# Lawler-Kozdron-Richards-Stroock’s combined Proof for the Matrix-Tree theorem and Wilson’s Theorem

David Wilson and a cover of Shlomo’s recent book “Curvature in mathematics and physics”

A few weeks ago, in David Kazhdan’s basic notion seminar, Shlomo Sternberg gave a lovely presentation Kirchho ff and Wilson via Kozdron and Stroock. The lecture is based on the work presented in the very recent paper by Michael J. Kozdron,  Larissa M. Richards, and Daniel W. Stroock: Determinants, their applications to Markov processes, and a random walk proof of Kirchhoff’s matrix tree theorem. Preprint, 2013. Available online at arXiv:1306.2059.

Here is the abstract:

Kirchhoff’s formula for the number of spanning trees in a connected graph  is over 150 years old. For example, it says that if $c_2, \dots, c_n$ are the nonzero  eigenvalues of the Laplacian, then the number k of spanning trees is $k= (1/n)c_2\cdots c_n.$ There are many proofs.  An algorithm due to Wilson via loop erased random walks produces such a tree, and Wilson’s theorem is that all spanning trees are produced by his algorithm with equal probability. Hence,  after the fact, we know that Wilson’s algorithm produces any given tree with probability 1/k.  A proof due to Lawler, using the Green’s function, shows directly that Wilson’s algorithm has the probability 1/k  of producing any given spanning tree, thus simultaneously proving Wilson’s theorem and Kirchhoff’s formula. Lawler’s proof has been considerably simplified by Kozdron and Stroock. I plan to explain their proof. The lecture will be completely self-contained, using only Cramer’s rule from linear algebra.

(Here are also lecture notes of the lecture by Ron Rosenthal.)

Here is some background.

## The matrix-tree theorem

The matrix tree theorem asserts that the number of rooted spanning trees of a connected graph G  is the product of the non-zero eigenvalues of L(G), the Laplacian of G.

Suppose that G has n vertices. The Laplacian of G is the matrix whose (i,i)-entry is the degree of the ith vertex, and its (i,j) entry for $i \ne j$ is 0 if the ith vertex is not adjacent to the jth vertex, and -1 if they are adjacent. So  L(G)=D-A(G) where A(G) is the adjacency matrix of G, and D is a diagonal matrix whose entries are the degrees of the vertices.  An equivalent formulation of the matrix-tree theorem is that the number of spanning trees is the determinant of a matrix obtained from the Laplacian by deleting the j th row and j th column.

We considered a high dimensional generalization of the matrix tree theorem in these posts (I, II, III, IV).

## How to generate a random spanning tree for a graph G?

### Using the matrix-tree theorem

Method A: Start with an edge $e \in G$, use the matrix-tree theorem to compute the probability $p_e$ that e belongs to a random spanning tree of G, take e with probability $p_e$. If e is taken consider the contraction $G/e$ and if G is not taken consider the deletion $G \backslash e$ and continue.

This is an efficient method to generate a random spanning tree according to the uniform probability distribution. You can extend it by assigning each edge a weight and chosing a tree with probability proportional to the product of its weights.

### Random weights and greedy

Method B: Assign each edge a random real number between 0 and 1 and chose the spanning tree which minimizes the sum of weights via the greedy algorithm.

This is a wonderful method but it leads to a different probability distribution on random spanning trees which is very interesting!

### The Aldous-Broder random walk method

Method C: The Aldous-Broder theorem. Start a simple random walk from a vertex of the graph until reaching all vertices, and take each edge that did not form a cycle with earlier edges. (Or, in other words, take every edge that reduced the number of connected components of the graph on the whole vertex set and visited edges.)

Amazingly, this leads to a random uniform spanning tree. The next method is also very amazing and important for many applications.

### David Wilson’s algorithm

Method D: Wilson’s algorithm. Fix a vertex as a root. (Later the root will be a whole set of vertices, and a tree on them.) Start from an arbitrary vertex u not in the root and take a simple random walk until you reach the root. Next, erase all edges in cycles of the path created by the random walk so you will left with a simple path from  u to the root. Add this path to the root and continue!

Here is a link to Wilson’s paper! Here is a nice presentation by Chatterji  and Gulwani.

# Auction-based Tic Tac Toe: Solution

Reshef, Moshe and Sam

## The question:

### (based on discussions with Reshef Meir, Moshe Tennenholtz, and Sam Payne)

Tic Tac Toe is played since anciant times. For the common version, where the two players X and O take turns in marking the empty squares in a 3 by 3 board – each player has a strategy that can guarantee a draw. Now suppose that the X player has a budget of Y dollars, the O playare has a budget of 100 dollars and before each round the two players bid who makes the next move. The highest bidder makes the move and his budget is reduced by his bid.

What would you expect the minimal value of Y is so the X player has a winning strategy?

We asked this question in our test your intuition series (#17)  in this post. Here is a recent new nice version of tic-tac-toe.

# Some old and new problems in combinatorics and geometry

Paul Erdős in Jerusalem, 1933  1993

Update: Here is a link to a draft of a paper* based on the first part of this lecture. Some old and new problems in combinatorial geometry I: Around Borsuk’s problem.

I just came back from a great Erdős Centennial conference in wonderful Budapest. I gave a lecture on old and new problems (mainly) in combinatorics and geometry (here are the slides), where I presented twenty problems, here they are:

## Around Borsuk’s Problem

Let $f(d)$ be the smallest integer so that every set of diameter one in $R^d$ can be covered by $f(d)$ sets of smaller diameter. Borsuk conjectured that $f(d) \le d+1$.

It is known (Kahn and Kalai, 1993) that : $f(d) \ge 1.2^{\sqrt d}$and also that (Schramm, 1989) $f(d) \le (\sqrt{3/2}+o(1))^d$.

