Speaker: Frank Curtis, Professor at Lehigh University

Title: "Deterministically Constrained Stochastic Optimization"

Abstract: Curtis will present the recent work by his research group on the design, analysis, and implementation of algorithms for solving continuous nonlinear optimization problems that involve a stochastic objective function and deterministic constraints. The talk will focus on sequential quadratic optimization (commonly known as SQP) methods for cases when the constraints are defined by nonlinear systems of equations and inequalities. These methods are applicable for solving various types of problems, such as for training machine learning (e.g., deep learning) models with constraints. His work focuses on the "fully stochastic" regime in which only stochastic gradient estimates are employed, for which we have derived convergence-in-expectation results and worst-case iteration complexity bounds that are on par with stochastic gradient methods for the unconstrained setting. Curtis will also discuss the various extensions that his group is exploring.

Bio: Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University. Prior to joining Lehigh, he received his bachelor's degree from the College of William and Mary, received his master's and doctoral degrees from the Department of Industrial Engineering and Management Science at Northwestern University, and worked as a Postdoctoral Researcher in the Courant Institute of Mathematical Sciences at New York University. His research focuses on the design, analysis, and implementation of numerical methods for solving large-scale nonlinear optimization problems. He received an Early Career Award from the Advanced Scientific Computing Research program of the U.S. Department of Energy, and has had other funded projects with the U.S. National Science Foundation, Office of Naval Research, and Advanced Research Projects Agency - Energy. He received, along with Leon Bottou (Facebook AI Research) and Jorge Nocedal (Northwestern), the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization. He was awarded, with James V. Burke (U. of Washington), Adrian Lewis (Cornell), and Michael Overton (NYU), the 2018 INFORMS Computing Society Prize. He and team members Daniel Molzahn (Georgia Tech), Andreas Waechter (Northwestern), Ermin Wei (Northwestern), and Elizabeth Wong (UC San Diego) were awarded second place in the ARPA-E Grid Optimization Competition in 2020. He currently serves as an Associate Editor for Mathematical Programming, SIAM Journal on Optimization, Mathematics of Operations Research, IMA Journal of Numerical Analysis, and Mathematical Programming Computation. He previously served as the Vice Chair for Nonlinear Programming for the INFORMS Optimization Society and is currently very active in professional societies and groups related to mathematical optimization, including INFORMS, the Mathematics Optimization Society, and the SIAM Activity Group on Optimization.

The Departmental Seminar Series is open to all. U-M Industrial and Operations Engineering graduate students and faculty are especially encouraged to attend. There will be a reception to follow the seminar in the IOE commons from 4 -5 p.m