Analysis of Boolean Functions – week 4

Lecture 6

Last week we discussed two applications of the Fourier-Walsh plus hypercontractivity method and in this lecture we will discuss one additional application:

The lecture was based on a 5-pages paper by Ehud Friedgut and Jeff Kahn: On the number of copies of one hypergraph in another  Israel Journal of Mathematics Vol. 105 (1998) pp. 251-256.

In this application our method  has nice but not optimal consequences, and another method – applying Shearer’s inequality, gives the optimal result.

1) The question: Given a hypegraph H with k vertices what is the maximum number of labeled copies of H inside a hypegraph G with \ell edges.

There are cases where the answer is known with great precision. The Kruskal-Katona theorem gives the answer when H is the hypegraph whose edges are all the r-subsets of a vertex set V of size k.

In our study  we will fix H and care only about the asymptotic behavior as a function of \ell. We have a simple upper bound of \ell^k and the question is to identify the correct exponent.

2) Stable sets and fractional stable sets. A stable set S in a hypergraph H (also called independent set) is a set of vertices so that every edge contains at most one vertex from S. The stable number \alpha(H) of a hypergraph H is the maximum size of a stable set.

Now comes an idea which is very important in graph theory, of considering the linear programming relaxation of combinatorial parameters. A fractional stable set is an assignment of non negative weights to the vertices, so the sum of weights in every edge is at most one. So this is a “fuzzy set” of a kind the membership of a vertex to a set is described by a number in the interval [0,1] rather than the set {0,1}. The size of a fractional stable set is the sum of weights of all vertices. The fractional stable number \alpha^*(H) is the minimum size of a fractional stable set.

3) Cover and fractional cover numbers

We next described covering and fractional covering (of vertices by edges) in hypergraphs, the covering number \rho(H) of a hypergraph H and the fractional covering number \rho^*(H). Linear programming duality gives that the fractional stable number  is equal to the fractional covering number.

4) The answer:

Friedgut-Kahn theorem: The number of copies of H is a hypergraph G with \ell edges is O(\ell^{\rho^*(H)}) and this bound is sharp.

The case of graphs was proved (in a different language) by Noga Alon in his M. Sc. thesis, and Noga’s first publication  On the number of subgraphs of prescribed type pf graphs with a given number of edges,( Israel J. Math. 38(1981), 116-130).) Part of the challenge was to find the right extension for Alon’s theorem.

5) The lower bound.

Next we explained the nice and simple construction giving the lower bound, which is based on the weights realizing the fractional stable number of H.

6) Bonami’s inequality in a dual form

Our next thing was to state a consequence of Bonami’s hypercontractive inequality, which is a direct extension of Chinchine inequality. Then we showed a weaker upper bound than the actual theorem based on the Bonami inequality .

It is an interesting open question to apply harmonic analysis to the general case. (I believe it is tractable.)

7) Traces and Shearer’s lemma

Next we defined the trace of a hypergraph on a subset W of vertex-set V and stated Shearer’s lemma.

8) More about traces and a little more extremal combinatorics

Not having enough time to complete the proof of Friedgut-Kahn theorem using Shearer’s lemma, we proved the fundamental Sauer-Shelah inequality (see this post), and stated Frankl’s conjecture (see this post, and this one  (sec 3d) ).

Lecture 7

We started with the proof of the Friedgut-Kahn bound using Shearer’s lemma. Then we explained the simple connection between influences and traces and mentioned the connection of Shearer’s lemma (and the Loomis-Whitney theorem) to edge-sioperimetry.

Our next application: First Passage percolation. We gave a short introduction to models of percolation and started to discuss our fourth application of the Fourier+hypercontractivity method: An upper bound for the variance of first passage percolation. Here the method gives the best known result, but unlike KKL’s theorem the result is not sharp. We are third way toward a proof so I may write about it next time. The discussion of first passage percolation is based on the paper First Passage Percolation Has Sublinear Distance Variance by Benjamini, Kalai and Schramm.

Analysis of Boolean Functions – Week 3

Lecture 4

In the third week we moved directly to the course’s “punchline” – the use of Fourier-Walsh expansion of Boolean functions and the use of Hypercontractivity.

Before that we  started with  a very nice discrete isoperimetric question on a graph which is very much related to the graph of the discrete cube.

