[saga.git] / ram.tex

\ifx\endpart\undefined
\input macros.tex
\fi

\chapter{Fine Details of Computation}
\id{ramchap}

\section{Models and machines}

Traditionally, computer scientists use a~variety of computational models
as a~formalism in which their algorithms are stated. If we were studying
NP-completeness, we could safely assume that all the models are equivalent,
possibly up to polynomial slowdown which is negligible. In our case, the
differences between good and not-so-good algorithms are on a~much smaller
scale. In this chapter, we will replace the usual ``yardsticks'' by a~micrometer,
state our computation models carefully and develop a repertoire of basic
data structures taking advantage of the fine details of the models.

We would like to keep the formalism close enough to the reality of the contemporary
computers. This rules out Turing machines and similar sequentially addressed
models, but even the remaining models are subtly different from each other. For example, some of them
allow indexing of arrays in constant time, while on the others,
arrays have to be emulated with pointer structures, requiring $\Omega(\log n)$
time to access a~single element of an~$n$-element array. It is hard to say which
way is superior --- while most ``real'' computers have instructions for constant-time
indexing, it seems to be physically impossible to fulfil this promise regardless of
the size of memory. Indeed, at the level of logical gates inside the computer,
the depth of the actual indexing circuits is logarithmic.

In recent decades, most researchers in the area of combinatorial algorithms
have been considering two computational models: the Random Access Machine and the Pointer
Machine. The former is closer to the programmer's view of a~real computer,
the latter is slightly more restricted and ``asymptotically safe.''
We will follow this practice and study our algorithms in both models.

\para
The \df{Random Access Machine (RAM)} is not a~single coherent model, but rather a~family
of closely related machines, sharing the following properties.
(See Cook and Reckhow \cite{cook:ram} for one of the usual formal definitions
and Hagerup \cite{hagerup:wordram} for a~thorough description of the differences
between the RAM variants.)

The \df{memory} of the machine is represented by an~array of \df{memory cells}
addressed by non-negative integers, each of them containing a~single non-negative integer.
The \df{program} is a~finite sequence of \df{instructions} of two basic kinds: calculation
instructions and control instructions.

\df{Calculation instructions} have two source arguments and one destination
argument, each \df{argument} being either an~immediate constant (not available
as destination), a~directly addressed memory cell (specified by its number)
or an~indirectly addressed memory cell (its address is stored in a~directly
addressed memory cell).

\df{Control instructions} include branches (to a~specific instruction in
the program), conditional branches (e.g., jump if two arguments specified as
in the calculation instructions are equal) and an~instruction to halt the program.

At the beginning of the computation, the memory contains the input data
in specified cells and arbitrary values in all other cells.
Then the program is executed one instruction at a~time. When it halts,
specified memory cells are interpreted as the program's output.

\para\id{wordsize}%
In the description of the RAM family, we have omitted several details
on~purpose, because different members of the family define them differently.
These are: the size of the available integers, the time complexity of a~single
instruction, the space complexity assigned to a~single memory cell and the set
of operations available in calculation instructions.

If we impose no limits on the magnitude of the numbers and we assume that
arithmetic and logical operations work on them in constant time, we get
a~very powerful parallel computer --- we can emulate an~exponential number
of parallel processors using arithmetics and suddenly almost everything can be
computed in constant time, modulo encoding and decoding of input and output.
Such models are unrealistic and there are two basic possibilities how to
avoid this behavior:

\numlist\ndotted
\:Keep unbounded numbers, but increase costs of instructions: each instruction
  consumes time proportional to the number of bits of the numbers it processes,
  including memory addresses. Similarly, space usage is measured in bits,
  counting not only the values, but also the addresses of the respective memory
  cells.
\:Place a~limit on the size of the numbers ---define the \df{word size~$W$,}
  the number of bits available in the memory cells--- and keep the cost of
  instructions and memory cells constant. The word size must not be constant,
  since we can address only~$2^W$ cells of memory. If the input of the algorithm
  is stored in~$N$ cells, we need~$W\ge\log N$ just to be able to read the input.
  On the other hand, we are interested in polynomial-time algorithms only, so $\Theta(\log N)$-bit
  numbers should be sufficient. In practice, we pick~$W$ to be the larger of
  $\Theta(\log N)$ and the size of integers used in the algorithm's input and output.
  We will call an integer which fits in a~single memory cell a~\df{machine word.}
\endlist

Both restrictions easily avoid the problems of unbounded parallelism. The first
choice is theoretically cleaner and Cook et al.~show nice correspondences to the
standard complexity classes, but the calculations of time and space complexity tend
to be somewhat tedious. What more, when compared with the RAM with restricted
word size, the complexities are usually exactly $\Theta(W)$ times higher.
This does not hold in general (consider a~program which uses many small numbers
and $\O(1)$ large ones), but it is true for the algorithms we are interested in.
Therefore we will always assume that the operations have unit cost and we make
sure that all numbers are limited by the available word size.

