹echny body a vybereme ten, který svírá nejmen¹í úhel s poslední stranou konvexního obalu. Novì pøidaná úseèka vyhovuje pozorování a proto +do konvexního obalu patøí. Po $h$ krocích se dostaneme zpìt k nejlevìj¹ímu bodu a výpoèet ukonèíme. V ka¾dém kroku potøebujeme projít v¹echny body a +vybrat následníka, co¾ doká¾eme v èase $\O(n)$. Celková slo¾itost algoritmu je tedy $\O(n \cdot h)$. + +\twofigures{7-geom6_provazkovy_algoritmus.eps}{Provázkový algoritmus.}{1.25in}{7-geom7_naslednik_pres_konvexni_obal.eps}{Hledání kandidáta v pøedpoèítaném obalu.}{2.5in} + +Provázkový algoritmus funguje, ale má jednu obrovskou nevýhodu -- je toti¾ ukrutnì pomalý. Ký¾eného zrychlení dosáhneme, pokud pou¾ijeme pøedpoèítané +konvexní obaly. Ty umo¾ní rychleji hledat následníka. Pro ka¾dou z mno¾in $Q_i$ najdeme zvlá¹» kandidáta a poté z nich vybereme toho nejlep¹ího. +Mo¾ný kandidát v¾dy le¾í na konvexním obalu mno¾iny $Q_i$. Vyu¾ijeme toho, ¾e body obalu jsou \uv{uspoøádané}, i kdy¾ trochu netypicky do kruhu. +Kandidáta mù¾eme hledat metodou pùlení intervalu, i kdy¾ detaily jsou malièko slo¾itìj¹í ne¾ je obvyklé. Jak pùlit zjistíme podle smìru zatáèení +konvexního obalu. Detaily si rozmyslí ètenáø sám. + +Èasová slo¾itost pùlení je $\O(\log h)$ pro jednu mno¾inu. Mno¾in je nejvý¹e $\O({n \over h})$, tedy následující bod konvexního obalu nalezneme v èase +$\O({n \over h} \log h)$. Celý obal nalezneme ve slibovaném èase $\O(n \log h)$. + +Popsanému algoritmu schází jedna dùle¾itá vìc: Ve skuteènosti vìt¹inou neznáme velikost $h$. Budeme proto algoritmus iterovat s~rostoucí hodnotou $h$, +dokud konvexní obal nesestrojíme. Pokud pøi slepování konvexních obalù zjistíme, ¾e konvexní obal je vìt¹í ne¾ $h$, výpoèet ukonèíme. Zbývá je¹tì +zvolit, jak rychle má $h$ rùst. Pokud by rostlo moc pomalu, budeme poèítat zbyteènì mnoho fází, naopak pøi rychlém rùstu by nás poslední fáze mohla +stát pøíli¹ mnoho. + +V~$k$-té iteraci polo¾íme $h = 2^{2^k}$. Dostáváme celkovou slo¾itost algoritmu: +$$\sum_{m=0}^{\O(\log \log h)} \O(n \log 2^{2^m}) = \sum_{m=0}^{\O(\log \log h)} \O(n \cdot 2^m) = \O(n \log h),$$ +kde poslední rovnost dostaneme jako souèet prvních $\O(\log \log h)$ èlenù geometrické øady $\sum 2^m$. + +\bye diff --git a/old/7-geom/7-geom1_male_obaly.eps b/old/7-geom/7-geom1_male_obaly.eps new file mode 100644 index 0000000..94a501d --- /dev/null +++ b/old/7-geom/7-geom1_male_obaly.eps @@ -0,0 +1,324 @@ +%!PS +%%BoundingBox: -13 -35 216 12 +%%HiResBoundingBox: -12.12236 -34.76685 215.33812 11.24376 +%%Creator: MetaPost 0.993 +%%CreationDate: 2009.11.17:1821 +%%Pages: 1 +%*Font: cmmi10 9.96265 9.96265 6e:8 +%*Font: cmr10 9.96265 9.96265 31:f008 +%%BeginProlog +%%EndProlog +%%Page: 1 1 + 1 1 1 setrgbcolor +newpath 1.99252 0 moveto +1.99252 0.52847 1.78256 1.03523 1.4089 1.4089 curveto +1.03523 1.78256 0.52847 1.99252 0 1.99252 curveto +-0.52847 1.99252 -1.03523 1.78256 -1.4089 1.4089 curveto +-1.78256 1.03523 -1.99252 0.52847 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+++ b/old/7-geom/Makefile @@ -0,0 +1,3 @@ +P=7-geom + +include ../Makerules diff --git a/old/7-geom/lib.mp b/old/7-geom/lib.mp new file mode 100644 index 0000000..2536305 --- /dev/null +++ b/old/7-geom/lib.mp @@ -0,0 +1,82 @@ +% implementation of figure naming and figure transparency +string name,tag; name := ""; tag := ""; +def updatefigname = + if tag="": filenametemplate (name & "%c.eps"); + else: filenametemplate (name & "%c_" & tag & ".eps"); + fi; +enddef; + +def figname(expr n) = name := n; updatefigname; enddef; +def figtag(expr t) = tag := t; updatefigname; enddef; + +picture transparent_picture; +color transparent_color; transparent_color := 0.9white; +def drawtransparent(expr num) = + transparent_picture := currentpicture; +endfig; + +if tag="": filenametemplate (name & "%c_transparent.eps"); +else: filenametemplate (name & "%c_" & tag & "_transparent.eps"); +fi; +beginfig(num); + draw transparent_picture withcolor transparent_color; +endfig; + +updatefigname; +enddef; + +def from(expr p,d,len) = + p+dir(d)*len +enddef; + +def dirs(expr p,d,len) = + p--from(p,d,len) +enddef; + +def drawvertices(expr s,n) = + for i:=s upto n: + draw vertex(PQ[i]); + endfor +enddef; + +def drawfvertices(expr s,n,flags) = + for i:=s upto n: + draw vertex(PQ[i]) flags; + endfor +enddef; + +def vertex(expr p) = p withpen pencircle scaled 4pt enddef; +def drawemptyvertex(expr p) = unfill fullcircle scaled 4pt shifted p; draw fullcircle scaled 4pt shifted p; enddef; +def drawendpointvertex(expr p) = draw vertex(p) withcolor red; draw fullcircle scaled 6pt shifted p; enddef; +def createpath(expr p) = shakepath(p, 0.015cm,0.1cm) enddef; +vardef shakepath(expr p,d,l) = + save r,b; + path r; r := point(arctime 0 of p) of p; + b := -1; + for i:=l step l until arclength(p): + r := r--(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*d*b; + b := -b; + endfor + r--point(arctime arclength(p) of p) of p +enddef; + +pen normalpen; normalpen := pencircle scaled 0.6pt; +pen boldpen; boldpen := pencircle scaled 1.5pt; +pen bolderpen; bolderpen := pencircle scaled 2pt; +def dotline = withdots scaled 0.82 withpen boldpen enddef; + +vardef unclosedbubblec(expr p,c) = + bubblec((p..reverse p..cycle),c) +enddef; + +vardef bubblec(expr p,c) = + save r; + path r; r := (point(arctime 0 of p) of p)+dir(angle(direction(arctime 0 of p) of p rotated 90))*c; + for i:=0.01cm step 0.025cm until arclength(p): + r := r..(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*c; + endfor + r..