Events - November 2022

Title: Integer programs with bounded subdeterminants and two nonzeros per row

Abstract: Determining the complexity of integer linear programs with integer coefficient matrices whose subdeterminants are bounded by a constant is currently a very actively discussed question in the field. In this talk, I will present a strongly polynomial-time algorithm for such integer programs with the further requirement that every constraint contains at most two variables. The core of our approach is the first polynomial-time algorithm for the weighted stable set problem on graphs that do not contain more than k vertex-disjoint odd cycles, where k is any constant. Previously, polynomial-time algorithms were only known for k = 0 (bipartite graphs) and for k = 1.

This is joint work with Samuel Fiorini, Gwenaël Joret, and Yelena Yuditsky, which recently appeared at FOCS this year.

Title: Asymptotics of the Euler characteristic of Kontsevich's commutative graph complex

Abstract: I will present results on the asymptotic growth rate of the Euler characteristic of Kontsevich's commutative graph complex. By a work of Chan-Galatius-Payne, these results imply the same asymptotic growth rate for the top-weight Euler characteristic of M_g, the moduli 
space of curves, and establish the existence of large amounts of unexplained cohomology in this space. This asymptotic growth rate 
follows from new generating functions for the edge-alternating sum of graphs without odd automorphisms. I will give an overview on this 
interaction between topology and combinatorics and illustrate the combinatorial and analytical tools that were needed to obtain these 
generating functions.

Title: Approximating Weighted Connectivity Augmentation below Factor 2

Abstract:  The Weighted Connectivity Augmentation Problem (WCAP) asks to increase the edge-connectivity of a graph in the cheapest possible way by adding edges from a given set. It is one of the most elementary network design problems for which no better-than-2 approximation algorithm has been known, whereas 2-approximations can be easily obtained through a variety of well-known techniques.

Title: Algorithms for Analytic Combinatorics in Several Variables

Abstract: In this presentation we will see how to apply the theory of complex analysis to study multivariate generating series by looking at several examples. Specifically, given a rational bivariate generating function G(x, y)/H(x, y) with coefficients f_{i, j} the objective is algorithmically determine asymptotic formulas to approximate f_{rn, sn} as n goes to infinity, for fixed positive integers r and s.

Title: Graphs, curvature, and local discrepancy

Abstract: Spectral graph theory, the use of eigenvalues to study graphs, gives an important window into many properties of graphs.  One of the reasons for this is that the eigenvalues can be used to certify the `pseudo-randomness' of the edge set of a graph.  In recent years, several notions of discrete curvature have been introduced that gives a 'local' way (depending on the neighborhood structure of vertices) to study some of the same properties that eigenvalues can capture. 

Title: Stochastic Minimum Norm Combinatorial Optimization

Abstract:  In this work, we introduce and study stochastic minimum-norm optimization. We have an underlying combinatorial optimization problem where the costs involved are random variables with given distributions; each feasible solution induces a random multidimensional cost vector. The goal is to find a solution that minimizes the expected norm of the induced cost vector, for a given monotone, symmetric norm. We give a framework for designing approximation algorithms for stochastic minimum-norm optimization and apply it to give approximation algorithms for stochastic minimum-norm versions of load balancing and spanning tree problems.

Title: Cayley graphs, association schemes and state transfer

Abstract: The aim of this talk is to give some examples of how association schemes can be used as a tool to study certain properties of Cayley graphs. In particular, they contribute to our long-term goal of characterizing perfect state transfer in Cayley graphs. The talk is based on the following paper https://arxiv.org/abs/2204.09802.

Title: Approximation Algorithms for Stochastic Knapsack 

Abstract: The classical Knapsack problem takes as input a set of items with some fixed nonnegative values and weights. The goal is to compute a subset of items of maximum total value, subject to the constraint that the total weight of these elements is at most a given limit. In this talk we review a paper by Gupta, Krishnaswamy, Molinaro and Ravi, in which the following stochastic variation of this problem is considered: the value and weight of each item are correlated random variables with known, arbitrary distributions.