Consider the graph G whose vertices are 0-1 vectors of length n with precisely r ‘1’s, and with edges corresponding to vertices of Hamming distance two. (Which is the minimal Hamming distance between distinct vertices.) Given a set A of m vertices, how small can E(A, \bar A) be? (We already talked about intersecting families of sets of constant size – the Erdos-Ko-Rado theorem and, in general, it is a nice challenge to extend some of the ideas/methods of the course to constant weight situation.)

And now for the main part of the lecture.

1) Basics harmonic analysis on the discrete cube. We considered the vector space of real functions on the discrete cube \Omega_n and defined an inner product structure. We also defined the p-th norm for 1\le p\le \infty. Next we defined the Fourier-Walsh functions and showed that they form a orthonormal basis. This now leads to the Fourier-Walsh expansion for an arbitrary real function f on the discrete cube f=\sum_{S\subset[n]}\hat f(S)W_S, and we could easily verify Parseval formula.

2) Influence and Fourier. If f is a real function on \Omega_n and f=\sum \hat f(S)W_S its Fourier-Walsh expansion. We showed that I_k(f)=\sum_{S:k \in S}\hat f^2(S). It follows that I(f)=\sum_S\hat f^2(S)|S|. The Fourier-theoretic proof for I(f) ≥ 4 t (1-t) where t=μ(f) now follows easily.

3) Chinchine, hypercontractivity and the discrete isoperimetric inequality.

Next we discussed what will it take to prove the better estimate I(f) ≥ K t log t. We stated Chinchine inequality, and explained why is Chinchine inequality relevant: For Boolean functions the pth power of the p-norm does not depend on p. (It always equals t.) Therefore if t is small, the p-th norms themselves much be well apart! After spending a few moments on the history of the inequality (as told to me by Ron Blei) we discussed what kind of extension do we need, and stated  the Bonami-Gross-Beckner inequality. We use Bonami’s inequality to proof of the inequality I(f) ≥ K t log t and briefly talked about what more does it give.

Lecture 5

1) Review and examples. We reviewed what we did in the previous lecture and considered a few examples: Dictatiorship; the AND function (an opportunity to mention the uncertainty principle,) and MAJORITY on three variables. We also mentioned another connection between influences and Fourier-Walsh coefficients: for a monotone (non decreasing) Boolean function f, I_k(f) = -2\hat f(\{k\}).

2) KKL’s theorem

KKL’s theorem: There is an absolute constant K so that for every Boolean function f, with t=μ(f), there exists k, 1 ≤ k ≤ n such that

I_k(f) \ge K t (1-t) logn/n.

To prove of KKL’s theorem: we repeat, to a large extent, the steps from Lecture 4 (of course, the proof of KKL’s theorem was where this line of argument came from.) We showed that if all individual influences are below 1/sqrt n than I(f) \ge K t(1-t) \log n.

We mentioned one corollary: For Boolean function invariant under a transitive group of permutations, all individual influences are equal and therefore I(f) \ge K t (1-t)\log n.

3) Further problems

In the  last part of the lecture we mentioned seven problems regarding influence of variables and KKL’s theorem (and I added two here):

1) What can be said about balanced Boolean functions with small total influence?

2) What can be said about Boolean functions for which I(f) ≤ K t log (1/t), for some constant K, where t=μ(f)?

3) What can be said about the connection between the symmetry group and the minimum total influence?

4) What can be said about Boolean functions (1/3 ≤ μ(f)≤ 2/3, say) for which \max I_k(f) \le K log n/n.?

5) What more can be said about the vector of influences (I_1(f),I_2(f), \dots I_n(f))?

6)* What is the sharp constant in KKL’s theorem?

7)* What about edge expansions of (small) sets of vertices in general graphs?

8) Under what conditions I(f) \ge n^\beta for β >0.

9) What about influence of larger sets? In particular, what is the smallest t (as a function of n ) such that if \mu(f)=t there is a set S of variables S ≤ 0.3n with I_S(f) \ge 0.9?

(This post  is a short version, I will add details later on.)

Richard Stanley: How the Proof of the Upper Bound Theorem (for spheres) was Found

The upper bound theorem asserts that among all d-dimensional polytopes with n vertices, the cyclic polytope maximizes the number of facets (and k-faces for every k). It was proved by McMullen for polytopes in 1970, and by Stanley for general triangulations of spheres in 1975. This theorem is related to a lot of mathematics (and also computational geometry) and there are many interesting extensions and related conjectures.

Richard Stanley posted (on his  homepage which is full with interesting things) an article describing How the proof of the upper bound theorem for triangulations of spheres was found.