(point(arctime arclength(p) of p) of p)+dir(angle(direction(arctime arclength(p) of p) of p rotated 90))*c..cycle +enddef; + +vardef bubble(expr p) = bubblec(p,0.12cm) enddef; +vardef unclosedbubble(expr p) = unclosedbubblec(p,0.12cm) enddef; diff --git a/old/8-geom2/8-geom2.mp b/old/8-geom2/8-geom2.mp new file mode 100644 index 0000000..5a44396 --- /dev/null +++ b/old/8-geom2/8-geom2.mp @@ -0,0 +1,137 @@ +input lib + +figname("8-geom2_"); +figtag("usecky"); +beginfig(1); + def drawusecka(expr p,q) = draw vertex(p); draw vertex(q); draw p--q; enddef; + pair A[],B[]; + A0 := origin; B0 := (4cm,1.5cm); + A1 := (0.5cm,1.5cm); B1 := (1cm,0); + A2 := (2cm,1.3cm); B2 := (3cm, -0.9cm); + A3 := (1cm,-1.3cm); B3 := (3.5cm,0); + z0 = whatever[A0,B0]; z0 = whatever[A1,B1]; + z1 = whatever[A0,B0]; z1 = whatever[A2,B2]; + z2 = whatever[A2,B2]; z2 = whatever[A3,B3]; + + for i:=0 upto 2: fill fullcircle scaled 7pt shifted z[i]; drawemptyvertex(z[i]); endfor + for i:=0 upto 3: drawusecka(A[i], B[i]); endfor +endfig; + +figtag("polorovina"); +beginfig(2); + pair A,B; + A := origin; + B := (1.7cm,0.5cm); + an := angle(B-A); + path p; p := from(.5[A,B],an+90,3cm)--from(.5[A,B],an-90,3cm); + fill p--reverse(p shifted (A-0.8[A,B]))--cycle withcolor 0.8white; + %fill p{dir (an+180)}..-0.35[A,B]..{dir an}cycle withcolor 0.8white; + draw p withpen boldpen; + + pair C; C := point(0.9) of p; + drawarrow from(C,an+180,0.1cm)--from(C,an+180,1cm) withpen boldpen; + C := point(0.1) of p; + drawarrow from(C,an+180,0.1cm)--from(C,an+180,1cm) withpen boldpen; + label.rt(btex $p$ etex, point(0.15) of p); + + draw A--B dashed evenly; + drawemptyvertex(A); drawemptyvertex(B); + label.llft(btex $a$ etex, A); + label.urt(btex $b$ etex, B); + label(btex $B_a$ etex, A+2(-0.1cm,0.5cm)); + label(btex $B_b$ etex, B+2(-0.1cm,0.5cm)); +endfig; + +figtag("voroneho_diagram"); +beginfig(3); + u := 1.35cm; + pair A[],B[]; + A0 := origin; A1 := (-u,-u); A2 := (1.3u,-u); A3 := (1.50821u,0.7u); A4 := (0.6u,0.8u); A5 := (0.2u,1.9u); A6 := (-1.3u,1u); + def osa(expr a, b, an,l) = from(.5[a,b], angle(b-a)+an, l) enddef; + vardef prusecik_os(expr p,q,r) = + save b; pair b; + b = whatever[osa(p,q, 90,1cm), osa(p,q,-90,1cm)]; + b = whatever[osa(q,r, 90,1cm), osa(q,r,-90,1cm)]; + b + enddef; + B[0] := prusecik_os(A0,A1,A2); B[1] := prusecik_os(A0,A1,A6); B[2] := prusecik_os(A0,A4,A6); B[3] := prusecik_os(A4,A5,A6); + B[4] := prusecik_os(A3,A4,A5); B[5] := prusecik_os(A0,A3,A4); B[6] := prusecik_os(A0,A2,A4); + draw osa(A1,A2,-90,2cm)--B[0]--B[1]--osa(A1,A6,90,1.3cm) withpen boldpen; + draw B[1]--B[2]--B[3]--osa(A6,A5,90,2cm) withpen boldpen; + draw B[3]--B[4]--osa(A3,A5,-90,2.7cm) withpen boldpen; + draw B[4]--B[5]--B[2] withpen boldpen; + draw B[5]--B[6]--osa(A2,A3,-90,1.3cm) withpen boldpen; + draw B[6]--B[0] withpen boldpen; + + draw A0--A1--A2--A0--A6--A1 dashed evenly; + draw A6--A5--A4--A0 dashed evenly; draw A3--A2 dashed evenly; + draw A3--A5 dashed evenly; draw A6--A4--A3 dashed evenly; + + for i:=0 upto 6: draw vertex(B[i]); endfor + for i:=0 upto 6: drawemptyvertex(A[i]); endfor +endfig; + +figtag("pasy_mnohouhelniku"); +beginfig(4); + u := 1.35cm; + pair A[],B[]; + A0 := origin; A1 := (-u,-u); A2 := (1.3u,-u); A3 := (1.50821u,0.7u); A4 := (0.6u,0.8u); A5 := (0.2u,1.9u); A6 := (-1.3u,1u); + def osa(expr a, b, an,l) = from(.5[a,b], angle(b-a)+an, l) enddef; + vardef prusecik_os(expr p,q,r) = + save b; pair b; + b = whatever[osa(p,q, 90,1cm), osa(p,q,-90,1cm)]; + b = whatever[osa(q,r, 90,1cm), osa(q,r,-90,1cm)]; + b + enddef; + def drawline(expr p) = draw ((-2.3u,p)--(2.5u,p)) cutbefore (D0--D1) cutafter (D2--D3) enddef; + B[0] := prusecik_os(A0,A1,A2); B[1] := prusecik_os(A0,A1,A6); B[2] := prusecik_os(A0,A4,A6); B[3] := prusecik_os(A4,A5,A6); + B[4] := prusecik_os(A3,A4,A5); B[5] := prusecik_os(A0,A3,A4); B[6] := prusecik_os(A0,A2,A4); + + pair C; C := origin; for i:=0 upto 6: C := C + B[i]; endfor C := C/6; + pair D[]; + D0 := C+(-2.25,2.25)*u; D1 := C+(-2.25,-2.25)*u; D2 := C+(2.25,-2.25)*u; D3 := C+(2.25,2.25)*u; + + draw B[0]--B[1]--B[2]--B[3]--B[4]--B[5]--B[6]--B[0] withpen boldpen; + draw B[5]--B[2] withpen boldpen; + pair E; E := 0.6[B0,B4]; + drawline(ypart(E)); + drawline(ypart(E)) cutbefore (B2--B5) cutafter (B4--B5) dashed evenly withpen bolderpen; + drawemptyvertex(E); + + for i:=0 upto 6: drawline(ypart(B[i])) dashed evenly; endfor + for i:=0 upto 6: draw vertex(B[i]); endfor +endfig; + +figtag("upravy_stromu"); +beginfig(5); + u := 1cm; + draw (0,0.1u)--(2.05u,-2.05u)--(-2.05u,-2.05u)--cycle; + pair A[]; A0 := from(origin,-90,0.1u); A1 := from(A0, -70, 0.5u); A2 := from(A1, -110, 0.5u); A3 := from(A2, -80, 0.5u); A4 := from(A3, -120, 0.45u); + path p; p := createpath(A0--A1--A2--A3--A4); + path q; q := (p scaled 0.93 shifted (-0.15u,-0.15u))--(-1.85u,-1.95u)--cycle; fill q withcolor 0.8white; draw q; + q := (p scaled 0.93 shifted (0.15u,-0.15u))--(1.85u,-1.95u)--cycle; fill q withcolor 0.8white; draw q; + p := createpath(A0--from(A0,-110,0.1u)--A1--A2--A3--A4); draw p withpen boldpen; +endfig; + +figtag("rychla_perzistence"); +beginfig(6); + u := 1cm; + def drawtable(expr p, lab, sa, sb) = + draw centersquare xscaled 2u yscaled (2u/3) shifted (p+(0,u/3)); + draw centersquare xscaled 2u yscaled (2u/3) shifted (p-(0,u/3)); + label(sa, p+(0,u/3)); + label(sb, p-(0,u/3)); + label.bot(lab, p-(0,2u/3)); + enddef; + + pair A[]; + for i:=0 upto 2: A[i] := (0,2u) rotated 120i; endfor + drawtable(A1, btex $v$ etex, btex verze 2 etex, btex verze 1 etex); + drawtable(A2, btex $v'$ etex, btex verze 3 etex, ""); + drawtable(A0, btex $u$ etex, btex verze 2 etex, btex verze 1 etex); + + drawarrow from(A[2]+(0,u/3), 180, 7u/6)--from(A[1]+(0,u/3), 0, 7u/6); + drawarrow from(A[0]+(0,-u/3), 180, 7u/6)..