Constrained Bayesian Optimization with Lower Confidence Bound

2024/03/28 by Neelesh S. Upadhye, Raju Chowdhury
Computer Science · Decision Sciences · #Advanced Multi-Objective Optimization Algorithms #Machine Learning and Algorithms #Advanced Bandit Algorithms Research

paper · doi:10.1080/00401706.2024.2336535

Abstract

In this article, we present a hybrid Bayesian optimization (BO) framework to solve constrained optimization problems by adopting a state-of-the-art acquisition function from the unconstrained BO literature, the well-known lower confidence bound acquisition function and propose a novel variant that analyzes the feasible and infeasible regions which ensure the theoretical convergence guarantee. The proposed variant is compared with the existing state-of-the-art approaches in the constrained BO literature via implementing these approaches on six different problems, including black-box, classical engineering, and hyperparameter tuning problems. Further, we demonstrate the effectiveness of our approach through graphical and statistical testing.

Citations

• Taking the Human Out of the Loop: A Review of Bayesian Optimization

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