Georgia Institute of Technology College of Sciences

With applications in the analysis of political districtings, Markov chains have become and essential tool for studying contiguous partitions of geometric regions. Nevertheless, there remains a dearth of rigorous results on the mixing times of the chains employed for this purpose. In this talk we'll discuss a sub-exponential bound on the mixing time of the Glauber dynamics chain for the case of bounded-size contiguous partition classes on certain grid-like classes of graphs.

In this talk, using a technique introduced by P.~T.~Nam in 2012 and the Coulomb Uncertainty Principle, I will present the proof of new bounds on the excess charge for non relativistic  atomic systems, independent of the particle statistics. These new bounds are the best bounds to date for bosonic systems. This is joint work with Juan Manel González and Trinidad Tubino.

Join Zoom Meeting: https://gatech.zoom.us/j/94786316294

We will discuss two types of structured multi-objective optimization programs.  In the first, the goal is to minimize a sum of functions described by a graph: each function is associated with a vertex, and there is an edge between vertices if two functions share a subset of their variables.   Problems of this type arise in state estimation problems, including simultaneous localization and mapping (SLAM) in robotics, tracking, and streaming reconstruction problems in signal processing.  We will show that under mild smoothness conditions, these types of problems exhibit a type of locality: if a node is added to the graph (changing the optimization problem), the optimal solution changes only for variables that are ``close’’ to the added node, immediately giving us a quick way to update the solution as the graph grows.

In the second part of the talk, we will consider a multi-task learning problem where the solutions are expected to lie in a low-dimensional subspace.  This corresponds to a low-rank matrix recover problem where the columns of the matrix have been ``sketched’’ independently.  We show that a novel convex relaxation of this problem results in optimal sample complexity bounds.  These bounds demonstrate the statistical leverage we gain by solving the problem jointly over solving each individually.