Seminars and Colloquia by Series

Series: ACO Seminar
Friday, January 20, 2017 - 15:05 , Location: Skiles 005 , Dion Gijswijt , TU Delft , Organizer: Esther Ezra
A subset of $\mathbb{F}_3^n$ is called a \emph{cap set} if it does not contain three vectors that sum to zero. In dimension four, this relates to the famous card game SET: a cap set corresponds to a collection of cards without a SET. The cap set problem is concerned with upper bounds on the size of cap sets. The central question raised by Frankl, Graham and R\”odl is: do cap sets have exponentially small density? May last year, this question was (unexpectedly) resolved in a pair of papers by Croot, Lev, and Pach and by Ellenberg and myself in a new application of the polynomial method. The proof is surprisingly short and simple. 
Series: ACO Seminar
Tuesday, November 15, 2016 - 13:30 , Location: Skiles 005 , Lutz Warnke , Cambridge University and Georgia Tech , Organizer: Robin Thomas
Concentration inequalities are fundamental tools in probabilistic combinatorics and theoretical computer science for proving that functions of random variables are typically near their means. Of particular importance is the case where f(X) is a function of independent random variables X=(X_1,...,X_n). Here the well-known bounded differences inequality (also called McDiarmid's or Hoeffding--Azuma inequality) establishes sharp concentration if the function f does not depend too much on any of the variables. One attractive feature is that it relies on a very simple Lipschitz condition (L): it suffices to show that |f(X)-f(X')| \leq c_k whenever X,X' differ only in X_k. While this is easy to check, the main disadvantage is that it considers worst-case changes c_k, which often makes the resulting bounds too weak to be useful. In this talk we discuss a variant of the bounded differences inequality which can be used to establish concentration of functions f(X) where (i) the typical changes are small although (ii) the worst case changes might be very large. One key aspect of this inequality is that it relies on a simple condition that (a) is easy to check and (b) coincides with heuristic considerations as to why concentration should hold. Indeed, given a `good' event G that holds with very high probability, we essentially relax the Lipschitz condition (L) to situations where G occurs. The point is that the resulting typical changes c_k are often much smaller than the worst case ones. If time permits, we shall illustrate its application by considering the reverse H-free process, where H is 2-balanced. We prove that the final number of edges in this process is concentrated, and also determine its likely value up to constant factors. This answers a question of Bollobás and Erdös.
Series: ACO Seminar
Tuesday, November 3, 2015 - 16:30 , Location: Skiles 005 , Adam Marcus , Mathematics and PACM, Princeton University , Organizer: Prasad Tetali
Recent work of the speaker with Dan Spielman and Nikhil Srivastava introduced the ``method of interlacing polynomials'' (MOIP) for solving problems in combinatorial linear algebra. The goal of this talk is to provide insight into the inner workings of the MOIP by introducing a new theory that reveals an intimate connection between the use of polynomials in the manner of the MOIP and free probability, a theory developed by Dan Voiculescu as a tool in the study of von Neumann algebras. I will start with a brief introduction to free probability (for those, like me, who are not operator theorists). In particular, I will discuss the two basic operations in free probability theory (the free additive and free multiplicative convolutions), and how they relate to the asymptotic eigenvalue distributions of random matrices. I will then show how certain binary operations on polynomials act as finite analogues of the free convolutions and how the MOIP is effectively transferring the asymptotic bounds obtained in free probability to bounds in the new theory (which can then be applied to finite scenarios). If time permits, I will show how such a theory gives far better intuition as to how one might apply the MOIP in the future, using recent results on restricted invertibility and the existence of Ramanujan graphs as examples.
Series: ACO Seminar
Monday, October 5, 2015 - 13:05 , Location: Skiles 006 , Amir Dembo , Stanford University , Organizer: Prasad Tetali
 The Max-Cut problem seeks to determine the maximal cut size in a given graph. With no polynomial-time efficient approximation for Max-Cut (unless P=NP), its asymptotic for a typical large sparse graph is of considerable interest. We prove that for uniformly random d-regular graph of N vertices, and for the uniformly chosen Erdos-Renyi graph of M=N d/2 edges, the leading correction to M/2 (the typical cut size), is P_* sqrt(N M/2). Here P_* is the ground state energy of the Sherrington-Kirkpatrick model, expressed analytically via Parisi's formula. This talk is based on a joint work with Subhabrata Sen and Andrea Montanari.   
Series: ACO Seminar
Monday, April 7, 2014 - 13:00 , Location: Klaus 1116 , Allan Borodin , University of Toronto , Organizer: Robin Thomas
We are generally interested in the following ill-defined problem: What is a conceptually simple algorithm and what is the power and limitations of such algorithms? In particular, what is a greedy algorithm or more generally a myopic algorithm for a combinatorial optimization problem? And to be even more specific, motivated by the Buchbinder et al ``online double sided greedy algorithm'' for the unconstrained non-monotone submodular maximization problem, what are (if any) the limitations of algorithms "of this genre" for the general unconstrained problem and for specific instances of the problem, such as Max-Di-Cut?Joint work with Norman Huang.
Series: ACO Seminar
Monday, March 31, 2014 - 16:05 , Location: Skiles 006 , Daniel Dadush , New York University , Organizer: Robin Thomas
The closest vector problem (CVP) on lattices (i.e. given an n dimensional lattice L with basis B and target point t, find a closest lattice point in L to t) is fundamental NP-hard problem with applications in coding, cryptography & optimization. In this talk, we will consider the preprocessing version of CVP (CVPP), an important variant of CVP, where we allow arbitrary time to preprocess the lattice before answering CVP queries. In breakthrough work, Micciancio & Voulgaris (STOC 2010) gave the first single exponential time algorithm for CVP under the l_2 norm based on Voronoi cell computations. More precisely, after a preprocessing step on L requiring tilde{O}(2^{2n}) time, during which they compute the Voronoi cell of L (the set of points closer to the origin than to any other point in L), they show that additional CVP queries on L (i.e. CVPP) can be solved in tilde{O}(2^{2n}) time. For our main result, we show that given the Voronoi cell V of L as preprocessing, CVP on any target t can be solved in expected tilde{O}(2^n) time. As our main technical contribution, we give a new randomized procedure that starting from any close enough lattice point to the target t, follows a path in the Voronoi graph of L (i.e. x,y in L are adjacent if x+V and y+V share a facet) to the closest lattice vector to t of expected polynomial size. In contrast, the path used by MV algorithm is only known to have length bounded by tilde{O}(2^n). Furthermore, for points x,y in L, we show that the distance between x and y in the Voronoi graph is within a factor n of ||x-y||_V (norm induced by the Voronoi cell), which is best possible. For our analysis, we rely on tools from high dimensional convex geometry. No background in convex geometry or lattices will be assumed. Time permitting, I will describe related results & open questions about paths on more general lattice Cayley graphs. Joint work with Nicolas Bonifas (Ecole Polytechnique).
Series: ACO Seminar
Wednesday, February 5, 2014 - 12:00 , Location: IC 209 , Marco Molinaro , Georgia Tech , Organizer: Emma Cohen