{dir -90}from(A[1]+(-3u/4,2u/3), 90, u/6); + drawarrow from(A[0]+(0,u/3), 0, 7u/6)..{dir -90}from(A[2]+(3u/4,2u/3), 90, u/6); +endfig; +end diff --git a/old/8-geom2/8-geom2.tex b/old/8-geom2/8-geom2.tex new file mode 100644 index 0000000..034b395 --- /dev/null +++ b/old/8-geom2/8-geom2.tex @@ -0,0 +1,167 @@ +\input lecnotes.tex + +\prednaska{8}{Geometrie vrací úder}{(sepsal Pavel Klavík)} + +\>Kdy¾ s geometrickými problémy poøádnì nezametete, ony vám to vrátí! Ale kdy¾ u¾ zametat, tak urèitì ne pod koberec a místo smetáku pou¾ijte pøímku. +V této pøedná¹ce nás spolu s dvìma geometrickými problémy samozøejmì èeká pokraèování pohádky o ledních medvìdech. + +{\I Medvìdi vyøe¹ili rybí problém a hlad je ji¾ netrápí. Av¹ak na severu ne¾ijí sami, za sousedy mají Eskymáky. Proto¾e je rozhodnì lep¹í se sousedy +dobøe vycházet, jsou medvìdi a Eskymáci velcí pøátelé. Skoro ka¾dý se se svými pøáteli rád schází. Av¹ak to je musí nejprve nalézt~\dots} + +\h{Hledání prùseèíkù úseèek} + +Zkusíme nejprve Eskymákùm vyøe¹it lokalizaci ledních medvìdù. + +{\I Kdy¾ takový medvìd nemá co na práci, rád se prochází. Na místech, kde se trasy protínají, je zvý¹ená ¹ance, ¾e se dva medvìdi potkají a zapovídají +-- ostatnì co byste èekali od medvìdù. To jsou ta správná místa pro Eskymáka, který chce potkat medvìda. Jenom¾e jak tato køí¾ení najít?} + +Pro zjednodu¹ení pøedpokládejme, ¾e medvìdi chodí po úseèkách tam a zpìt. Budeme tedy chtít nalézt v¹echny prùseèíky úseèek v rovinì. + +\bigskip +\centerline{\epsfxsize=1.5in\epsfbox{8-geom2_0_bear.eps}\hskip 4em\epsfxsize2in\epsfbox{8-geom2_1_usecky.eps}} +\smallskip +\centerline{Problém Eskymákù: Kde v¹ude se køí¾í medvìdí trasy?} +\bigskip + +Pro $n$ úseèek mù¾e existovat a¾ $\Omega(n^2)$ prùseèíkù.\foot{Zkuste takový pøíklad zkonstruovat.} Tedy optimální slo¾itosti by dosáhl i algoritmus, +který by pro ka¾dou dvojici úseèek testoval, zda se protínají. Èasovou slo¾itost algoritmu v¹ak posuzujeme i vzhledem k velikosti výstupu $p$. Typické +rozmístìní úseèek mívá toti¾ prùseèíkù spí¹e pomálu. Pro tento pøípad si uká¾eme podstatnì rychlej¹í algoritmus. + +Pro jednodu¹¹í popis pøedpokládejme, ¾e úseèky le¾í v obecné poloze. To znamená, ¾e ¾ádné tøi úseèky se neprotínají v jednom bodì a prùnikem ka¾dých +dvou úseèek je nejvý¹e jeden bod. Navíc pøedpokládejme, ¾e krajní bod ¾ádné úseèky nele¾í na jiné úseèce a také neexistují vodorovné úseèky. Na závìr si +uká¾eme, jak se s tìmito pøípady vypoøádat. + +Algoritmus funguje na principu zametání roviny, popsaném v minulé pøedná¹ce. Budeme posouvat vodorovnou pøímku odshora dolù. V¾dy, kdy¾ narazíme na +nový prùnik, ohlásíme jeho výskyt. Samozøejmì spojité posouvání nahradíme diskrétním a pøímku v¾dy posuneme do dal¹ího zajímavého bodu. + +Zajímavé události jsou {\I zaèátky úseèek}, {\I konce úseèek} a {\I prùseèíky úseèek}. Po utøídìní známe pro první dva typy událostí poøadí, v jakém +se objeví. Výskyty prùseèíkù budeme poèítat prùbì¾nì, jinak bychom celý problém nemuseli øe¹it. + +V ka¾dém kroku si pamatujeme {\I prùøez} $P$ -- posloupnost úseèek aktuálnì protnutých zametací pøímkou. Tyto úseèky máme utøídìné zleva doprava. Navíc si +udr¾ujeme kalendáø $K$ budoucích událostí. Z hlediska prùseèíkù budeme na úseèky nahlí¾et jako na polopøímky. Pro sousední dvojice úseèek si +udr¾ujeme, zda se jejich smìry nìkde protnou. Algoritmus pro hledání prùnikù úseèek funguje následovnì: + +\s{Algoritmus:} + +\algo + +\:$P \leftarrow \emptyset$. +\:Do $K$ vlo¾íme zaèátky a konce v¹ech úseèek. +\:Dokud $K \ne \emptyset$: +\::Odebereme nejvy¹¹í událost. +\::Pokud je to zaèátek úseèky, zatøídíme novou úseèku do $P$. +\::Pokud je to konec úseèky, odebereme úseèku z $P$. +\::Pokud je to prùseèík, nahlásíme ho a prohodíme úseèky v $P$. +\::Navíc v¾dy pøepoèítáme prùseèíkové události, v¾dy maximálnì dvì odebereme a dvì nové pøidáme. +\endalgo + +Zbývá rozmyslet si, jaké datové struktury pou¾ijeme, abychom prùseèíky nalezli dostateènì rychle. Pro kalendáø pou¾ijeme napøíklad haldu. Prùøez si +budeme udr¾ovat ve vyhledávacím stromì. Poznamenejme, ¾e nemusíme znát souøadnice úseèek, staèí znát jejich poøadí, které se mezi jednotlivými +událostmi nemìní. Pøi pøidávání úseèek procházíme stromem a porovnáváme souøadnice v prùøezu, které prùbì¾nì dopoèítáváme. + +Kalendáø obsahuje v¾dy nejvý¹e $\O(n)$ událostí. Podobnì prùøez obsahuje v ka¾dém okam¾iku nejvý¹e $\O(n)$ úseèek. Jednu událost kalendáøe doká¾eme +o¹etøit v èase $\O(\log n)$. V¹ech událostí je $\O(n+p)$, a tedy celková slo¾itost algoritmu je $\O((n+p) \log n)$. + +Slíbili jsme, ¾e popí¹eme, jak se vypoøádat s vý¹e uvedenými podmínkami na vstup. Události kalendáøe se stejnou $y$-ovou souøadnicí budeme tøídit v +poøadí zaèátky, prùseèíky a konce úseèek. Tím nahlásíme i prùseèíky krajù úseèek a ani vodorovné úseèky nebudou vadit. Podobnì se není tøeba obávat +prùseèíkù více úseèek v jednom bodì. Úseèky jdoucí stejným smìrem, jejich¾ prùnik je úseèka, jsou komplikovanìj¹í, ale lze jejich prùseèíky o¹etøit a +vypsat tøeba souøadnice úseèky tvoøící jejich prùnik. + +Na závìr poznamenejme, ¾e Balaban vymyslel efektivnìj¹í algoritmus, který funguje v èase $\O(n \log n + p)$, ale je podstatnì komplikovanìj¹í. + +\h{Hledání nejbli¾¹ích bodù a Voroného diagramy} + +Nyní se pokusíme vyøe¹it i problém druhé strany -- pomù¾eme medvìdùm nalézt Eskymáky. + +{\I Eskymáci tráví vìt¹inu èasu doma, ve svém iglù. Takový medvìd je na své toulce zasnì¾enou krajinou, kdy¾ tu se najednou rozhodne nav¹tívit nìjakého +Eskymáka. Proto se podívá do své medvìdí mapy a nalezne nejbli¾¹í iglù. Má to ale jeden háèek, iglù jsou spousty a medvìd by dávno usnul, ne¾ by +nejbli¾¹í objevil.