\para
As for the choice of RAM operations, the following three instruction sets are often used:

\itemize\ibull
\:\df{Word-RAM} --- allows the ``C-language operators'', i.e., addition,
  subtraction, multiplication, division, remainder, bitwise $\band$, $\bor$, exclusive
  $\bor$ ($\bxor$) and negation ($\bnot$), and bitwise shifts ($\shl$ and~$\shr$).
\:\df{${\rm AC}^0$-RAM} --- allows all operations from the class ${\rm AC}^0$, i.e.,
  those computable by constant-depth polynomial-size boolean circuits with unlimited
  fan-in and fan-out. This includes all operations of the Word-RAM except for multiplication,
  division and remainders, and also many other operations like computing the Hamming
  weight (number of bits set in a~given number).
\:Both restrictions at once.
\endlist

Thorup discusses the usual techniques employed by RAM algorithms in~\cite{thorup:aczero}
and he shows that they work on both Word-RAM and ${\rm AC}^0$-RAM, but the combination
of the two restrictions is too weak. On the other hand, the intersection of~${\rm AC}^0$
with the instruction set of modern processors is already strong enough (e.g., when we
add some floating-point operations and multimedia instructions available on the Intel's
Pentium~4~\cite{intel:pentium}).

We will therefore use the Word-RAM instruction set, mentioning differences from the
${\rm AC}^0$-RAM where necessary.

\nota
When speaking of the \df{RAM,} we implicitly mean the version with numbers limited
by a~specified word size of $W$~bits, unit cost of operations and memory cells and the instruction
set of the Word-RAM. This corresponds to the usage in recent algorithmic literature,
although the authors rarely mention the details. In some cases, a~non-uniform variant
of the Word-RAM is considered as well (e.g., in~\cite{hagerup:dd}):

\defn\id{nonuniform}%
A~Word-RAM is called \df{weakly non-uniform,} if it is equipped with $\O(1)$-time
access to a~constant number of word-sized constants, which depend only on the word
size. These are called \df{native constants} and they are available in fixed memory
cells when the program starts. (By analogy with the high-level programming languages,
these constants can be thought of as computed at ``compile time.'')

\para
The \df{Pointer Machine (PM)} also does not have any well established definition. The
various kinds of pointer machines are mapped by Ben-Amram in~\cite{benamram:pm},
but unlike the RAM's they turn out to be equivalent up to constant slowdown.
Our definition will be closely related to the \em{linking automaton} proposed
by Knuth in~\cite{knuth:fundalg}, we will only adapt it to use RAM-like
instructions instead of an~opaque control unit.

The PM works with two different types of data: \df{symbols} from a~finite alphabet
and \df{pointers}. The memory of the machine consists of a~fixed amount of \df{registers}
(some of them capable of storing a~single symbol, each of the others holds a~single pointer)
and an~arbitrary amount of \df{cells}. The structure of all cells is the same: each of them
again contains a~fixed number of fields for symbols and pointers. Registers can be addressed
directly, the cells only via pointers --- by using a~pointer stored either in a~register,
or in a~cell pointed to by a~register (longer chains of pointers cannot be followed in
constant time).

We can therefore view the whole memory as a~directed graph, whose vertices
correspond to the cells (the registers are stored in a~single special cell).
The outgoing edges of each vertex correspond to pointer fields of the cells and they are
labeled with distinct labels drawn from a~finite set. In addition to that,
each vertex contains a~fixed amount of symbols. The program can directly access
vertices within distance~2 from the register vertex.

The program is a~finite sequence of instructions of the following kinds:

\itemize\ibull
\:\df{symbol instructions,} which read a~pair of symbols, apply an~arbitrary
  function on them and write the result to a~symbol register or field;
\:\df{pointer instructions} for assignment of pointers to pointer registers/fields
  and for creation of new memory cells (a~pointer to the new cell is assigned
  immediately);
\:\df{control instructions} --- similarly to the RAM; conditional jumps can decide
  on~arbitrary unary relations on symbols and compare pointers for equality.
\endlist

Time and space complexity are defined in the straightforward way: all instructions
have unit cost and so do all memory cells.

Both input and output of the machine are passed in the form of a~linked structure
pointed to by a~designated register. For example, we can pass graphs back and forth
without having to encode them as strings of numbers or symbols. This is important,
because with the finite alphabet of the~PM, symbolic representations of graphs
generally require super-linear space and therefore also time.\foot{%
The usual representation of edges as pairs of vertex labels uses $\Theta(m\log n)$ bits
and as a~simple counting argument shows, this is asymptotically optimal for general
sparse graphs. On the other hand, specific families of sparse graphs can be stored
more efficiently, e.g., by a~remarkable result of Tur\'an~\cite{turan:succinct},
planar graphs can be encoded in~$\O(n)$ bits. Encoding of dense graphs is of
course trivial as the adjacency matrix has only~$\Theta(n^2)$ bits.}

\para
Compared to the RAM, the PM lacks two important capabilities: indexing of arrays
and arithmetic instructions. We can emulate both with poly-logarithmic slowdown,
but it will turn out that they are rarely needed in graph algorithms. We are
also going to prove that the RAM is strictly stronger, so we will prefer to
formulate our algorithms in the PM model and use RAM only when necessary.

\thm
Every program for the Word-RAM with word size~$W$ can be translated to a~PM program
computing the same with $\O(W^2)$ slowdown (given a~suitable encoding of inputs and
outputs, of course). If the RAM program does not use multiplication, division
and remainder operations, $\O(W)$~slowdown is sufficient.