Joint DOS-ACO Seminar. Food and refreshments will be provided.

Sparse cutting-planes are often the ones used in mixed-integer programing (MIP) solvers, since they help in solving the linear programs encountered during branch-&-bound more efficiently. However, how well can we approximate the integer hull by just using sparse cutting-planes? In order to understand this question better, given a polyope P (e.g. the integer hull of a MIP), let P^k be its best approximation using cuts with at most k non-zero coefficients. We consider d(P, P^k) = max_{x in P^k} (min_{y in P} |x - y|) as a measure of the quality of sparse cuts. In our first result, we present general upper bounds on d(P, P^k) which depend on the number of vertices in the polytope and exhibits three phases as k increases. Our bounds imply that if P has polynomially many vertices, using half sparsity already approximates it very well. Second, we present a lower bound on d(P, P^k) for random polytopes that show that the upper bounds are quite tight. Third, we show that for a class of hard packing IPs, sparse cutting-planes do not approximate the integer hull well. Finally, we show that using sparse cutting-planes in extended formulations is at least as good as using them in the original polyhedron, and give an example where the former is actually much better. Joint work with Santanu Dey and Qianyi Wang.
Series: ACO Seminar
Tuesday, November 26, 2013 - 14:05 , Location: Skiles 005 , Christian Borgs , Microsoft Research (New England), Cambridge, MA , borgs@microsoft.com , Organizer: Prasad Tetali
  Many real-world graphs  are large and growing.  This naturally raises the question of a suitable concept of graph convergence.  For graphs with average degree proportional to the number of vertices (dense graphs), this question is by now quite well-studied.  But real world graphs tend to be sparse, in the sense that the average or even the maximal degree is bounded by some reasonably small constant.  In this talk, I study several notions of convergence for graphs of bounded degree and show that, in contrast to dense graphs, where various a priori different notions of convergence are equivalent, the corresponding notions are not equivalent for sparse graphs.  I then describe a notion of convergence formulated in terms of a large deviation principle which implies all previous notions of convergence.
Series: ACO Seminar
Monday, February 11, 2013 - 16:00 , Location: Klaus 1116 W , Vijay V. Vazirani , School of Computer Science, Georgia Tech , Organizer:
For all practical purposes, the Micali-Vazirani algorithm, discovered in 1980, is still the most efficient known maximum matching algorithm (for very dense graphs, slight asymptotic improvement can be obtained using fast matrix multiplication). However, this has remained a "black box" result for the last 32 years. We hope to change this with the help of a recent paper giving a simpler proof and exposition of the algorithm: http://arxiv.org/abs/1210.4594 In the interest of covering all the ideas, we will assume that the audience is familiar with basic notions such as augmenting paths and bipartite matching algorithm.
Series: ACO Seminar
Tuesday, May 22, 2012 - 11:05 , Location: Skiles 005 , Peter Winkler , Dartmouth College, Hanover, NH , Organizer: Prasad Tetali
   We derive optimal strategies for a pursuit-and-evasion game and show that when pitted against each other, the two strategies construct a small set containing unit-length line segments at all angles. Joint work with Y. Babichenko, Y. Peres, R. Peretz, and P. Sousi.

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