Seminar Series | Stochastic and Hierarchical Multi-Agent Optimization

Speaker: Farzad Yousefian, Rutgers University

Title: Stochastic and Hierarchical Multi-Agent Optimization

Abstract: We present mathematical models and provably convergent algorithms for addressing the presence of hierarchy in large-scale multi-agent optimization. This includes the following avenues:

(i) Computation of the best and worst equilibrium: In noncooperative Nash games, equilibria are known to be inefficient. This is exemplified by the Prisoner's Dilemma and was first provably shown in 1980s. We present single-timescale decentralized optimization methods for computing the best Nash equilibrium. We also discuss a method to compute the worst equilibrium.

(ii) Stochastic Mathematical Programs with Equilibrium Constraints (MPECs): We consider stochastic variants of MPECs that find broad applicability in engineering and naval research. We develop a class of randomized zeroth-order smoothing-enabled methods. We derive new complexity guarantees for solving stochastic MPECs, in both single-stage and two-stage cases. Preliminary numerics suggest that the new schemes scale with problem size and provide solutions of similar accuracy in a fraction of the time taken by existing methods.

Bio: Farzad Yousefian is an Assistant Professor in the Department of Industrial and Systems Engineering at Rutgers University. He received his Ph.D. in Industrial Engineering from the University of Illinois at Urbana-Champaign. His research interest lies in distributed optimization in multi-agent networks, stochastic and large-scale optimization, nonconvex optimization, hierarchical optimization, and computational game theory. His research has been funded by the National Science Foundation (NSF) Faculty Early Career Development (CAREER) award, the Office of Naval Research (ONR), and the Department of Energy (DOE). He is a recipient (jointly with his co-authors) of the Best Theoretical Paper award at the 2013 Winter Simulation Conference (WSC).