\proofsketch
Represent the memory of the RAM by a~balanced binary search tree or by a~radix
trie of depth~$\O(W)$. Values are encoded as~linked lists of symbols pointed
to by the nodes of the tree. Both direct and indirect accesses to the memory
can therefore be done in~$\O(W)$ time. Use standard algorithms for arithmetic
on big numbers: $\O(W)$ per operation except for multiplication, division and
remainders which take $\O(W^2)$.\foot{We could use more efficient arithmetic
algorithms, but the quadratic bound is good enough for our purposes.}
\qed

\FIXME{Add references, especially to the unbounded parallelism remark.}

\thm
Every program for the PM running in polynomial time can be translated to a~program
computing the same on the Word-RAM with only $\O(1)$ slowdown.

\proofsketch
Encode each cell of the PM's memory to $\O(1)$ integers. Store the encoded cells to
the memory of the RAM sequentially and use memory addresses as pointers. As the symbols
are finite and there is only a~polynomial number of cells allocated during execution
of the program, $\O(\log N)$-bit integers suffice ($N$~is the size of the program's input).
\qed

\para
There are also \df{randomized} versions of both machines. These are equipped
with an~additional instruction for generating a~single random bit. The standard
techniques of design and analysis of randomized algorithms apply (see for
example Motwani and Raghavan~\cite{motwani:randalg}).

\FIXME{Consult sources. Does it make more sense to generate random words at once on the RAM?}

\rem
There is one more interesting machine: the \df{Immutable Pointer Machine} (see
the description of LISP machines in \cite{benamram:pm}). It differs from the
ordinary PM by the inability to modify existing memory cells. Only the contents
of the registers are allowed to change. All cell modifications thus have to
be performed by creating a~copy of the particular cell with some fields changed.
This in turn requires the pointers to the cell to be updated, possibly triggering
a~cascade of further cell copies. For example, when a~node of a~binary search tree is
updated, all nodes on the path from that node to the root have to be copied.

One of the advantages of this model is that the states of the machine are
persistent --- it is possible to return to a~previously visited state by recalling
the $\O(1)$ values of the registers (everything else could not have changed
since that time) and ``fork'' the computations. This corresponds to the semantics
of pure functional languages, e.g., Haskell~\cite{jones:haskell}.

Unless we are willing to accept a~logarithmic penalty in execution time and space
(in fact, our emulation of the Word-RAM on the PM can be easily made immutable),
the design of efficient algorithms for the immutable PM requires very different
techniques. Therefore, we will concentrate on the imperative models instead
and refer the interested reader to the thorough treatment of purely functional
data structures in the Okasaki's monograph~\cite{okasaki:funcds}.

%--------------------------------------------------------------------------------

\section{Bucket sorting and contractions}\id{bucketsort}%

The Contractive Bor\o{u}vka's algorithm (\ref{contbor}) needed to contract a~given
set of edges in the current graph and flatten it afterwards, all this in time $\O(m)$.
We have spared the technical details for this section and they will be useful
in further algorithms, too.

As already suggested, the contractions can be performed by building an~auxiliary
graph and finding its connected components. Thus we will take care of the flattening
only.

\para
On the RAM, it is sufficient to sort the edges lexicographically (each edge viewed
as an~ordered pair of vertex identifiers with the smaller of the identifiers placed
first). We can do that by a two-pass bucket sort with~$n$ buckets corresponding
to the vertex identifiers.

However, there is a~catch in this. Suppose that we use the standard representation
of graphs by adjacency lists whose heads are stored in an array indexed by vertex
identifiers. When we contract and flatten the graph, the number of vertices decreases,
but if we inherit the original vertex identifiers, the arrays will still have the
same size. Hence we spend a~super-linear amount of time on scanning the increasingly
sparse arrays, most of the time skipping unused entries.

To avoid this, we have to renumber the vertices after each contraction to component
identifiers from the auxiliary graph and we create a~new vertex array. This way,
the representation of the graph will be kept linear with respect to the size of the
current graph.

\para
The pointer representation of graphs does not suffer from sparsity as the vertices
are always identified by pointers to per-vertex structures. Each such structure
then contains all attributes associated with the vertex, including the head of its
adjacency list. However, we have to find a~way how to perform bucket sorting
without arrays.

We will keep a~list of the per-vertex structures which defines the order of~vertices.
Each such structure will contain a~pointer to the head of the corresponding bucket,
again stored as a~list. Putting an~edge to a~bucket can be done in constant time then,
scanning all~$n$ buckets takes $\O(n+m)$ time.

\FIXME{Add an example of locally determined orders, e.g., tree isomorphism?}

%--------------------------------------------------------------------------------

\section{Data structures on the RAM}

There is a~lot of data structures designed specifically for the RAM, taking
advantage of both indexing and arithmetics. In many cases, they surpass the known
lower bounds for the same problem on the~PM and they often achieve constant time
per operation, at least when either the magnitude of the values or the size of
the data structure are suitably bounded.

A~classical result of this type are the trees of van Emde Boas~\cite{boas:vebt},
which represent a~subset of the integers $\{0,\ldots,U-1\}$, allowing insertion,
deletion and order operations (minimum, maximum, successor etc.) in time $\O(\log\log U)$,
regardless of the size of the subset. If we replace the heap used in the Jarn\'\i{}k's
algorithm (\ref{jarnik}) by this structure, we immediately get an~algorithm
for finding the MST in integer-weighted graphs in time $\O(m\log\log w_{max})$,
where $w_{max}$ is the maximum weight. We will show later that it is even
possible to achieve linear time complexity for arbitrary integer weights.

