Presented By: Industrial & Operations Engineering
PhD Research Talk: Baoyu Zhou
Methods for Constrained Stochastic Optimization: Theory and Practice
Seminar Abstract:
In this talk, I will present some recents works on the design, analysis, and implementation of practical algorithms for solving stochastic optimization problems with constraints, while such problems arise from important applications including artificial intelligence, inventory control, power systems, etc. The first part of this talk focuses on some new understandings of an inexact regularized L-shaped algorithm for two-stage stochastic programming problems. Under common assumptions including fixed recourse and bounded (sub)gradients, we provide the number of iterations, operations, and samples that the algorithm needs to find a near-optimal solution, where the radius of the convergence neighborhood depends on the level of the inexactness of objective function estimates. In the second part, I will introduce a sequential quadratic programming method for minimizing a stochastic objective function subject to deterministic constraints. In addition to presenting the theoretical convergence behavior, we compare the empirical performance of our proposed method with other alternatives to demonstrate the advantages of our algorithm. In the end, I will discuss some of my future research directions.
Presenter Bio:
Baoyu Zhou is a postdoctoral researcher at the University of Michigan (Department of Industrial and Operations Engineering) and the University of Chicago (Booth School of Business), working with Professors Albert S. Berahas, Haihao Lu, and John R. Birge. He received his doctoral and master's degree in Industrial and Systems Engineering (ISE) from Lehigh University, advised by Professor Frank E. Curtis. Before joining Lehigh, he received his bachelor's degree in Mechanical Engineering from Shanghai Jiao Tong University. He was a Givens Associate in the Mathematics and Computer Science Division at Argonne National Laboratory and a Research Intern at Facebook AI Research. He won the Van Hoesen Family Best Publication Award at Lehigh ISE Department in 2021 and received the Elizabeth V. Stout Dissertation Award at the P.C. Rossin College of Engineering and Applied Science in 2022. His research focuses on developing, analyzing, and implementing practical algorithms for solving large-scale continuous optimization problems.
In this talk, I will present some recents works on the design, analysis, and implementation of practical algorithms for solving stochastic optimization problems with constraints, while such problems arise from important applications including artificial intelligence, inventory control, power systems, etc. The first part of this talk focuses on some new understandings of an inexact regularized L-shaped algorithm for two-stage stochastic programming problems. Under common assumptions including fixed recourse and bounded (sub)gradients, we provide the number of iterations, operations, and samples that the algorithm needs to find a near-optimal solution, where the radius of the convergence neighborhood depends on the level of the inexactness of objective function estimates. In the second part, I will introduce a sequential quadratic programming method for minimizing a stochastic objective function subject to deterministic constraints. In addition to presenting the theoretical convergence behavior, we compare the empirical performance of our proposed method with other alternatives to demonstrate the advantages of our algorithm. In the end, I will discuss some of my future research directions.
Presenter Bio:
Baoyu Zhou is a postdoctoral researcher at the University of Michigan (Department of Industrial and Operations Engineering) and the University of Chicago (Booth School of Business), working with Professors Albert S. Berahas, Haihao Lu, and John R. Birge. He received his doctoral and master's degree in Industrial and Systems Engineering (ISE) from Lehigh University, advised by Professor Frank E. Curtis. Before joining Lehigh, he received his bachelor's degree in Mechanical Engineering from Shanghai Jiao Tong University. He was a Givens Associate in the Mathematics and Computer Science Division at Argonne National Laboratory and a Research Intern at Facebook AI Research. He won the Van Hoesen Family Best Publication Award at Lehigh ISE Department in 2021 and received the Elizabeth V. Stout Dissertation Award at the P.C. Rossin College of Engineering and Applied Science in 2022. His research focuses on developing, analyzing, and implementing practical algorithms for solving large-scale continuous optimization problems.
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