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Lookahead Bayesian Optimization with Inequality Constraints
Remi Lam · Karen Willcox

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #161 #None

We consider the task of optimizing an objective function subject to inequality constraints when both the objective and the constraints are expensive to evaluate. Bayesian optimization (BO) is a popular way to tackle optimization problems with expensive objective function evaluations, but has mostly been applied to unconstrained problems. Several BO approaches have been proposed to address expensive constraints but are limited to greedy strategies maximizing immediate reward. To address this limitation, we propose a lookahead approach that selects the next evaluation in order to maximize the long-term feasible reduction of the objective function. We present numerical experiments demonstrating the performance improvements of such a lookahead approach compared to several greedy BO algorithms, including constrained expected improvement (EIC) and predictive entropy search with constraint (PESC).

Author Information

Remi Lam (MIT)
Karen Willcox (MIT)

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