TITLE: Gradient-based Adaptive Stochastic Search

SPEAKER: Enlu Zhou

ABSTRACT:

Gradient-based adaptive stochastic search (GASS) is an algorithm for solving general optimization problems with little structure. GASS iteratively finds high quality solutions by randomly sampling candidate solutions from a parameterized distribution model over the solution space. The basic idea is to convert the original (possibly non-continuous, non-differentiable) problem into a differentiable optimization problem on the parameter space of the parameterized sampling distribution, and then use a direct gradient search method to find improved sampling distributions. Thus, GASS combines the robustness feature of stochastic search by considering a population of candidate solutions with the relative fast convergence speed of classical gradient methods. The convergence and converge rate properties of the algorithm are analyzed. The performance of the algorithm is illustrated on a number of benchmark problems and a resource allocation problem in communication networks.

 Bio: 

Enlu Zhou received the B.S. degree with highest honors in electrical engineering from Zhejiang University, China, in 2004, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park, in 2009. Since then she has been an Assistant Professor at the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign. Her research interests include simulation optimization, Markov decision processes, and Monte Carlo statistical methods. She is a recipient of the “Best Theoretical Paper” award at the 2009 Winter Simulation Conference and the 2012 AFOSR Young Investigator award.