A~real breakthrough has been made by Fredman and Willard, who introduced
the Fusion trees~\cite{fw:fusion} which again perform membership and predecessor
operation on a~set of $n$~integers, but this time with complexity $\O(\log_W n)$
per operation on a~Word-RAM with $W$-bit words. This of course assumes that
each element of the set fits in a~single word. As $W$ must at least~$\log n$,
the operations take $\O(\log n/\log\log n)$ and we are able to sort $n$~integers
in time~$o(n\log n)$. This was a~beginning of a~long sequence of faster and
faster sorting algorithms, culminating with the work by Thorup and Han.
They have improved the time complexity of integer sorting to $\O(n\log\log n)$ deterministically~\cite{han:detsort}
and expected $\O(n\sqrt{\log\log n})$ for randomized algorithms~\cite{hanthor:randsort},
both in linear space.

Despite the recent progress, the corner-stone of most RAM data structures
is still the representation of data structures by integers introduced by Fredman
and Willard. It will also form a~basis for the rest of this chapter.

\FIXME{Add more history.}

%--------------------------------------------------------------------------------

\section{Bits and vectors}\id{bitsect}

In this rather technical section, we will show how the RAM can be used as a~vector
computer to operate in parallel on multiple elements, as long as these elements
fit in a~single machine word. At the first sight this might seem useless, because we
cannot require large word sizes, but surprisingly often the elements are small
enough relative to the size of the algorithm's input and thus also relative to
the minimum possible word size. Also, as the following lemma shows, we can
easily emulate slightly longer words:

\lemman{Multiple-precision calculations}
Given a~RAM with $W$-bit words, we can emulate all calculation and control
instructions of a~RAM with word size $kW$ in time depending only on the~$k$.
(This is usually called \df{multiple-precision arithmetics.})

\proof
We split each word of the ``big'' machine to $W'$-bit blocks, where $W'=W/2$, and store these
blocks in $2k$ consecutive memory cells. Addition, subtraction, comparison and
bitwise logical operations can be performed block-by-block. Shifts by a~multiple
of~$W'$ are trivial, otherwise we can combine each block of the result from
shifted versions of two original blocks.
To multiply two numbers, we can use the elementary school algorithm using the $W'$-bit
blocks as digits in base $2^{W'}$ --- the product of any two blocks fits
in a~single word.

Division is harder, but Newton-Raphson iteration (see~\cite{ito:newrap})
converges to the quotient in a~constant number of iterations, each of them
involving $\O(1)$ multiple-precision additions and multiplications. A~good
starting approximation can be obtained by dividing the two most-significant
(non-zero) blocks of both numbers.

Another approach to division is using the improved elementary school algorithm as described
by Knuth in~\cite{knuth:seminalg}. It uses $\O(k^2)$ steps, but the steps involve
calculation of the most significant bit set in a~word. We will show below that it
can be done in constant time, but we have to be careful to avoid division instructions.
\qed

\notan{Bit strings}\id{bitnota}%
We will work with binary representations of natural numbers by strings over the
alphabet $\{\0,\1\}$: we will use $\(x)$ for the number~$x$ written in binary,
$\(x)_b$ for the same padded to exactly $b$ bits by adding leading zeroes,
and $x[k]$ for the value of the $k$-th bit of~$x$ (with a~numbering of bits such that $2^k[k]=1$).
The usual conventions for operations on strings will be utilized: When $s$
and~$t$ are strings, we write $st$ for their concatenation and
$s^k$ for the string~$s$ repeated $k$~times.
When the meaning is clear from the context,
we will use $x$ and $\(x)$ interchangeably to avoid outbreak of symbols.

\defn
The \df{bitwise encoding} of a~vector ${\bf x}=(x_0,\ldots,x_{d-1})$ of~$b$-bit numbers
is an~integer~$x$ such that $\(x)=\(x_{d-1})_b\0\(x_{d-2})_b\0\ldots\0\(x_0)_b$, i.e.,
$x = \sum_i 2^{(b+1)i}\cdot x_i$. (We have interspersed the elements with \df{separator bits.})

\notan{Vectors}\id{vecnota}%
We will use boldface letters for vectors and the same letters in normal type
for their encodings. The elements of a~vector~${\bf x}$ will be written as
$x_0,\ldots,x_{d-1}$.

\para
If we want to fit the whole vector in a~single word, the parameters $b$ and~$d$ must satisty
the condition $(b+1)d\le W$.
By using multiple-precision arithmetics, we can encode all vectors satisfying $bd=\O(W)$.
We will now describe how to translate simple vector manipulations to sequences of $\O(1)$ RAM operations
on their codes. As we are interested in asymptotic complexity only, we prefer clarity
of the algorithms over saving instructions. Among other things, we freely use calculations
on words of size $\O(bd)$, assuming that the Multiple-precision lemma comes to save us
when necessary.

\para
First of all, let us observe that we can use $\band$ and $\bor$ with suitable constants
to write zeroes or ones to an~arbitrary set of bit positions at once. These operations
are usually called \df{bit masking}. Also, any element of a~vector can be extracted or
replaced by a~different value in $\O(1)$ time by masking and shifts.