}\foot{Zlí jazykové by øekli, ¾e medvìdi jsou moc líní a nebo v mapách ani èíst neumí!} + +Popí¹eme si nejprve, jak vypadá medvìdí mapa. Medvìdí mapa obsahuje celou Arktidu a jsou v ní vyznaèena v¹echna iglù. Navíc obsahuje vyznaèené +oblasti tvoøené body, které jsou nejblí¾e k jednomu danému iglù. Takovému schématu se øíká {\I Voroného diagram}. Ten pro zadané body $x_1, \ldots, x_n$ +obsahuje rozdìlení roviny na oblasti $B_1, \ldots, B_n$, kde $B_i$ je mno¾ina bodù, které jsou blí¾e k $x_i$ ne¾ k ostatním bodùm $x_j$. Formálnì jsou +tyto oblasti definovány následovnì: +$$B_i = \left\{y \in {\bb R}^2\ \vert\ \forall j:\rho(x_i,y) \le \rho(x_j,y)\right\},$$ +kde $\rho(x,y)$ znaèí vzdálenost bodù $x$ a $y$. + +Uká¾eme si, ¾e Voroného diagram má pøekvapivì jednoduchou strukturu. Nejprve uva¾me, jak budou vypadat oblasti $B_a$ a $B_b$ pouze pro dva body +$a$ a $b$, jak je naznaèeno na obrázku. V¹echny body stejnì vzdálené od $a$ i $b$ le¾í na pøímce $p$ -- ose úseèky $ab$. Oblasti $B_a$ a $B_b$ +jsou tedy tvoøeny polorovinami ohranièenými osou $p$. Tedy obecnì tvoøí mno¾ina v¹ech bodù bli¾¹ích k $x_i$ ne¾ k $x_j$ nìjakou polorovinu. Oblast +$B_i$ obsahuje v¹echny body, které jsou souèasnì bli¾¹í k $x_i$ ne¾ ke v¹em ostatním bodùm $x_j$ -- tedy le¾í ve v¹ech polorovinách souèasnì. +Ka¾dá z oblastí $B_i$ je tvoøena prùnikem $n-1$ polorovin, tedy je to (mo¾ná neomezený) mnohoúhelník.\foot{Sly¹eli jste u¾ o lineárním programování? +Jak název vùbec nenapoví, {\I lineární programování} je teorii zabývající se øe¹ením a vlastnostmi soustav lineárních nerovnic. Lineární program je +popsaný lineární funkcí, kterou chceme maximalizovat za podmínek popsaných soustavou lineárních nerovnic. Ka¾dá nerovnice urèuje poloprostor, ve +kterém se pøípustná øe¹ení nachází. Proto¾e pøípustné øe¹ení splòuje v¹echny nerovnice zároveò, je mno¾ina v¹ech pøípustných øe¹ení (mo¾ná neomezený) +mnohostìn, obecnì ve veliké dimenzi ${\bb R}^d$, kde $d$ je poèet promìnných. Mno¾iny $B_i$ lze snadno popsat jako mno¾iny v¹ech pøípustných øe¹ení +lineárních programù pomocí vý¹e ukázaných polorovin. Na závìr poznamenejme, ¾e dlouho otevøená otázka, zda lze nalézt optimální øe¹ení lineárního +programu v polynomiálním èase, byla pozitivnì vyøe¹ena -- je znám polynomiální algoritmus, kterému se øíká {\I metoda vnitøního bodu}. Na druhou +stranu, pokud chceme najít pøípustné celoèíselné øe¹ení, je úloha NP-úplná a je jednoduché na ni pøevést spoustu optimalizaèních problémù. Dokázat +NP-tì¾kost není pøíli¹ tì¾ké. Na druhou stranu ukázat, ¾e tento problém le¾í v NP, není vùbec jednoduché.} +Pøíklad Voroného diagramu je naznaèen na obrázku. Zadané body jsou oznaèeny prázdnými krou¾ky a hranice oblastí $B_i$ jsou vyznaèené èernými èárami. + +\twofigures{8-geom2_2_polorovina.eps}{Body bli¾¹í k $a$ ne¾ $b$.}{1.25in}{8-geom2_3_voroneho_diagram.eps}{Voroného diagram.}{2.5in} + +Není náhoda, pokud vám hranice oblastí pøipomíná rovinný graf. Jeho vrcholy jsou body, které jsou stejnì vzdálené od alespoò tøí zadaných bodù. Jeho +stìny jsou oblasti $B_i$. Jeho hrany jsou tvoøeny èástí hranice mezi dvìma oblastmi -- body, které mají dvì oblasti spoleèné. Obecnì prùnik dvou +oblastí mù¾e být, v závislosti na jejich sousedìní, prázdný, bod, úseèka, polopøímka nebo dokonce celá pøímka. V dal¹ím textu si pøedstavme, ¾e celý +Voroného diagram uzavøeme do dostateènì velkého obdélníka,\foot{Pøeci jenom i celá Arktida je omezenì velká.} èím¾ dostaneme omezený rovinný graf. + +Poznamenejme, ¾e pøeru¹ované èáry tvoøí hrany duálního rovinného grafu s vrcholy v zadaných bodech. Hrany spojují sousední body na kru¾nicích, které +obsahují alespoò tøi ze zadaných bodù. Napøíklad na obrázku dostáváme skoro samé trojúhelníky, proto¾e vìt¹ina kru¾nic obsahuje pøesnì tøi zadané +body. Av¹ak nalezneme i jeden ètyøúhelník, jeho¾ vrcholy le¾í na jedné kru¾nici. + +Zkusíme nyní odhadnout, jak velký je rovinný graf popisující Voroného diagram. Podle slavné Eulerovy formule má ka¾dý rovinný graf nejvý¹e lineárnì +mnoho vrcholù, hran a stìn -- pro $v$ vrcholù, $e$ hran a $f$ stìn je $e \le 3v-6$ a navíc $v+f = e+2$. Tedy slo¾itost diagramu je lineární vzhledem k +poètu zadaných bodù $n=f$, $\O(n)$. Navíc Voroného diagram lze zkonstruovat v èase $\O(n \log n)$, napøíklad pomocí zametání roviny nebo metodou +rozdìl a panuj. Tím se v¹ak zabývat nebudeme,\foot{Pro zvídavé, kteøí nemají zkou¹ku druhý den ráno: Detaily naleznete v zápiscích z pøedloòského +ADSka.} místo toho si uká¾eme, jak v ji¾ spoèteném Voroného diagramu rychle hledat nejbli¾¹í body. + +\h{Lokalizace bodu uvnitø mnohoúhelníkové sítì} + +Problém medvìdù je najít v medvìdí mapì co nejrychleji nejbli¾¹í iglù. Máme v rovinì sí» tvoøenou mnohoúhelníky. Chceme pro jednotlivé body rychle +rozhodovat, do kterého mnohoúhelníku patøí. Na¹e øe¹ení budeme optimalizovat pro jeden pevný rozklad a obrovské mno¾ství rùzných dotazù, které chceme +co nejrychleji zodpovìdìt.\foot{Pøedstavujme si to tøeba tak, ¾e medvìdùm zprovozníme server. Ten jednou schroustá celou mapu a potom co nejrychleji +odpovídá na jejich dotazy. Medvìdi tak nemusí v mapách nic hledat, staèí se pøipojit na server a poèkat na odpovìï.