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Department of Mathematics,
Department of Mathematics,
University of California San Diego
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Math 278C: Optimization and Data Science
Prof. Suhan Zhong
Taxus A&M University
Chance constrained optimization with polynomial perturbation
Abstract:
We study a robust approximation method for solving a class of chance constrained optimization problems. The constraints are assumed to be polynomial in the random vector. A semidefinite relaxation algorithm is proposed for solving this kind of problem. Its asymptotic and finite convergence are proven under some mild assumptions.
Host: Jiawang Nie
May 24, 2023
3:00 PM
APM 7321
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