Join us in welcoming Frank Curtis, professor and director of graduate studies in industrial engineering at Lehigh University, as he discusses his research. His research focuses on designing, analyzing, and implementing numerical methods for solving large-scale nonlinear optimization problems.
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Stochastic Algorithms for Solving Constrained Optimization Problems
I will present recent work by my research group on solving constrained continuous optimization problems with stochastic algorithms. I will provide an overview of a suite of sequential quadratic optimization (commonly known as SQP) algorithms that we have proposed, analyzed, and tested for which we have convergence-in-expectation and worst-case complexity guarantees. I will also discuss recent work on the design and analysis of a stochastic interior-point method, which, as far as we are aware, is the first of its kind. Our algorithms are motivated by various applications, including fair and domain-informed machine learning.
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 algorithms for solving large-scale optimization problems. He received an Early Career Award from the ASCR program of the U.S. Department of Energy and has received funding from various programs of the U.S. National Science Foundation as well as from the Office of Naval Research. He received, along with Leon Bottou (Facebook AI) and Jorge Nocedal (Northwestern), the 2021 SIAM/MOS Lagrange Prize in Continuous Optimization. He was awarded, with James V. Burke (Washington), Adrian Lewis (Cornell), and Michael Overton (NYU), the 2018 INFORMS Computing Society Prize. He currently serves as Area Editor for Continuous Optimization for Mathematics of Operations Research and as an Associate Editor for Mathematical Programming, SIAM Journal on Optimization, IMA Journal of Numerical Analysis, and Mathematical Programming Computation.