It is interesting how, for Richard, the work oמ face-numbers of polytopes started with his work on integer points in polytopes and especially the Anand-Dumir-Gupta” conjecture on enumeration of “magic squares.” (See this survey article by Winfried Bruns.)  Integer points in polytopes, and face numbers represent the two initial chapters of Richard’s “green book” on Commutative algebra and combinatorics. Both these topics are related to commutative algebra and to the algebraic geometry of toric varieties.

CCA

See also these relevant posts “(Eran Nevo) The g-conjecture II: The commutative algebra connection,), (Eran Nevo) The g-conjecture I, and How the g-conjecture came about. Various results and problems related to the upper bound theorem can be found in Section 2 of my paper Combinatorics with a Geometric Flavor.

Analysis of Boolean functions – week 2

Post on week 1; home page of the course analysis of Boolean functions

Lecture II:

We discussed two important examples that were introduced by Ben-Or and Linial: Recursive majority and  tribes.

Recursive majority (RM): F_m is a Boolean function with 3^m variables and F_{m+1} (x,y,z) = F_1(F_m(x),F_m(y),F_m(z)). For the base case we use the majority function F_1(x,y,z)=MAJ(x,y,z).

Tribes: Divide your n variables into s pairwise disjoint sets (“tribes”) of cardinality t. f=1 if for some tribe all variables equal one, and thus T=0 if for every tribe there is a variable with value ‘0’.

We note that this is not an odd function i.e. it is not symmetric with respect to switching ‘0’ and ‘1’. To have \mu=1/2 we need to set t=\log_2n - \log_2\log_2n+c. We computed the influence of every variable to be C log n/n. The tribe function is a depth-two formula of linear size and we briefly discussed what are Boolean formulas and Boolean circuits (These notions can be found in many places and also in this post.).

I states several conjectures and questions that Ben-Or and Linial raised in their 85 paper:

Conjecture 1: For every balanced Boolean function with n variables there is a variable k whose influence is \Omega (\log n/n).

Conjecture 2: For every balanced Boolean function with n variables there is a set S of n/log n variables whose influence I_S(f) is 1-o(1).

Question 3: To what extent can the bound in Conjecture 2 be improved if the function f is odd. (Namely, f(1-x_1,1-x_2,\dots, 1-x_n)=1-f(x_1,x_2,\dots, x_n).)

Our next theme was discrete isoperimetric results.  I noted the connection between total influence and edge expansion and proved the basic isoperimetric inequality: If μ(f)=t then I(f) ≥ 4 t(1-t). The proof uses the canonical paths argument.

Lecture III:

We proved using “compression” that sharp bound on I(f) as a function of t=μ(f). We made the analogy between compression and Steiner symmetrization – a classic method for proving the classical isoperimetric theorem. We discussed similar results on vertex boundary and on Talagrand-Margulis boundary (to be elaborated later in the course).

Then We proved the Harris-Kleitman inequality and showed how to deduce the fact that intersecting family of subsets of [n] with the property that the family of complements is also intersecting has at most 2^{n-2} sets.

The next topic is spectral graph theory. We proved the Hoffman bound for the largest size of an independent set in a graph G.

I mentioned graph-Laplacians and the spectral bound for expansions (Alon-Milman, Tanner)..

The proofs mentioned above are so lovely that I will add them on this page, but sometime later.

Next week I will introduce harmonic analysis on the discrete cube and give a Fourier-theoretic explanation for  the additional log (1/t) factor in the edge isoperimetric inequality.

Important announcement: Real analysis boot camp in the Simons Institute for the Theory of Computing, is part of the program in Real Analysis and Computer Science. It is taking place next week on September 9-13 and has three lecture series. All lecture series are related to the topic of the course and especially:

Around Borsuk’s Conjecture 3: How to Save Borsuk’s conjecture

Borsuk asked in 1933 if every bounded set K of diameter 1 in R^d can be covered by d+1 sets of smaller diameter. A positive answer was referred to as the “Borsuk Conjecture,” and it was disproved by Jeff Kahn and me in 1993. Many interesting open problems remain.  The first two posts in the series “Around Borsuk’s Conjecture” are here and here. See also these posts (I,II,III, IV), and the post “Surprises in mathematics and theory” on Lipton and Reagan’s blog GLL.

Can we save the conjecture? We can certainly try, and in this post I would like to examine the possibility that Borsuk’s conjecture is correct except from some “coincidental” sets. The question is how to properly define “coincidental.”

Let K be a set of points in R^d and let A be a set of pairs of points in K. We say that the pair (K, A) is general if for every continuous deformation of the distances on A there is a deformation K’ of K which realizes the deformed distances.

