Algorithms for Deterministically Constrained Stochastic Optimization

Frank E. Curtis
Lehigh University

I will present the recent work by my research group on the design, analysis, and implementation of algorithms for solving nonlinear optimization problems that involve a stochastic objective function and deterministic constraints. The talk will focus on our sequential quadratic optimization (commonly known as SQP) methods for cases when the constraints are defined by nonlinear systems of equations, which arise in various applications including optimal control, PDE-constrained optimization, and network optimization problems. One might also consider our techniques for training machine learning (e.g., deep learning) models with constraints. I will also discuss the various extensions that my group is exploring along with other related open questions.