Colloquium Details

Stochastic Algorithms for Solving Nonlinearly Constrained Continuous Optimization Problems

Speaker: Frank E. Curtis, Lehigh University

Location: 60 Fifth Avenue Room 150

Date: March 29, 2024, 11:30 a.m.

Host: Benjamin Peherstrofer


This talk focuses on the recent work of my research group on the design and analysis of stochastic algorithms for solving nonlinearly constrained continuous optimization problems arising in areas such as optimization under uncertainty and informed machine learning. These algorithms aim to capitalize on the advantages of stochastic-gradient-based methods while offering a better means for enforcing "hard constraints" than those offered by regularization/penalty methods. We have been developing multiple classes of algorithms with convergence guarantees (e.g., almost-sure convergence to primal-dual stationarity) and promising empirical performance that can be employed in settings when the numbers of data points and optimization variables are large. The talk will cover the challenges that we have overcome so far and the next set of open questions that we will answer.

Speaker Bio:

Frank E. Curtis is a Professor in the Department of Industrial and Systems Engineering at Lehigh University. He received a bachelor’s degree with a double major in Computer Science and Mathematics from the College of William and Mary in 2003, received a master’s degree in 2004 and Ph.D. degree in 2007 from the Department of Industrial Engineering and Management Science at Northwestern University, and spent two years as a Postdoctoral Researcher in the Courant Institute of Mathematical Sciences at New York University from 2007 until 2009. His research focuses on the design, analysis, and implementation of algorithms for solving large-scale nonlinear optimization problems. He received an Early Career Award from the Advanced Scientific Computing Research (ASCR) program of the U.S. Department of Energy, and has received funding from various programs of the U.S. National Science Foundation (NSF), including a TRIPODS Phase I grant.  His work has also been funded by the U.S. Office of Naval Research (ONR) and Advanced Research Projects Agency-Energy (ARPA-E).  He received, along with Leon Bottou and Jorge Nocedal, the 2021 SIAM-MOS Lagrange Prize in Continuous Optimization. He was awarded, with James V. Burke, Adrian Lewis, and Michael Overton, 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, IMA Journal of Numerical Analysis, Operations Research, and Mathematical Programming Computation.


In-person attendance only available to those with active NYU ID cards.

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