Problem 1: Is f(d) exponential in d?

Problem 2: What is the smallest dimension for which Borsuk’s conjecture is false?

## Volume of sets of constant width in high dimensions

Problem 3: Let us denote the volume of the n-ball of radius 1/2 by $V_n$.

Question (Oded Schramm): Is there some $\epsilon >0$ so that for every $n>1$ there exist a set $K_n$ of constant width 1 in dimension n whose volume satisfies $VOL(K_n) \le (1-\epsilon)^n V_n$.

## Around Tverberg’s theorem

Tverberg’s Theorem states the following: Let $x_1,x_2,\dots, x_m$ be points in $R^d$ with $m \ge (r-1)(d+1)+1$Then there is a partition $S_1,S_2,\dots, S_r$ of $\{1,2,\dots,m\}$ such that  $\cap _{j=1}^rconv (x_i: i \in S_j) \ne \emptyset$.

Problem 4:  Let $t(d,r,k)$ be the smallest integer such that given $m$ points  $x_1,x_2,\dots, x_m$ in $R^d$, $m \ge t(d,r,k)$ there exists a partition $S_1,S_2,\dots, S_r$ of $\{1,2,\dots,m\}$ such that every $k$ among the convex hulls $conv (x_i: i \in S_j)$, $j=1,2,\dots,r$  have a point in common.

Reay’s “relaxed Tverberg conjecture” asserts that that whenever $k >1$ (and $k \le r$), $t(d,r,k)= (d+1)(r-1)+1$.

Problem 5: For a set $A$, denote by $T_r(A)$ those points in $R^d$ which belong to the convex hull of $r$ pairwise disjoint subsets of $X$. We call these points Tverberg points of order $r$.

Conjecture: For every $A \subset R^d$ , $\sum_{r=1}^{|A|} {\rm dim} T_r(A) \ge 0$.

Note that $\dim \emptyset = -1$.

Problem 6:   How many points $T(d;s,t)$ in $R^d$ guarantee that they can be divided into two parts so that every union of $s$ convex sets containing the first part has a non empty intersection with every union of $t$ convex sets containing the second part.

## A question about directed graphs

Problem 7: Let G be a directed graph with n vertices and 2n-2 edges. When can you divide your set of edges into two trees $T_1$ and $T_2$ (so far we disregard the orientation of edges,) so that when you reverse the directions of all edges in $T_2$ you get a strongly connected digraph.

Problem 8

Conjecture: Let $\cal C$ be a collection of triangulations of an n-gon so that every two triangulations in $\cal C$ share a diagonal.  Then $|{\cal C}|$ is at most the number of triangulations of an (n-1)-gon.

## F ≤ 4E

Problem 9: Let K be a two-dimensional simplicial complex and suppose that K can be embedded in $R^4$. Denote by E the number of edges of K and by F the number of 2-faces of K.

Conjecture:  4E

A weaker version which is also widely open and very interesting is: For some absolute constant C C E.

## Polynomial Hirsch

Problem 10:  The diameter of graphs of d-polytopes with n facets is bounded above by a polynomial in d and n.

## Analysis – Fixed points

Problem 11: Let K be a convex body in $R^d$. (Say, a ball, say a cube…) For which classes $\cal C$ of functions, every $f \in {\cal C}$ which takes K into itself admits a fixed point in K.

## Number theory – infinitely many primes in sparse sets

Problem 12: Find a (not extremely artificial) set A of integers so that for every n, $|A\cap [n]| \le n^{0.499}$where you can prove that A contains infinitely many primes.

## Möbius randomness for sparse sets

Problem 13: Find a (not extremely artificial) set A of integers so that for every n, $|A\cap [n]| \le n^{0.499}$ where you can prove that

$\sum \{\mu(k): k \le n, k \in A\} = o(|A \cap [n]).$

## Computation – noisy game of life

Problem 14: Does a noisy version of Conway’s game of life support universal computation?

## Ramsey for polytopes

Problem 15:

Conjecture: For a fixed k, every d-polytope of sufficiently high dimension contains a k-face which is either a simplex or a (combinatorial) cube.

## Expectation thresholds and thresholds

Problem 16: Consider a random graph G in G(n,p) and the graph property: G contains a copy of a specific graph H. (Note: H depends on n; a motivating example: H is a Hamiltonian cycle.) Let q be the minimal value for which the expected number of copies of H’ in G in G(n,q) is at least 1/2 for every subgraph H’ of H. Let p be the value for which the probability that G in G(n,p) contains a copy of H is 1/2.

Conjecture: [Kahn – Kalai 2006]  p/q = O( log n)

## Traces

Problem 17: Let X be a family of subsets of $[n]=\{1,2,\dots,n\}$.
How large X is needed to be so that the restriction (trace) of X to some set $B \subset [n]$$|B|=(1/2+\delta)n$ has at least $3/4 \cdot 2^{|B|}$ elements?

## Graph-codes

Problem 18: Let  P  be a property of graphs. Let $\cal G$ be a collection of graphs with n vertices so that the symmetric difference of two graphs in $\cal G$ has property PHow large can $\cal G$ be.

## Conditions for colorability

Problem 19: A conjecture by Roy Meshulam and me:

There is a constant C such that every graph G
with no induced cycles of order divisible by 3 is colorable by C colors.

Problem 20:

Another conjecture by Roy Meshulam and me: For every b>0 there
is a constant C=C(b) with the following property:

Let G be a graph such that for all its induced subgraphs H

The number of independent sets of odd size minus the number of independent sets of even size is between -b  and b.

Then G is colorable by C(b) colors.

## Remarks:

The title of the lecture is borrowed from several papers and talks by Erdős. Continue reading