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Gradient-based Adaptive Stochastic Search (GASS)

Enlu Zhou

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 distribution, and then use a direct gradient search method to find improved 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 performance of the algorithm is illustrated on a number of benchmark problems and a resource allocation problem in communication networks. If time permits, I will also talk about the extension of GASS to simulation optimization problems, where the objective function can only be evaluated through a stochastic simulation model.


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. From 2009-2013 she was an assistant professor in the Industrial & Enterprise Systems Engineering Department at the University of Illinois Urbana-Champaign. Since 2013 she has been an assistant professor in the H. Milton School of Industrial & Systems Engineering at Georgia Institute of Technology. Her research interests include Markov decision processes, simulation optimization, and Monte Carlo statistical methods, with applications in financial engineering and revenue management. She is a recipient of the “Best Theoretical Paper” award at the 2009 Winter Simulation Conference, the 2012 AFOSR Young Investigator award, and the 2014 NSF CAREER award.