Georgia Institute of Technology College of Sciences

A systematic study of large combinatorial objects has recently led to discovering many connections between discrete mathematics and analysis. In this talk, we apply analytic methods to permutations. In particular, we associate every sequence of permutations with a measure on a unit square and show the following: if the density of every 4-element subpermutation in a permutation p is 1/4!+o(1), then the density of every k-element subpermutation is 1/k!+o(1). This answers a question of Graham whether quasirandomness of a permutation is captured by densities of its 4-element subpermutations. The result is based on a joint work with Oleg Pikhurko.

We show that any internally 4-connected non-planar bipartite graph contains a subdivision of K3,3 in which each subdivided path contains an even number of vertices. In addition to being natural, this result has broader applications in matching theory: for example, finding such a subdivision of K3,3 is the first step in an algorithm for determining whether or not a bipartite graph is Pfaffian. This is joint work with Robin Thomas.

We present a dynamic data structure representing a graph G, which allows addition and removal of edges from G and can determine the number of appearances of a graph of a bounded size as an induced subgraph of G. The queries are answered in constant time. When the data structure is used to represent graphs from a class with bounded expansion (which includes planar graphs and more generally all proper classes closed on topological minors, as well as many other natural classes of graphs with bounded average degree), the amortized time complexity of updates is polylogarithmic. This data structure is motivated by improving time complexity of graph coloring algorithms and of random graph generation.

The online matching problem has received significant attention in recent years because of its connections to allocation problems in internet advertising, crowd sourcing, etc. In these real-world applications, the typical goal is not to maximize the number of allocations; rather it is to maximize the number of “successful” allocations, where success of an allocation is governed by a stochastic event that comes after the allocation. These applications motivate us to introduce stochastic rewards in the online matching problem. In this talk, I will formally define this problem, point out its connections to previously studied allocation problems, give a deterministic algorithm that is close to optimal in its competitive ratio, and describe some directions of future research in this line of work. (Based on joint work with Aranyak Mehta.)