\newdimen\slotwd
\def\setslot#1{\setbox0=#1\slotwd=\wd0}
\def\slot#1{\hbox to \slotwd{\hfil #1\hfil}}
\def\[#1]{\slot{$#1$}}
\def\9{\rack{\0}{\hss$\cdot$\hss}}

\def\alik#1{%
\medskip
\halign{\hskip 0.15\hsize\hfil $ ##$&\hbox to 0.6\hsize{${}##$ \hss}\cr
#1}
\medskip
}

\algn{Operations on vectors with $d$~elements of $b$~bits each}\id{vecops}

\itemize\ibull

\:$\<Replicate>(\alpha)$ --- creates a~vector $(\alpha,\ldots,\alpha)$:

\alik{\<Replicate>(\alpha)=\alpha\cdot(\0^b\1)^d. \cr}

\:$\<Sum>(x)$ --- calculates the sum of the elements of~${\bf x}$, assuming that
the result fits in $b$~bits:

\alik{\<Sum>(x) = x \bmod \1^{b+1}. \cr}

This is correct because when we calculate modulo~$\1^{b+1}$, the number $2^{b+1}=\1\0^{b+1}$
is congruent to~1 and thus $x = \sum_i 2^{(b+1)i}\cdot x_i \equiv \sum_i 1^i\cdot x_i \equiv \sum_i x_i$.
As the result should fit in $b$~bits, the modulo makes no difference.

If we want to avoid division, we can use double-precision multiplication instead:

\setslot{\hbox{~$\0x_{d-1}$}}
\def\.{\[]}
\def\z{\[\0^b\1]}
\def\dd{\slot{$\cdots$}}
\def\vd{\slot{$\vdots$}}
\def\rule{\noalign{\medskip\nointerlineskip}$\hrulefill$\cr\noalign{\nointerlineskip\medskip}}

\alik{
\[\0x_{d-1}] \dd \[\0x_2] \[\0x_1] \[\0x_0] \cr
*~~ \z \dd \z\z\z \cr
\rule
\[x_{d-1}] \dd \[x_2] \[x_1] \[x_0] \cr
\[x_{d-1}] \[x_{d-2}] \dd \[x_1] \[x_0] \. \cr
\[x_{d-1}] \[x_{d-2}] \[x_{d-3}] \dd \[x_0] \. \. \cr
\vd\vd\vd\vd\.\.\.\cr
\[x_{d-1}] \dd \[x_2]\[x_1]\[x_0] \. \. \. \. \cr
\rule
\[r_{d-1}] \dd \[r_2] \[r_1] \[s_d] \dd \[s_3] \[s_2] \[s_1] \cr
}

This way, we even get the vector of all partial sums:
$s_k=\sum_{i=0}^{k-1}x_i$, $r_k=\sum_{i=k}^{d-1}x_i$.

\:$\<Cmp>(x,y)$ --- element-wise comparison of~vectors ${\bf x}$ and~${\bf y}$,
i.e., a~vector ${\bf z}$ such that $z_i=1$ if $x_i<y_i$ and $z_i=0$ otherwise.

We replace the separator zeroes in~$x$ by ones and subtract~$y$. These ones
change back to zeroes exactly at the positions where $x_i<y_i$ and they stop
carries from propagating, so the fields do not interact with each other:

\setslot{\vbox{\hbox{~$x_{d-1}$}\hbox{~$y_{d-1}$}}}
\def\9{\rack{\0}{\hss ?\hss}}
\alik{
   \1 \[x_{d-1}] \1 \[x_{d-2}] \[\cdots] \1 \[x_1] \1 \[x_0]  \cr
-~ \0 \[y_{d-1}] \0 \[y_{d-2}] \[\cdots] \0 \[y_1] \0 \[y_0]  \cr
\rule
   \9 \[\ldots]  \9 \[\ldots]  \[\cdots] \9 \[\ldots] \9 \[\ldots] \cr
}

It only remains to shift the separator bits to the right positions, negate them
and mask out all other bits.

\:$\<Rank>(x,\alpha)$ --- returns the number of elements of~${\bf x}$ which are less than~$\alpha$,
assuming that the result fits in~$b$ bits:

\alik{
\<Rank>(x,\alpha) = \<Sum>(\<Cmp>(x,\<Replicate>(\alpha))). \cr
}

\:$\<Insert>(x,\alpha)$ --- inserts~$\alpha$ into a~sorted vector $\bf x$:

We calculate $k = \<Rank>(x,\alpha)$ first, then insert~$\alpha$ as the $k$-th
field of~$\bf x$ using masking operations and shifts.

\:$\<Unpack>(\alpha)$ --- creates a~vector whose elements are the bits of~$\(\alpha)_d$.
In other words, inserts blocks~$\0^b$ between the bits of~$\alpha$. Assuming that $b\ge d$,
we can do it as follows:

\algo
\:$x\=\<Replicate>(\alpha)$.
\:$y\=(2^{b-1},2^{b-2},\ldots,2^0)$. \cmt{bitwise encoding of this vector}
\:$z\=x \band y$.
\:Return $\<Cmp>(z,y)$.
\endalgo

Let us observe that $z_i$ is either zero or equal to~$y_i$ depending on the value
of the $i$-th bit of the number~$\alpha$. Comparing it with~$y_i$ normalizes it
to either zero or one.

\:$\<Unpack>_\pi(\alpha)$ --- like \<Unpack>, but changes the order of the
bits according to a~fixed permutation~$\pi$: The $i$-th element of the
resulting vector is equal to~$\alpha[\pi(i)]$.