(This condition is related to the “strong Arnold property” (aka “transversality”) in the theory of Colin de Verdière invariants of graphs; see also this paper  by van der Holst, Lovasz and Schrijver.)

Conjecture 1: If D is the set of diameters in K and (K,D) is general then K can be partitioned into d+1 sets of smaller diameter.

We propose also (somewhat stronger) that this conjecture holds even when “continuous deformation” is replaced with “infinitesimal deformation”.

The finite case is of special interest:

A graph embedded in R^d is stress-free if we cannot assign non-trivial weights to the edges so that the weighted sum of the edges containing any  vertex v (regarded as vectors from v) is zero for every vertex v. (Here we embed the vertices and regard the edges as straight line segments. (Edges may intersect.) Such a graph is called a “geometric graph”.) When we restrict Conjecture 1 to finite configurations of points we get.

Conjecture 2: If G is a stress free geometric graph of diameters in R^d  then G is (d+1)-colorable.

A geometric graph of diameters is a geometric graph with all edges having the same length and all non edged having smaller lengths. The attempt for “saving” the Borsuk Conjecture presented here and Conjectures 1 and 2 first appeared in a 2002 collection of open problems dedicated to Daniel J. Kleitman, edited by Douglas West.

When we consider finite configurations of points  we can make a similar conjecture for the minimal distances:

Conjecture 3: If the geometric graph of pairs of vertices realizing the minimal distances of a point-configuration in R^d is stress-free, then it is (d+1)-colorable.

We can speculate that even the following stronger conjectures are true:

Conjecture 4: If G is a stress-free geometric graph in R^d so that all edges in G are longer than all non-edges of G, then G is (d+1)-colorable.

Conjecture 5: If G is a stress-free geometric graph in R^d so that all edges in G are shorter than all non-edges of G, then G is (d+1)-colorable.

We can even try to extend the condition further so edges in the geometric graph will be larger (or smaller) than non-edges only just “locally” for neighbors of each given vertex.

Comments:

1) It is not true that every stress-free geometric graph in R^d is (d+1)-colorable, and not even that every stress-free unit-distance graph is (d+1)-colorable. Here is the (well-known) example referred to as the Moser Spindle. Finding conditions under which stress-free graphs in R^d are (d+1)-colorable is an interesting challenge.

MoserSpindle_700

2) Since a stress-free graph with n vertices has at most dn - {{d+1} \choose {2}} edges it must have a vertex of degree 2d-1 or less and hence it is 2d colorable. I expect this to be best possible but I am not sure about it. This shows that our “saved” version of Borsuk’s conjecture is of very different nature from the original one. For graphs of diameters in R^d the chromatic number can, by the work of Jeff and me be exponential in \sqrt d.

3) It would be interesting to show that conjecture 1 holds in the non-discrete case when  d+1 is replaced by 2d.

4) Coloring vertices of geometric graphs where the edged correspond to the minimal distance is related also the the well known Erdos-Faber-Lovasz conjecture.ERDSFA~1.

See also this 1994 article by Jeff Kahn on Hypergraphs matching, covering and coloring problems.

5) The most famous conjecture regarding coloring of graphs is, of course, the four-color conjecture asserting that every planar graph is 4-colorable that was proved by Appel and Haken in 1976.  Thinking about the four-color conjecture is always both fascinating and frustrating. An embedding for maximal planar graphs as vertices of a convex 3-dimensional polytope is stress-free (and so is, therefore, also a generic embedding), but we know that this property alone does not suffice for 4-colorability. Finding further conditions for  stress-free graphs in R^d that guarantee (d+1)-colorability can be relevant to the 4CT.

An old conjecture of mine asserts that

Conjecture 6: Let G be a graph obtained from the graph of a d-polytope P by triangulating each (non-triangular) face with non-intersecting diagonals. If G is stress-free (in which case the polytope P is called “elementary”) then G is (d+1)-colorable.

Closer to the conjectures of this post we can ask:

Conjecture 7: If G is a stress-free geometric graph in R^d so that for every edge  e of G  is tangent to the unit ball and every non edge of G intersect the interior of the unit ball, then G is (d+1)-colorable.

A question that I forgot to include in part I.

What is the minimum diameter d_n such that the unit ball in R^n can be covered by n+1 sets of smaller diameter? It is known that 2-C'\log n/n \le d_n\le 2-C/n for some constants C and C’.

