Diaz:
> 2: simplify, remove proof sketches
-- 3: intro: replace "efficient" by "linear"
-- 3.1.7, 3.1.10: skip proof
-- 3.3: do we really need the full Komlos's result?
-- 3.3.17: maxflow: shouldn't Karzanov be cited, too?
-- 3.5.1: does the first alg give any insight?
-- 3.5.1: mention geometric distribution
-- 5.4.6: could we give a simple proof?
-- 5: mention graphs with moving vertices?
-- 6: mention d-regular graphs
-- 6: two monographs on Euclidean MST
- M. Steel: Probability theory and combinatorial optimization, SIAM 1997
- J. Yukich: Probability theory of classical Euclidean optimization problems, Springer 1998
+> 3.1.7, 3.1.10: skip proof
+> 3.5.1: does the first alg give any insight?
+> 5: mention graphs with moving vertices?
Patrice:
-- Remark on 2.5.1: polynomial time could be replaced by sub-exponential time.
+> Remark on 2.5.1: polynomial time could be replaced by sub-exponential time.
- For 1.5.6, you should probably quote D. Cheriton and R.E. Tarjan.
Finding Minimum Spanning Trees. SIAM J. on Comp. 5(4) (1976) pp.
724-742. who gave a linear time algorithm for planar graphs, extended by
Tarjan in 1983 to proper minor closed classes (both quoted by Gustedt).
[XXX: Cannot get the paper.]
-- In 3.1.12 and 3.1.16, you should make explicit the dependence of the
+> In 3.1.12 and 3.1.16, you should make explicit the dependence of the
running time with respect, for instance, to the Hadwiger number of the
graph or to the maximal density nabla(G) of a minor of the graph, as
considering a minor closed class or another does not change the
The contractive algorithm given in Section~\ref{contalg} has been found to perform
well on planar graphs, but in the general case its time complexity was not linear.
-Can we find any broader class of graphs where this algorithm is still efficient?
+Can we find any broader class of graphs where this algorithm is still linear?
The right context turns out to be the minor-closed graph classes, which are
closed under contractions and have bounded density.
\thmn{Excluded minors, Robertson \& Seymour \cite{rs:wagner}}
For every non-trivial minor-closed graph class~$\cal C$ there exists
a~finite set~$\cal H$ of graphs such that ${\cal C}=\Forb({\cal H})$.
+\qed
-\proof
This theorem has been proven in a~long series of papers on graph minors
culminating with~\cite{rs:wagner}. See this paper and follow the references
to the previous articles in the series.
-\qed
\para
For analysis of the contractive algorithm,
flips in step~2 of this algorithm. We also know that the algorithm stops before
it adds $n$~edges to~$F$. Therefore it flips at most as many coins as a~simple
random process that repeatedly flips until it gets~$n$ heads. As waiting for
-every occurrence of heads takes expected time~$1/p$, waiting for~$n$ heads
-must take $n/p$. This is the bound we wanted to achieve.
+every occurrence of heads takes expected time~$1/p$ (the distribution is geometric),
+waiting for~$n$ heads must take $n/p$. This is the bound we wanted to achieve.
\qed
\para
variations on the geometric MST, see Eppstein's survey paper
\cite{eppstein:spanning}.
+There are also plentiful interesting results on expected properties of the
+Euclidean MST of various random point configurations. These are well covered
+by the monographs of Steele \cite{steele:ptco} and Yukich \cite{yukich:pteucl}.
+
\paran{Steiner trees}
The constraint that the segments in the previous example are allowed to touch
each other only in the given points looks artificial and it is indeed uncommon in
pages={282--285},
year={1951}
}
+
+@book { steele:ptco,
+ author = "J. Michael Steele",
+ title = "{Probability Theory and Combinatorial Optimization}",
+ series = "{CMBS-NSF Regional Conference Series in Applied Mathematics}",
+ volume = 69,
+ year = "1987",
+ publisher = "Society for Industrial and Applied Mathematics"
+}
+
+@book{ yukich:pteucl,
+ title={{Probability Theory of Classical Euclidean Optimization Problems}},
+ author={Yukich, J.E.},
+ year={1998},
+ publisher={Springer},
+ publisher = {Springer Verlag},
+ volume={1675},
+ series={{Lecture Notes in Math}},
+}