Safe Bayesian Optimization under Unknown Constraints
Bergmann D, Graichen K (2020)
Publication Type: Conference contribution
Publication year: 2020
Publisher: Institute of Electrical and Electronics Engineers Inc.
Book Volume: 2020-December
Pages Range: 3592-3597
Conference Proceedings Title: 59th IEEE Conference on Decision and Control (CDC 2020)
ISBN: 9781728174471
DOI: 10.1109/CDC42340.2020.9304209
Abstract
This paper presents a safe optimization method for minimizing an unknown cost function subject to unknown inequality and equality constraints. The cost function as well as the constraints evaluation may be corrupted with Gaussian measurement noise with known uncertainty. The focus especially lies in the safe exploration of the cost function, which means that an evaluation of the cost function far away of previous evaluations is not favoured. Also evaluations in regions where the constraints are violated are undesired. This is the case, for example, in technical applications, where systems may become unstable or damaged, if constraints are violated. To this end, a combination of the expected improvement of the cost and its mean is minimized, while bounding the variance of the cost. The inequality and equality constraints are reformulated as constraints for the probability of the constraint violation. The optimization method is evaluated on numerical examples.
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Germany (DE)How to cite
APA:
Bergmann, D., & Graichen, K. (2020). Safe Bayesian Optimization under Unknown Constraints. In 59th IEEE Conference on Decision and Control (CDC 2020) (pp. 3592-3597). Institute of Electrical and Electronics Engineers Inc..
MLA:
Bergmann, Daniel, and Knut Graichen. "Safe Bayesian Optimization under Unknown Constraints." Proceedings of the 59th IEEE Conference on Decision and Control, CDC 2020 Institute of Electrical and Electronics Engineers Inc., 2020. 3592-3597.
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