Analysis of Boolean Functions – week 1

Home page of the course.

In the first lecture I defined the discrete n-dimensional cube and  Boolean functions. Then I moved to discuss five problems in extremal combinatorics dealing with intersecting families of sets.

1) The largest possible intersecting family of subsets of [n];

2) The largest possible intersecting family of subsets of [n] so that the family of complements is also intersecting;

3) The largest possible family of graphs on v vertices such that each two graphs in the family contains a common triangle;

4) Chvatal’s conjecture regarding the maximum size of an intersecting family of sets contained in an ideal of sets;

5) Erdos-Ko-Rado Theorem.

Exercise: Prove one of the following

a) The Harris-Kleitman’s inequality

b) (from the H-K inequality) Every family of subsets of [n] with the property that every two sets have non-empty intersection and no full union contains at most 2^{n-2} sets.

More reading: this post :”Extremal combinatorics I: extremal problems on set systems“. Spoiler: The formulation of Chvatal’s conjecture but also the answer to the second exercise can be found on this post: Extremal combinatorics III: some basic theorems. (See also peoblen 25 in the 1972 paper Selected combinatorial research problems by Chvatal, Klarner and Knuth.)

feige

I moved to discuss the problem of collective coin flipping and the notion of influence as defined by Ben-Or and Linial. I mentioned the Baton-passing protocol, the Alon-Naor result, and Feige’s two-rooms protocol.

More reading: this post :” Nati’s influence“. The original paper of Ben-Or Linial:  Collective coin flipping, M. Ben-Or  and N. Linial, in “Randomness and    Computation” (S. Micali ed.) Academic Press, New York, 1989, pp.    91-115.

Poznań: Random Structures and Algorithms 2013

michal  photo

Michal Karonski (left) who built Poland’s probabilistic combinatorics group at Poznań, and a sculpture honoring the Polish mathematicians who first broke the Enigma machine (right, with David Conlon, picture taken by Jacob Fox).

I am visiting now Poznań for the 16th Conference on Random Structures and Algorithms. This bi-annually series of conferences started 30 years ago (as a satellite conference to the 1983 ICM which took place in Warsaw) and this time there was also a special celebration for Bela Bollobás 70th birthday. I was looking forward to  this first visit to Poland which is, of course, a moving experience for me. Before Poznań I spent a few days in Gdańsk visiting Robert Alicki. Today (Wednesday)  at the Poznań conference I gave a lecture on threshold phenomena and here are the slides. In the afternoon we had the traditional random run with a record number of runners. Let me briefly tell you about very few of the other lectures: Update (Thursday): A very good day, and among others a great talk of Jacob Fox on Relative Szemeredi Theorem (click for the slides from a similar talk from Budapest) where he presented a joint work with David Conlon and Yufei Zhao giving a very general and strong form of Szemeredi theorem for quasi-random sparse sets, which among other applications, leads to a much simpler proof of the Green -Tao theorem.

Mathias Schacht

Mathias Schacht gave a wonderful talk  on extremal results in random graphs (click for the slides) which describes some large recent body of highly successful research on the topic. Here are two crucial slides, and going through the whole presentation can give a very good overall picture. ms1 mt2

Vera Sós

Vera Sós gave an inspiring talk about the random nature of graphs which are extremal to the Ramsey property and connections with graph limits. Vera presented the following very interesting conjecture on graph limits. We say that a sequence of graphs (G_n) has a limit if for every k and every graph H with k vertices the proportion in G_n of induced H-subgraphs among all k-vertex induced subgraphs tend to a limit. Let us also say that (G_n) has a V-limit if for every k and every e the proportion in G_n of induced subgraphs with k vertices and e edges among all k-vertex induced subgraphs tend to a limit. Sós’ question: Is having a V-limit equivalent to having a limit. This is open even in the case of quasirandomness, namely, when the limit is given by the Erdos-Renyi model G(n,p). (Update: in this case V-limit is equivalent to limit, as several participants of the conference observed.) Both a positive and a negative answer to this fundamental question would lead to many further (different) open problems.

