Keynote: Stochastic Algorithms for Nonlinearly Constrained Optimization

Abstract: I will present the latest contributions of my research group on the design and analysis of stochastic algorithms for solving nonlinearly constrained optimization problems. The signature feature of our algorithms is that they handle constraints as constraints, rather than through penalty or augmented Lagrangian functions. I will discuss how we have seen that our algorithms can accelerate the performance of stochastic algorithms for constrained optimization and informed learning, and discuss various extensions that we have explored of our core algorithmic methodology. I will close with various open questions that remain to be explored.

Short Bio: Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh 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 U.S. Department of Energy (DoE), and has received funding from the U.S. National Science Foundation (NSF). He received the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization. He was awarded the 2018 INFORMS Computing Society Prize. He currently serves as Area Editor for Continuous Optimization for Mathematics of Operations Research and serves as an Associate Editor for Mathematical Programming, SIAM Journal on Optimization, and Mathematical Programming Computation.