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Activity Number: 645 - Bayesian Optimization
Type: Topic Contributed
Date/Time: Thursday, August 1, 2019 : 10:30 AM to 12:20 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #301680
Title: The Statistical Filter Approach to Constrained Optimization
Author(s): Herbert Lee*
Companies: Univ of California, Santa Cruz
Keywords: black box function; multivariate Gaussian process; computer simulator; computer experiment

Expensive black box systems arise in many engineering applications but can be difficult to optimize because their output functions may be complex, multi-modal, and difficult to understand. The task becomes even more challenging when the optimization is subject to multiple constraints and no derivative information is available. In this paper, we combine response surface modeling and filter methods in order to solve problems of this nature. In employing a filter algorithm for solving constrained optimization problems, we establish a novel probabilistic metric for guiding the filter. Overall, this hybridization of statistical modeling and nonlinear programming efficiently utilizes both global and local search in order to quickly converge to a global solution to the constrained optimization problem. To demonstrate the effectiveness of the proposed methods, we perform numerical tests on a synthetic test problem, a problem from the literature, and a real-world hydrology computer experiment optimization problem.

Authors who are presenting talks have a * after their name.

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