Implemented as above, but with a~mask $y=(2^{\pi(b-1)},\ldots,2^{\pi(0)})$.

\:$\<Pack>(x)$ --- the inverse of \<Unpack>: given a~vector of zeroes and ones,
it produces a~number whose bits are the elements of the vector (in other words,
it crosses out the $\0^b$ blocks).

We interpret the~$x$ as an~encoding of a~vector with elements one bit shorter
and we sum these elements. For example, when $n=4$ and~$b=4$:

\setslot{\hbox{$x_3$}}
\def\z{\[\0]}
\def\|{\hskip1pt\vrule height 10pt depth 4pt\hskip1pt}
\def\.{\hphantom{\|}}

\alik{
\|\z\.\z\.\z\.\z\.\[x_3]\|\z\.\z\.\z\.\z\.\[x_2]\|\z\.\z\.\z\.\z\[x_1]\|\z\.\z\.\z\.\z\.\[x_0]\|\cr
\|\z\.\z\.\z\.\z\|\[x_3]\.\z\.\z\.\z\|\z\.\[x_2]\.\z\.\z\|\z\.\z\[x_1]\.\z\|\z\.\z\.\z\.\[x_0]\|\cr
}

However, this ``reformatting'' does not produce a~correct encoding of a~vector,
because the separator zeroes are missing. For this reason, the implementation
of~\<Sum> using modulo does not work correctly (it produces $\0^b$ instead of $\1^b$).
We therefore use the technique based on multiplication instead, which does not need
the separators. (Alternatively, we can observe that $\1^b$ is the only case
affected, so we can handle it separately.)

\endlist

\para
We can use the aforementioned tricks to perform interesting operations on individual
numbers in constant time, too. Let us assume for a~while that we are
operating on $b$-bit numbers and the word size is at least~$b^2$.
This enables us to make use of intermediate vectors with $b$~elements
of $b$~bits each.

\algn{Integer operations in quadratic workspace}\id{lsbmsb}

\itemize\ibull

\:$\<Weight>(\alpha)$ --- computes the Hamming weight of~$\alpha$, i.e., the number of ones in~$\(\alpha)$.

We perform \<Unpack> and then \<Sum>.

\:$\<Permute>_\pi(\alpha)$ --- shuffles the bits of~$\alpha$ according
to a~fixed permutation~$\pi$.

We perform $\<Unpack>_\pi$ and \<Pack> back.

\:$\<LSB>(\alpha)$ --- finds the least significant bit of~$\alpha$,
i.e., the smallest~$i$ such that $\alpha[i]=1$.

By a~combination of subtraction with $\bxor$, we create a~number
which contains ones exactly at the position of $\<LSB>(\alpha)$ and below:

\alik{
\alpha&=                        \9\9\9\9\9\1\0\0\0\0\cr
\alpha-1&=                      \9\9\9\9\9\0\1\1\1\1\cr
\alpha\bxor(\alpha-1)&=         \0\9\9\9\0\1\1\1\1\1\cr
}

Then we calculate the \<Weight> of the result and subtract~1.

\:$\<MSB>(\alpha)$ --- finds the most significant bit of~$\alpha$ (the position
of the highest bit set).

Reverse the bits of the number~$\alpha$ first by calling \<Permute>, then apply \<LSB>
and subtract the result from~$b-1$.

\endlist

\rem
As noted by Brodnik~\cite{brodnik:lsb} and others, the space requirements of
the \<LSB> operation can be reduced to linear. We split the input to $\sqrt{b}$
blocks of $\sqrt{b}$ bits each. Then we determine which blocks are non-zero and
identify the lowest such block (this is a~\<LSB> of a~number whose bits
correspond to the blocks). Finally we calculate the \<LSB> of this block. In
both calls to \<LSB,> we have a $\sqrt{b}$-bit number in a~$b$-bit word, so we
can use the previous algorithm. The same trick of course works for finding the
\<MSB> as well.

The following algorithm shows the details.

\algn{LSB in linear workspace}

\algo\id{lsb}
\algin A~$w$-bit number~$\alpha$.
\:$b\=\lceil\sqrt{w}\,\rceil$. \cmt{size of a~block}
\:$\ell\=b$. \cmt{the number of blocks is the same}
\:$x\=(\alpha \band (\0\1^b)^\ell) \bor (\alpha \band (\1\0^b)^\ell)$.
\hfill\break
\cmt{encoding of a~vector~${\bf x}$ such that $x_i\ne 0$ iff the $i$-th block is non-zero}%
\foot{Why is this so complicated? It is tempting to take $\alpha$ itself as a~code of this vector,
but we unfortunately need the separator bits between elements, so we create them and
relocate the bits we have overwritten.}
\:$y\=\<Cmp>(0,x)$. \cmt{$y_i=1$ if the $i$-th block is non-zero, otherwise $y_0=0$}
\:$\beta\=\<Pack>(y)$. \cmt{each block compressed to a~single bit}
\:$p\=\<LSB>(\beta)$. \cmt{the index of the lowest non-zero block}
\:$\gamma\=(\alpha \shr bp) \band \1^b$. \cmt{the contents of that block}
\:$q\=\<LSB>(\gamma)$. \cmt{the lowest bit set there}
\algout $\<LSB>(\alpha) = bp+q$.
\endalgo

\rem
We have used a~plenty of constants which depend on the format of the vectors.
Either we can write non-uniform programs (see \ref{nonuniform}) and use native constants,
or we can observe that all such constants can be easily manufactured. For example,
$(\0^b\1)^d = \1^{(b+1)d} / \1^{b+1} = (2^{(b+1)d}-1)/(2^{b+1}-1)$. The only exceptions
are the~$w$ and~$b$ in the LSB algorithm \ref{lsb}, which we are unable to produce
in constant time. In practice we use the ``bit tricks'' as frequently called subroutines
in an~encompassing algorithm, so we usually can spend a~lot of time on the precalculation
of constants performed once during algorithm startup.