Joel Spencer

Joel Spencer gave a great (blackboard) talk about algorithmic aspects of the probabilistic method, and how existence theorems via the probabilistic method now often require complicated randomized algorithm. Joel mentioned his famous six standard deviation theorem. In this case, Joel conjectured thirty years ago that there is no efficient algorithm to find the coloring promised by his theorem. Joel was delighted to see his conjecture being refuted first by Nikhil Bansal (who found an algorithm whose proof depends on the theorem) and then later by Shachar Lovett and  Raghu Meka (who found a new algorithm giving a new proof) . In fact, Joel said, having his conjecture disproved is even more delightful than having it proved. Based on this experience Joel and I are now proposing another conjecture: Kalai-Spencer (pre)conjecture: Every existence statement proved by the probabilistic method can be complemented by an efficient (possibly randomized) algorithm. By “complemented by an efficient algorithm” we mean that there is an efficient(polynomial time)  randomized algorithm to create the promised object with high probability.  We refer to it as a preconjecture since the term “the probabilistic method” is not entirely well-defined. But it may be possible to put this conjecture on formal grounds, and to discuss it informally even before.

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

wilson  curvature

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.

Some old and new problems in combinatorics and geometry

Paul99

Paul Erdős in Jerusalem, 1933  1993

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)+1Then 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.

Erdős-Ko-Rado theorem meets Catalan

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

Andriy Bondarenko Showed that Borsuk’s Conjecture is False for Dimensions Greater Than 65!

The news in brief

Andriy V. Bondarenko proved in his remarkable paper The Borsuk Conjecture for two-distance sets  that the Borsuk’s conjecture is false for all dimensions greater than 65. This is a substantial improvement of the earlier record (all dimensions above 298) by Aicke Hinrichs and Christian Richter.

Borsuk’s conjecture

Borsuk’s conjecture asserted that every set of diameter 1 in d-dimensional Euclidean space can be covered by d+1 sets of smaller diameter. (Here are links to a post describing the disproof by Kahn and me  and a post devoted to problems around Borsuk’s conjecture.)

Two questions posed by David Larman

David Larman posed in the ’70s two basic questions about Borsuk’s conjecture:

1) Does the conjecture hold for collections of 0-1 vectors (of constant weight)?

2) Does the conjecture hold for 2-distance sets? 2-distance sets are sets of points such that the pairwise distances between any two of them have only two values.

Reducing the dimensions for which Borsuk’s conjecture fails

In 1993 Jeff Kahn and I disproved Borsuk’s conjecture in dimension 1325 and all dimensions greater than 2014. Larman’s first conjecture played a special role in our work.   While being a special case of Borsuk’s conjecture, it looked much less correct.

The lowest dimension for a counterexample were gradually reduced to  946 by A. Nilli, 561 by A. Raigorodskii, 560 by  Weißbach, 323 by A. Hinrichs and 320 by I. Pikhurko. Currently the best known result is that Borsuk’s conjecture is false for n ≥ 298; The two last papers relies strongly on the Leech lattice.

Bondarenko proved that the Borsuk’s conjecture is false for all dimensions greater than 65.  For this he disproved Larman’s second conjecture.

Bondarenko’s abstract

In this paper we answer Larman’s question on Borsuk’s conjecture for two-distance sets. We found a two-distance set consisting of 416 points on the unit sphere in the dimension 65 which cannot be partitioned into 83 parts of smaller diameter. This also reduces the smallest dimension in which Borsuk’s conjecture is known to be false. Other examples of two-distance sets with large Borsuk’s numbers will be given.

Two-distance sets

There was much interest in understanding sets of points in R^n  which have only two pairwise distances (or K pairwise distances). Larman, Rogers and Seidel proved that the maximum number can be at most (n+1)(n+4)/2 and Aart Blokhuis improved the bound to (n+1)(n+2)/2. The set of all 0-1 vectors of length n+1 with two ones gives an example with n(n+1)/2 vectors.

Equiangular lines

This is a good opportunity to mention another question related to two-distance sets. Suppose that you have a set of lines through the origin in R^n so that the angles between any two of them is the same. Such  a set is  called an equiangular set of lines. Given such a set of cardinality m, if we take on each line one unit vector, this gives us a 2-distance set. It is known that m ≤ n(n+1)/2 but for a long time it was unknown if a quadratic set of equiangular lines exists in high dimensions. An exciting breakthrough came in 2000 when Dom deCaen constructed a set of equiangular lines in R^n with 2/9(n+1)^2 lines for infinitely many values of n.

Strongly regular graphs

Strongly regular graphs are central in the new examples. A graph is strongly regular if every vertex has k neighbors, every adjacent pair of vertices have λ common neighbors and every non-adjacent pair of vertices have μ common neighbors. The study of strongly regular graphs (and other notions of strong regularity/symmetry) is a very important area in graph theory which involves deep algebra and geometry. Andriy’s construction is based on a known strongly regular graph G_2(4).