} Nejprve pøedzpracujeme zadané +mnohoúhelníky a vytvoøíme strukturu, která nám umo¾ní rychlé dotazy na jednotlivé body. + +Uka¾me si pro zaèátek øe¹ení bez pøedzpracování. Rovinu budeme zametat pøímkou shora dolù. Podobnì jako pøi hledání prùseèíkù úseèek, udr¾ujeme si prùøez +pøímkou. V¹imnìte si, ¾e tento prùøez se mìní jenom ve vrcholech mnohoúhelníkù. Ve chvíli, kdy narazíme na hledaný bod, podíváme se, do kterého +intervalu v prùøezu patøí. To nám dá mnohoúhelník, který nahlásíme. Prùøez budeme uchovávat ve vyhledávacím stromì. Takové øe¹ení má slo¾itost $\O(n +\log n)$ na dotaz, co¾ je hroznì pomalé. + +Pøedzpracování bude fungovat následovnì. Jak je naznaèeno na obrázku pøeru¹ovanými èárami, rozøe¾eme si celou rovinu na pásy, bìhem kterých se prùøez +pøímkou nemìní. Pro ka¾dý z nich si pamatujeme stav stromu popisující, jak vypadal prùøez pøi procházení tímto pásem. Kdy¾ chceme lokalizovat nìjaký bod, +nejprve pùlením nalezneme pás, ve kterém se nachází. Poté polo¾íme dotaz na pøíslu¹ný strom. Strom procházíme a po cestì si dopoèítáme souøadnice +prùøezu, a¾ lokalizujeme správný interval v prùøezu. Dotaz doká¾eme zodpovìdìt v èase $\O(\log n)$. Hledaný bod je na obrázku naznaèen prázdným +koleèkem a nalezený interval v prùøezu je vyta¾ený tuènì. + +\figure{8-geom2_4_pasy_mnohouhelniku.eps}{Mnohoúhelníky rozøezané na pásy.}{2.5in} + +Jenom¾e na¹e øe¹ení má jeden háèek: Jak zkonstruovat jednotlivé verze stromu dostateènì rychle? K tomu napomohou {\I èásteènì perzistentní} datové +struktury. Pod perzistencí se myslí, ¾e struktura umo¾òuje uchovávat svoji historii. Èásteènì perzistentní struktury nemohou svoji historii +modifikovat. + +Popí¹eme si, jak vytvoøit perzistentní strom s pamìtí $\O(\log n)$ na zmìnu. Pokud provádíme operaci na stromì, mìní se jenom malá èást stromu. +Napøíklad pøi vkládání do stromu se mìní jenom prvky na jedné cestièce z koøene do listu (a pøípadnì rotací i na jejím nejbli¾¹ím okolí). Proto si +ulo¾íme upravenou cestièku a zbytek stromu budeme sdílet s pøedchozí verzí. Na obrázku je vyznaèena cesta, její¾ vrcholy jsou upravovány. ©edì +oznaèené podstromy navì¹ené na tuto cestu se nemìní, a proto na nì staèí zkopírovat ukazatele. Mimochodem zmìny ka¾dé operace se slo¾itostí $\O(k)$ +lze zapsat v pamìti $\O(k)$, prostì operace nemá tolik èasu, aby mohla pozmìnit pøíli¹ velikou èást stromu. + +\figure{8-geom2_5_upravy_stromu.eps}{Jedna operace mìní pouze okolí cesty -- navì¹ené podstromy se nemìní.}{2in} + +Celková èasová slo¾itost je tedy $\O(n \log n)$ na pøedzpracování Voroného diagramu a vytvoøení persistentního stromu. Kvùli persistenci potøebuje +toto pøedzpracování pamì» $\O(n \log n)$. Na dotaz spotøebujeme èas $\O(\log n)$, nebo» nejprve vyhledáme pùlením pøíslu¹ný pás a poté polo¾íme dotaz +na pøíslu¹nou verzi stromu. Rychleji to ani provést nepùjde, nebo» potøebujeme utøídit souøadnice bodù. + +\s{Lze to lépe?} Na závìr poznamenejme, ¾e se umí provést vý¹e popsaná persistence vyhledávacího stromu v amortizované pamìti $\O(1)$ na zmìnu. Ve +struènosti naznaèíme my¹lenku. Pou¾ijeme stromy, které pøi insertu a deletu provádí amortizovanì jenom konstantnì mnoho úprav své struktury. To nám +napøíklad zaruèí 2-4 stromy z pøedná¹ky a podobnou vlastnost lze dokázat i o èerveno-èerných stromech. Pøi zmìnì potom nebudeme upravovat celou cestu, +ale upravíme jenom jednotlivé vrcholy, kterých se zmìna týká. Ka¾dý vrchol stromu si v sobì bude pamatovat a¾ dvì své verze. Pokud chceme vytvoøit +tøetí verzi, vrchol zkopírujeme stranou. To v¹ak mù¾e vyvolat zmìny v jeho rodièích a¾ do koøene. Situace je naznaèena na obrázku. Pøi vytvoøení nové +verze $3$ pro vrcholu $v$ vytvoøíme jeho kopii $v'$, do které ulo¾íme tuto verzi. Av¹ak musíme také zmìnit rodièe $u$, kterému vytvoøíme novou verzi +ukazující na $v'$. Abychom dosáhli ký¾ené konstantní pamì»ové slo¾itosti, pomù¾e potenciálový argument -- zmìn se provádí amortizovanì jenom +konstantnì mnoho. Navíc si pro ka¾dou verzi pamatujeme její koøen, ze kterého máme dotaz spustit. + +\figure{8-geom2_6_rychla_perzistence.eps}{Vytvoøení nové verze vrcholu.}{2in} + +\bye diff --git a/old/8-geom2/8-geom2_0_bear.eps b/old/8-geom2/8-geom2_0_bear.eps new file mode 100644 index 0000000..9e6ec74 --- /dev/null +++ b/old/8-geom2/8-geom2_0_bear.eps @@ -0,0 +1,391 @@ +%!PS-Adobe-3.0 EPSF-3.0 +%%Creator: Adobe Illustrator by AutoTrace version 0.31.1 +%%Title: /tmp/potraceguiTmp-r4ayx1 +%%CreationDate: Tue Dec 15 14:58:41 2009 +%%BoundingBox: 0 0 437 436 +%%DocumentData: Clean7Bit +%%EndComments +%%BeginProlog +/bd { bind def } bind def +/incompound false def +/m { moveto } bd +/l { lineto } bd +/c { curveto } bd +/F { incompound not {fill} if } bd +/f { closepath F } bd +/S { stroke } bd +/*u { /incompound true def } bd +/*U { /incompound false def f} bd +/k { setcmykcolor } bd +/K { k } bd +%%EndProlog +%%BeginSetup +%%EndSetup +0.000 0.000 0.000 0.000 k +*u +0.889 435 m +0 428 l +0 411 l +0 341 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--- /dev/null +++ b/old/8-geom2/lib.mp @@ -0,0 +1,84 @@ +% implementation of figure naming and figure transparency +string name,tag; name := ""; tag := ""; +def updatefigname = + if tag="": filenametemplate (name & "%c.eps"); + else: filenametemplate (name & "%c_" & tag & ".eps"); + fi; +enddef; + +def figname(expr n) = name := n; updatefigname; enddef; +def figtag(expr t) = tag := t; updatefigname; enddef; + +picture transparent_picture; +color transparent_color; transparent_color := 0.9white; +def drawtransparent(expr num) = + transparent_picture := currentpicture; +endfig; + +if tag="": filenametemplate (name & "%c_transparent.eps"); +else: filenametemplate (name & "%c_" & tag & "_transparent.eps"); +fi; +beginfig(num); + draw transparent_picture withcolor transparent_color; +endfig; + +updatefigname; +enddef; + +def from(expr p,d,len) = + p+dir(d)*len +enddef; + +def dirs(expr p,d,len) = + p--from(p,d,len) +enddef; + +def drawvertices(expr s,n) = + for i:=s upto n: + draw vertex(PQ[i]); + endfor +enddef; + +def drawfvertices(expr s,n,flags) = + for i:=s upto n: + draw vertex(PQ[i]) flags; + endfor +enddef; + +path centersquare; centersquare := (-0.5,-0.5)--(0.5,-0.5)--(0.5,0.5)--(-0.5,0.5)--cycle; + +def vertex(expr p) = p withpen pencircle scaled 4pt enddef; +def drawemptyvertex(expr p) = unfill fullcircle scaled 4pt shifted p; draw fullcircle scaled 4pt shifted p; enddef; +def drawendpointvertex(expr p) = draw vertex(p) withcolor red; draw fullcircle scaled 6pt shifted p; enddef; +def createpath(expr p) = shakepath(p, 0.015cm,0.1cm) enddef; +vardef shakepath(expr p,d,l) = + save r,b; + path r; r := point(arctime 0 of p) of p; + b := -1; + for i:=l step l until arclength(p): + r := r--(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*d*b; + b := -b; + endfor + r--point(arctime arclength(p) of p) of p +enddef; + +pen normalpen; normalpen := pencircle scaled 0.6pt; +pen boldpen; boldpen := pencircle scaled 1.5pt; +pen bolderpen; bolderpen := pencircle scaled 2pt; +def dotline = withdots scaled 0.82 withpen boldpen enddef; + +vardef unclosedbubblec(expr p,c) = + bubblec((p..reverse p..cycle),c) +enddef; + +vardef bubblec(expr p,c) = + save r; + path r; r := (point(arctime 0 of p) of p)+dir(angle(direction(arctime 0 of p) of p rotated 90))*c; + for i:=0.01cm step 0.025cm until arclength(p): + r := r..(point(arctime i of p) of p)+dir(angle(direction(arctime i of p) of p rotated 90))*c; + endfor + r..(point(arctime arclength(p) of p) of p)+dir(angle(direction(arctime arclength(p) of p) of p rotated 90))*c..cycle +enddef; + +vardef bubble(expr p) = bubblec(p,0.12cm) enddef; +vardef unclosedbubble(expr p) = unclosedbubblec(p,0.12cm) enddef; diff --git a/old/9-fft/9-fft.tex b/old/9-fft/9-fft.tex new file mode 100644 index 0000000..3340759 --- /dev/null +++ b/old/9-fft/9-fft.tex @@ -0,0 +1,392 @@ +\def\scharfs{\char"19} +\input lecnotes.tex +\def\imply{\Rightarrow} +\prednaska{9}{Fourierova transformace}{\vbox{\hbox{(K.Jakubec, M.Polák + a~G.Ocsovszky,}\hbox{\ V.Tùma, M.Kozák)}}} + +Násobení polynomù mù¾e mnohým pøipadat jako pomìrnì (algoritmicky) snadný +problém. Asi ka¾dého hned napadne \uv{hloupý} algoritmus -- vezmeme +koeficienty prvního polynomu a~vynásobíme ka¾dý se v¹emi koeficienty druhého +polynomu a~pøíslu¹nì u~toho seèteme i~exponenty (stejnì jako to dìláme, kdy¾ +násobíme polynomy na~papíøe). Pokud stupeò prvního polynomu je $n$ a~druhého +$m$, strávíme tím èas $\Omega(mn)$. Pro $m=n$ je to kvadraticky pomalé. +Na~první pohled se mù¾e zdát, ¾e rychleji to prostì nejde (pøeci musíme +v¾dy vynásobit \uv{ka¾dý s~ka¾dým}). Ve skuteènosti to ale rychleji fungovat +mù¾e, ale k~tomu je potøeba znát trochu tajemný algoritmus FFT neboli {\I Fast +Fourier Transform}. + + +\ss{Trochu algebry na~zaèátek} +Celé polynomy oznaèujeme velkými písmeny, jednotlivé èleny polynomù pøíslu¹nými +malými písmeny (pø.: polynom $W$ stupnì $d$ má koeficienty $w_{0}, w_{1}, +w_{2},\ldots, w_{d}$). + +Libovolný polynom $P$ stupnì (nejvý¹e) $d$ lze reprezentovat +jednak jeho koeficienty, tedy èísly $p_{0}, p_{1}, \ldots ,p_{d}$, druhak +i~pomocí hodnot: + +\>{\bf Lemma:} Polynom stupnì nejvý¹e $d$ je jednoznaènì urèeò svými +hodnotami v~$d+1$ rùzných bodech. + +\>{\it Dùkaz:} +Polynom stupnì $d$ má maximálnì $d$ koøenù (indukcí -- je-li +$k$ koøenem $P$, pak lze $P$ napsat jako $(x-k)Q$ kde $Q$ je polynom stupnì +o~jedna men¹í, pøitom polynom stupnì 1 má jediný koøen); uvá¾íme-li +dva rùzné polynomy $P$ a~$Q$ stupnì $d$ nabývající v~daných bodech stejných +hodnot, tak $P-Q$ je polynom stupnì maximálnì $d$, ka¾dé +z $x_{0}\ldots x_{d}$ je koøenem tohoto polynomu $\imply$ spor, polynom stupnì +$d$ má $d+1$ koøenù $\imply$ $P-Q$ musí být nulový polynom $\imply$ $P=Q$. +\qed +\medskip + +Pov¹imnìme si jedné skuteènosti -- máme-li dva polynomy $A$ a~$B$ stupnì $d$ +a~body $x_{0}, \ldots, x_{n}$, dále polynom $C=A \cdot B$ (stupnì $2d$), pak +platí $C(x_{j}) = A(x_{j}) \cdot B(x_{j})$ pro $j = 0,1,2, \ldots, n$. Toto +èiní tento druhý zpùsob reprezentace polynomu velice atraktivním pro násobení +-- máme-li $A$ i $B$ reprezentované hodnotami v $n \geq 2d+1$ bodech, pak +snadno (v $\Theta(n)$) spoèteme takovou reprezentaci $C$. +Problémem je, ¾e typicky máme polynom zadaný koeficienty, a~ne hodnotami +v~bodech. Tím pádem potøebujeme nìjaký hodnì rychlý algoritmus (tj. +rychlej¹í ne¾ kvadratický, jinak bychom si nepomohli oproti hloupému +algoritmu) na~pøevod polynomu z jedné reprezentace do druhé a~zase zpìt. + +\s{Idea, jak by mìl algoritmus pracovat:} +\algo +\:Vybereme $n\geq 2d+1$ bodù $x_{0}, x_{1}, \ldots , x_{n-1}$. +\:V tìchto bodech vyhodnotíme polynomy $A$ a~$B$. +\:Nyní ji¾ v~lineárním èase získáme hodnoty polynomu $C$ v~tìchto bodech: + $C(x_i) = A(x_i)\cdot B(x_i)$ +\:Pøevedeme hodnoty polynomu $C$ na~jeho koeficienty. +\endalgo + +\>Je vidìt, ¾e klíèové jsou kroky 2 a~4. +Celý trik spoèívá v~chytrém vybrání onìch bodù, ve kterých budeme polynomy +vyhodnocovat -- zvolí-li se obecná $x_j$, tak se to rychle neumí, pro speciální +$x_j$ ale uká¾eme, ¾e to rychle jde. + +\ss{Vyhodnocení polynomu metodou Rozdìl a~panuj (algoritmus FFT):} +Mìjme polynom $P$ stupnì $\leq d$ a~chtìjme jej vyhodnotit v~$n$ bodech. +Vybereme si body tak, aby byly spárované, èili $\pm x_{0}, \pm x_{1}, +\ldots , \pm x_{n/2-1} $. To nám výpoèet urychlí, proto¾e pak se druhé +mocniny $x_{j}$ shodují s~druhými mocninami $-x_{j}$. + +Polynom $P$ rozlo¾íme na~dvì èásti, první obsahuje èleny se sudými exponenty, +druhá s~lichými: +$$P(x) = (p_{0}x^{0} + p_{2}x^{2} + \ldots + p_{d-2}x^{d-2}) + (p_{1}x^{1} + + p_{3}x^{3} + \ldots + p_{d-1}x^{d-1})$$ +\>se zavedením znaèení: +$$P_s(t) = p_0t^0 + p_{2}t^{1} + \ldots + p_{d - 2}t^{d - 2\over 2}$$ +$$P_l(t) = p_1t^0 + p_3t^1 + \ldots + p_{d - 1}t^{d - 2\over 2}$$ + +\>bude $P(x) = P_s(x^{2}) + xP_l(x^{2})$ a~$P(-x) = P_s(x^{2}) - +xP_l(x^{2})$. Jinak øeèeno, vyhodnocování polynomu $P$ v~$n$ bodech se nám +smrskne na~vyhodnocení $P_s$ a~$P_l$ v~$n/2$ bodech -- oba jsou polynomy +stupnì nejvý¹e $d/2$ a~vyhodnocujeme je v~$x^{2}$ (vyu¾íváme +rovnosti $(x_{i})^{2} = (-x_{i})^{2}$). + +\s{Pøíklad:} +$3 + 4x + 6x^{2} + 2x^{3} + x^{4} + 10x^{5} = (3 + 6x^{2} + x^{4}) + x(4 + +2x^{2} + 10x^{4})$. + +Teï nám ov¹em vyvstane problém s~oním párováním -- druhá mocnina pøece nemù¾e +být záporná a~tím pádem u¾ v~druhé úrovni rekurze body spárované nebudou. +Z~tohoto dùvodu musíme pou¾ít komplexní èísla -- tam druhé mocniny záporné býti +mohou. + +% komplex +\h{Komplexní intermezzo} +\def\i{{\rm i}} +\def\\{\hfil\break} + +\s{Základní operace} + +\itemize\ibull +\:Definice: ${\bb C} = \{a + b\i \mid a,b \in {\bb R}\}$ + +\:Sèítání: $(a+b\i)\pm(p+q\i) = (a\pm p) + (b\pm q)\i$. \\ +Pro $\alpha\in{\bb R}$ je $\alpha(a+b\i) = \alpha a + \alpha b\i$. + +\:Komplexní sdru¾ení: $\overline{a+b\i} = a-b\i$. \\ +$\overline{\overline x} = x$, $\overline{x\pm y} = \overline{x} \pm +\overline{y}$, $\overline{x\cdot y} = \overline x \cdot \overline y$, $x +\cdot \overline x \in {\bb R}$. + +\:Absolutní hodnota: $\vert x \vert = \sqrt{x\cdot\overline{x}}$, tak¾e $\vert +a+b\i \vert = \sqrt{a^2+b^2}$. \\ +Také $\vert \alpha x \vert = \vert \alpha\vert \cdot \vert x \vert$. + +\:Dìlení: $x/y = (x\cdot \overline{y}) / (y \cdot \overline{y})$. +\endlist + +\s{Gau{\scharfs}ova rovina a goniometrický tvar} + +\itemize\ibull +\:Komplexním èíslùm pøiøadíme body v~${\bb R}^2$: $a+b\i \leftrightarrow (a,b)$. + +\:$\vert x\vert$ je vzdálenost od~bodu $(0,0)$. + +\:$\vert x\vert = 1$ pro èísla le¾ící na~jednotkové kru¾nici ({\I komplexní jednotky}). \\ +Pak platí $x=\cos\varphi + \i\sin\varphi$ pro nìjaké $\varphi\in\left[ +0,2\pi \right)$. + +\:Pro libovolné $x\in{\bb C}$: $x=\vert x \vert \cdot (\cos\varphi(x) + +\i\sin\varphi(x))$. \\ +Èíslu $\varphi(x)\in\left[ 0,2\pi \right)$ øíkáme {\I argument} +èísla~$x$, nìkdy znaèíme $\mathop{\rm arg} x$. + +\:Navíc $\varphi({\overline{x}}) = -\varphi(x)$. +\endlist + +\s{Exponenciální tvar} + +\itemize\ibull +\:Eulerova formule: $e^{i\varphi} = \cos\varphi + \i\sin\varphi$. + +\:Ka¾dé $x\in{\bb C}$ lze tedy zapsat jako $\vert x\vert \cdot e^{\i\cdot +\varphi(x)}$. + +\:Násobení: $xy = \left(\vert x\vert\cdot e^{\i\cdot\varphi(x)}\right) \cdot + \left(\vert y\vert\cdot e^{\i\cdot\varphi(y)}\right) = \vert x\vert + \cdot \vert y\vert \cdot e^{\i\cdot(\varphi(x) + \varphi(y))}$. \\ +(absolutní hodnoty se násobí, argumenty sèítají) + +\:Umocòování: $x^\alpha = \left(\vert x\vert\cdot e^{\i\cdot\varphi(x)}\right)^ + \alpha = {\vert x\vert}^\alpha\cdot e^{\i \alpha \varphi(x)}$. + +\:Odmocòování: $\root n\of x = {\vert x\vert}^{1/n} \cdot e^{\i\cdot +\varphi(x)/n}$. \\ +Pozor -- odmocnina není jednoznaèná: $1^4=(-1)^4=\i^4=(-\i)^4=1$. +\endlist + +\s{Odmocniny z~jednièky} + +\itemize\ibull +\:Je-li nìjaké $x\in{\bb C}$ $n$-tou odmocninou z~jednièky, musí platit: +$\vert x \vert = 1$, tak¾e $x=e^{\i\varphi}$ pro nìjaké~$\varphi$. +Proto $x^n = e^{\i\varphi n} = \cos{\varphi n} + \i\sin\varphi n = 1$. +Platí tedy $\varphi n = 2k\pi$ pro nìjaké $k\in{\bb Z}$. + +\:Z~toho plyne: $\varphi = 2k\pi/n$ \\ +(pro $k=0,\ldots,n-1$ dostáváme rùzné $n$-té odmocniny). + +\:Obecné odmocòování: $\root n \of x = {\vert x\vert}^{1/n} \cdot e^{\i\varphi + (x)/n} \cdot u$, kde $u=\root n\of 1$. + +\:Je-li $x$ odmocninou z 1, pak $\overline{x} = x^{-1}$ -- je toti¾ $1 = \vert +x\cdot \overline{x}\vert = x\cdot \overline{x}$. +\endlist + +\s{Primitivní odmocniny} + +\s{Definice:} $x$ je {\I primitivní} $k$-tá odmocnina z 1 $\equiv x^k=1 \land \forall j: 0Tuto definici splòují napøíklad èísla $\omega = e^{2\pi \i / k}$ a $\overline\omega = e^{-2\pi\i/k}$. +Platí toti¾, ¾e $\omega^j = e^{2\pi\i j/k}$, co¾ je rovno~1 právì tehdy, +je-li $j$ násobkem~$k$ (jednotlivé mocniny èísla~$\omega$ postupnì obíhají +jednotkovou kru¾nici). Analogicky pro~$\overline\omega$. + +\>Uka¾me si nìkolik pozorování fungujících pro libovolné èíslo~$\omega$, +které je primitivní $k$-tou odmocninou z~jednièky (nìkdy budeme potøebovat, +aby navíc $k$ bylo sudé): + +\itemize\ibull +\:Pro $0\leq jFFT($P$, $ \omega$) + +\>{\sl Vstup:} $p_{0}, \ldots , p_{n-1}$, koeficienty polynomu $P$ stupnì +nejvý¹e $n-1$, a~$\omega$, +$n$-tá primitivní odmocina z jedné. + +\>{\sl Výstup:} Hodnoty polynomu v~bodech $1, \omega, \omega^{2}, \ldots , +\omega^{n - 1}$, èili èísla $P(1), P(\omega), P(\omega^{2}),$ $\ldots , +P(\omega^{n - 1})$. + +\algo +\:Pokud $n = 1$, vrátíme $p_{0}$ a~skonèíme. +\:Jinak rozdìlíme $P$ na èleny se sudými a lichými exponenty (jako v pùvodní +my¹lence) a~rekurzivnì zavoláme FFT($P_s$, $\omega^{2}$) a~FFT($P_l$, +$\omega^{2}$) -- $P_l$ i~$P_s$ jsou stupnì max. $n/2-1$, $\omega^2$ je +$n/2$-tá primitivní odmocnina, a mocniny $\omega^2$ jsou stále po dvou +spárované ($n$ je mocnina dvojky, a tedy i $n/2$ je sudé; popø. $n=2$ a je to +zøejmé). +\:Pro $j = 0, \ldots , n/2 - 1$ spoèítáme: + +\:\qquad $P(\omega^{j}) = P_s(\omega^{2j}) + \omega^{j}\cdot P_l(\omega^{2j})$. + +\:\qquad $P(\omega^{j+n/2})=P_s(\omega^{2j})-\omega^{j}\cdot P_l(\omega^{2j})$. + +\endalgo + + +\s{Èasová slo¾itost:} +\>$T(n)=2T(n/2) + \Theta(n) \Rightarrow$ slo¾itost $\Theta(n \log n)$, jako +MergeSort. + + +Máme tedy algoritmus, který pøevede koeficienty polynomu na~hodnoty tohoto +polynomu v~rùzných bodech. Potøebujeme ale také algoritmus, který doká¾e +reprezentaci polynomu pomocí hodnot pøevést zpìt na~koeficienty polynomu. +K~tomu nám pomù¾e podívat se na~ná¹ algoritmus trochu obecnìji. + + +\s{Definice:} +\>{\I Diskrétní Fourierova transformace} $(DFT)$ +je zobrazení $f: { {\bb C} ^n} \rightarrow { {\bb C} ^n}$, kde $$y=f(x) \equiv +\forall j \ y_{j} = \sum \limits ^{n-1}_{k=0} x_{k} \cdot \omega ^{jk}$$ +(DFT si lze mimo jiné pøedstavit jako funkci vyhodnocující polynom +s~koeficienty $x_k$ v~bodech $\omega^j$). Takovéto zobrazení je lineární +a~tedy popsatelné maticí $\Omega$ s~prvky $\Omega_{jk} = \omega^{jk}$. +Chceme-li umìt pøevádìt z~hodnot polynomu na koeficienty, zajímá nás inverze +této matice. + + +\ss{Jak najít inverzní matici?} +Znaème $\overline{\Omega}$ matici, její¾ prvky +jsou komplexnì sdru¾ené odpovídajícím prvkùm $\Omega$, a vyu¾ijme následující +lemma: + +\ss{Lemma:}$\quad \Omega\cdot \overline{\Omega} = n\cdot E$. + +\proof $$ (\Omega\cdot \overline{\Omega})_{jk} = \sum_{l=0}^{n-1} \omega^{jl} +\cdot \overline{\omega^{lk}} = \sum \omega^{jl} \cdot \overline{\omega}^{lk} = +\sum \omega^{jl} \cdot \omega^{-lk} = \sum \omega^{l(j-k)}\hbox{.}$$ +\itemize\ibull +\:Pokud $j=k$, pak $ \sum \limits ^{n-1}_{l=0} (\omega ^{0}) ^{l} = n$. + +\:Pokud $j\neq k$, pou¾ijeme vzoreèek pro souèet geometrické øady +s kvocientem $\omega ^{(j-k) }$ a~dostaneme ${{\omega^{(j-k)n} -1} \over +{\omega^{(j-k)} -1}} ={1-1 \over \neq 0} = 0$ ( +$\omega^{j-k} - 1$ je jistì $\neq 0$, nebo» $\omega$ je $n$-tá primitivní +odmoncina a $j-kNa¹li jsme inverzi: +$\Omega({1 \over n} \overline{\Omega}) = {1 \over n}\Omega \cdot +\overline{\Omega} = E$, \quad $\Omega^{-1}_{jk} = {1 \over n}\overline{\omega^ +{jk}} = {1 \over n}\omega^{-jk} = {1 \over n} {(\omega^{-1})}^{jk}$, \quad +(pøipomínáme, $\omega^{-1}$ je $\overline{\omega}$). + +Vyhodnocení polynomu lze provést vynásobením $\Omega$, pøevod do pùvodní +reprezentace vynásobením $\Omega^{-1}$. My jsme si ale v¹imli chytrého +spárování, a vyhodnocujeme polynom rychleji ne¾ kvadraticky (proto FFT, +jako¾e {\it fast}, ne jako {\it fuj}). Uvìdomíme-li si, ¾e $\overline \omega = +\omega^{-1}$ je také $n$-tá primitivní odmocnina z 1 (má akorát +úhel s opaèným znaménkem), tak mù¾eme stejným trikem vyhodnotit i~zpìtný +pøevod -- nejprve vyhodnotíme $A$ a $B$ v $\omega^j$, poté pronásobíme +hodnoty a~dostaneme tak hodnoty polynomu $C=A\cdot B$, a pustíme na nì +stejný algoritmus s~$\omega^{-1}$ (hodnoty $C$ +vlastnì budou v~algoritmu \uv{koeficienty polynomu}). Nakonec jen získané +hodnoty vydìlíme $n$ a~máme chtìné koeficienty. + +\s{Výsledek:} Pro $n= 2^k$ lze DFT na~${\bb C}^n$ spoèítat v~èase $\Theta(n +\log n)$ a~DFT$^{-1}$ takté¾. + +\s{Dùsledek:} +Polynomy stupnì $n$ lze násobit v~èase $\Theta(n \log n)$: +$\Theta(n \log n)$ pro vyhodnocení, $\Theta(n)$ pro vynásobení a~$\Theta(n +\log n)$ pro pøevedení zpìt. + +\s{Pou¾ití FFT:} + +\itemize\ibull + +\:Zpracování signálu -- rozklad na~siny a~cosiny o~rùzných frekvencích +$\Rightarrow$ spektrální rozklad. +\:Komprese dat -- napøíklad formát JPEG. +\:Násobení dlouhých èísel v~èase $\Theta(n \log n)$. +\endlist + +\s{Paralelní implementace FFT} + +\figure{img.eps}{Pøíklad prùbìhu algoritmu na~vstupu velikosti 8}{3in} + +Zkusme si prùbìh algoritmu FFT znázornit graficky (podobnì, jako jsme kreslili +hradlové sítì). Na~levé stranì obrázku se nachází vstupní vektor $x_0,\ldots,x_{n-1}$ +(v~nìjakém poøadí), na~pravé stranì pak výstupní vektor $y_0,\ldots,y_{n-1}$. +Sledujme chod algoritmu pozpátku: Výstup spoèítáme z~výsledkù \uv{polovièních} +transformací vektorù $x_0,x_2,\ldots,x_{n-2}$ a $x_1,x_3,\ldots,x_{n-1}$. +Èerné krou¾ky pøitom odpovídají výpoètu lineární kombinace $a+\omega^kb$, +kde $a,b$ jsou vstupy krou¾ku a $k$~nìjaké pøirozené èíslo závislé na poloze +krou¾ku. Ka¾dá z~polovièních transformací se poèítá analogicky z~výsledkù +transformace velikosti $n/4$ atd. Celkovì výpoèet probíhá v~$\log_2 n$ vrstvách +po~$\Theta(n)$ operacích. + +Jeliko¾ operace na~ka¾dé vrstvì probíhají na sobì nezávisle, mù¾eme je poèítat +paralelnì. Ná¹ diagram tedy popisuje hradlovou sí» pro paralelní výpoèet FFT +v~èase $\Theta(\log n\cdot T)$ a prostoru $\O(n\cdot S)$, kde $T$ a~$S$ znaèí +èasovou a prostorovou slo¾itost výpoètu lineární kombinace dvou komplexních èísel. + +\s{Cvièení:} Doka¾te, ¾e permutace vektoru $x_0,\ldots,x_{n-1}$ odpovídá bitovému +zrcadlení, tedy ¾e na pozici~$b$ shora se vyskytuje prvek $x_d$, kde~$d$ je +èíslo~$b$ zapsané ve~dvojkové soustavì pozpátku. + +\s{Nerekurzivní FFT} + +Obvod z~pøedchozího obrázku také mù¾eme vyhodnocovat po~hladinách zleva doprava, +èím¾ získáme elegantní nerekurzivní algoritmus pro výpoèet FFT v~èase $\Theta(n\log n)$ +a prostoru $\Theta(n)$: + +\algo +\algin $x_0,\ldots,x_{n-1}$ +\:Pro $j=0,\ldots,n-1$ polo¾íme $y_k\= x_{r(k)}$, kde $r$ je funkce bitového zrcadlení. +\:Pøedpoèítáme tabulku $\omega^0,\omega^1,\ldots,\omega^{n-1}$. +\:$b\= 1$ \cmt{velikost bloku} +\:Dokud $b