%--------------------------------------------------------------------------------

\section{Q-Heaps}\id{qheaps}%

We have shown how to perform relatively complicated operations on a~set of values
in constant time, but so far only under the assumption that the number of these
values is small enough and that the values themselves are also small enough
(so that the whole set fits in $\O(1)$ machine words). Now we will show how to
lift the restriction on the magnitude of the values and still keep constant time
complexity. We will describe a~simplified version of the Q-Heaps developed by
Fredman and Willard in~\cite{fw:transdich}.

The Q-Heap represents a~set of at most~$k$ word-sized integers, where $k\le W^{1/4}$
and $W$ is the word size of the machine. It will support insertion, deletion, finding
of minimum, and other operations described below, in constant time, provided that
we are willing to spend~$\O(2^{k^4})$ time on preprocessing.

The exponential-time preprocessing may sound alarming, but a~typical application uses
Q-Heaps of size $k=\log^{1/4} N$, where $N$ is the size of the algorithm's input,
which guarantees that $k\le W^{1/4}$ and $\O(2^{k^4}) = \O(N)$. Let us however
remark that the whole construction is primarily of theoretical importance
and that the huge constants involved everywhere make these heaps useless
for practical algorithms. However, many of the tricks used prove themselves
useful even in real-life implementations.

Preprocessing makes it possible to precompute tables for almost arbitrary functions
and then assume that they can be evaluated in constant time:

\lemma\id{qhprecomp}%
When~$f$ is a~function computable in polynomial time, $\O(2^{k^4})$ time is enough
to precompute a~table of the values of~$f$ for the values of its arguments whose total
bit size is $\O(k^3)$.

\proof
There are $2^{\O(k^3)}$ possible combinations of arguments of the given size and for each of
them we spend $\O(k^c)$ time by calculating the function (for some~$c\ge 1$). It remains
to observe that $2^{\O(k^3)}\cdot \O(k^c) = \O(2^{k^4})$.
\qed

\para
We will first show an~auxiliary construction based on tries and then derive
the actual definition of the Q-Heap from it.

\nota
Let us introduce some notation first:
\itemize\ibull
\:$W$ --- the word size of the RAM,
\:$k = \O(W^{1/4})$ --- the limit on the size of the heap,
\:$n\le k$ --- the number of elements in the represented set,
\:$X=\{x_1, \ldots, x_n\}$ --- the elements themselves: distinct $W$-bit numbers
indexed in a~way that $x_1 < \ldots < x_n$,
\:$c_i = \<MSB>(x_i \bxor x_{i+1})$ --- the most significant bit of those in which $x_i$ and~$x_{i+1}$ differ,
\:$R_X(x)$ --- the rank of~$x$ in~$X$, that is the number of elements of~$X$, which are less than~$x$
(where $x$~itself need not be an~element of~$X$).\foot{We will dedicate the whole chapter \ref{rankchap} to the
study of various ranks.}
\endlist

\defn
A~\df{trie} for a~set of strings~$S$ over a~finite alphabet~$\Sigma$ is
a~rooted tree whose vertices are the prefixes of the strings in~$S$ and there
is an~edge going from a~prefix~$\alpha$ to a~prefix~$\beta$ iff $\beta$ can be
obtained from~$\alpha$ by adding a~single symbol of the alphabet. The edge
will be labeled with the particular symbol. We will also define a~\df{letter depth}
of a~vertex to be the length of the corresponding prefix and mark the vertices
which match a~string of~$S$.

A~\df{compressed trie} is obtained from the trie by removing the vertices of outdegree~1.
Whereever is a~directed path whose internal vertices have outdegree~1, we replace this
path by a~single edge labeled with the contatenation of the original edge's labels.

In both kinds of tries, we will order the outgoing edges of every vertex by their labels
lexicographically.

\obs
In both tries, the root of the tree is the empty word and for every vertex, the
corresponding prefix is equal to the concatenation of edge labels on the path
leading from the root to the vertex. The letter depth of the vertex is equal to
the total size of these labels. All leaves correspond to strings in~$S$, but so can
some internal vertices if there are two strings in~$S$ such that one is a~prefix
of the other.

Furthermore, the labels of all edges leaving a~common vertex are always
distinct and when we compress the trie, no two such labels have share their initial
symbols. This allows us to search in the trie efficiently: when looking for
a~string~$x$, we follow the path from the root and whenever we visit
an~internal vertex of letter depth~$d$, we test the $d$-th character of~$x$,
follow the edge whose label starts with this character, and check that the
rest of the label matches.

