Time: 1:30 p.m. - 2:30 p.m., Tuesday, March 3, 2026

Venue: Room 524, Siyuan Faculty Building

Topic: A Penalty-Based Method for Stochastically Constrained Simulation Optimization

Speaker: Professor Jiaqiao Hu (Stony Brook University, State University of New York)

Host: Professor Jianqiang Hu (School of Management at Fudan University)

Abstract: We propose a penalty-based method for solving stochastically constrained simulation optimization problems with differentiable structure. We introduce a novel class of penalty functions and reformulate the original constrained problem as a sequence of unconstrained subproblems, which are approximately solved using a multi-timescale stochastic gradient descent framework. The method combines features of both exterior and interior penalty approaches: it acts like an exterior method to guide an infeasible solution toward the feasible region and transitions to an interior method once feasibility is achieved. In contrast to existing methods, the approach operates fully online, eliminates the need for explicit feasibility checks, and requires no auxiliary optimization subroutines during the iterative process. Under suitable assumptions, we establish almost sure convergence of the algorithm and derive a convergence rate bound via a bias-variance decomposition of the stochastic gradient estimators.

Bio: Jiaqiao Hu is a Professor in the Department of Applied Mathematics and Statistics at the State University of New York, Stony Brook. He received the B.E. degree in automation from Shanghai Jiao Tong University, the M.S. degree in applied mathematics from the University of Maryland, Baltimore County, and the Ph.D. degree in electrical engineering from the University of Maryland, College Park. His research interests include Markov Decision Processes, simulation optimization, and stochastic modeling and analysis.