The compressed trie is also efficient in terms of space consumption --- it has
$\O(\vert S\vert)$ vertices (this can be easily shown by induction on~$\vert S\vert$)
and all edge labels can be represented in space linear in the sum of the
lengths of the strings in~$S$.

\defn
For our set~$X$, we will define~$T$ as a~compressed trie for the set of binary
encodings of the numbers~$x_i$, padded to exactly $W$~bits, i.e., for $S = \{ \(x)_W ; x\in X \}$.

\obs
The trie~$T$ has several interesting properties. Since all words in~$S$ have the same
length, the leaves of the trie correspond to these exact words, that is to the numbers~$x_i$.
The inorder traversal of the trie enumerates the words of~$S$ in lexicographic order
and therefore also the~$x_i$'s in the order of their values. Between each
pair of leaves $x_i$ and~$x_{i+1}$ it visits an~internal vertex whose letter depth
is exactly~$W-1-c_i$.

\para
Let us now modify the algorithm for searching in the trie and make it compare
only the first symbols of the edges. For $x\in X$, the algorithm will still
return the correct leave, for all~$x$ outside~$X$ it will no longer fail
and instead it will land in some leaf $x_i$. We will call the index of this leaf $T(x)$.
At the first sight this vertex may seem unrelated, but we will show that it can
be used to determine the rank of~$x$ in~$X$, which will later form a~basis
for all other Q-Heap operations:

\lemma\id{qhdeterm}%
The rank $R_X(x)$ is uniquely determined by a~combination of:
\itemize\ibull
\:the trie~$T$,
\:the index $i=T(x)$ of the leaf visited when searching for~$x$ in~$T$,
\:the relation ($<$, $=$, $>$) between $x$ and $x_i$,
\:the bit position $b=\<MSB>(x\bxor x_i)$ of the first disagreement between~$x$ and~$x_i$.
\endlist

\proof
If $x\in X$, we detect that from $x_i=x$ and the rank is obviously~$i$ itself.
Let us assume that $x\not\in X$ and imagine that we follow the same path as during the search for~$T(x)$,
but this time we check the full edge labels. The position~$b$ is the first position
where~$\(x)$ disagrees with a~label. Before this point, all edges not taken by
the search were leading either to subtrees containing elements all smaller than~$x$
or all larger than~$x$ and the only values not known yet are those in the subtree
below the edge which we currently consider. Now if $x[b]=0$ (and therefore $x<x_i$),
all values in the subtree have $x_j[b]=1$ and thus they are larger. In the other
case, $x[b]=1$ and $x_j[b]=0$, so they are smaller.
\qed

\para
The preceding lemma shows that the rank can be computed in polynomial time, but
unfortunately the variables on which it depends are too large for the table to
be efficiently precomputed. We will therefore carefully choose a~representation
of the trie, which is compact enough.

\lemma\id{citree}%
The trie is uniquely determined by the order of the values~$c_1,\ldots,c_{n-1}$.

\proof
We already know that the letter depths of the trie vertices are exactly
the numbers~$W-1-c_i$. The root of the trie must have the smallest of these
letter depths, i.e., it must correspond to the highest numbered bit. Let
us call this bit~$c_i$. This implies that the values $x_1,\ldots,x_i$
must lie in the left subtree of the root and $x_{i+1},\ldots,x_n$ in its
right subtree. Both subtrees can be then constructed recursively.\foot{This
construction is also known as the \df{cartesian tree} for the sequence
$c_1,\ldots,c_n$.}
\qed

\para
However, the vector of the $c_i$'s is also too long (is has $k\log W$ bits
and we have no upper bound on~$W$ in terms of~$k$), so we will compress it even
further:

\nota
\itemize\ibull
\:$B = \{c_1,\ldots,c_n\}$ --- the set of all bit positions examined by the trie,
stored as a~sorted array,
\:$C : \{1,\ldots,n\} \rightarrow \{1,\ldots,n\}$ --- a~function such that
$c_i = B[C(i)]$,
\:$x[B]$ --- a~bit string containing the bits of~$x$ originally located
at the positions indexed by~$B$.
\endlist

\obs
The set~$B$ has $\O(k\log W)=\O(W)$ bits, so it can be stored in a~constant number
of machine words as a~vector. The function~$C$ can be also stored as a~vector
of $k\log k$ bits.

\lemma
The rank $R_X(x)$ can be computed in constant time from:
\itemize\ibull
\:the function~$C$,
\:the values $x_1,\ldots,x_n$,
\:the bit string~$x[B]$,
\:$x$ itself.
\endlist

\proof
We know that the trie~$T$ is uniquely determined by the order of the $c_i$'s
and therefore by the function~$C$ since the array~$B$ is sorted. The shape of
the trie together with the bits in $x[B]$ determine the leaf $T[x]$ visited
when searching for~$x$. All this can be computed in polynomial time and it
depends on $\O(k\log k)$ bits of input, so according to Lemma~\ref{qhprecomp}
we can look it up in a~precomputed table.

Similarly we will determine all other ingredients of Lemma~\ref{qhdeterm} in
constant time. As we know~$x$ and all the $x_i$'s, we can immediately find
the relation $x$ and $x_{T[x]}$ and use the LSB/MSB algorithm (\ref{lsb})
to find the topmost disagreeing bit.

All these ingredients can be stored in $\O(k\log k)$ bits, so we may assume
that the rank can be looked up in constant time as well.
\qed


\endpart