Risk-averse constrained blackbox optimization under mixed aleatory/epistemic uncertainties

This paper addresses risk-averse constrained optimization problems where the objective and constraint functions can only be computed by a blackbox subject to unknown uncertainties. To handle mixed aleatory/epistemic uncertainties, the problem is transformed into a conditional value-at-risk (CVaR) co...

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Bibliographic Details
Published in:	Computational optimization and applications Vol. 92; no. 2; pp. 375 - 435
Main Authors:	Audet, Charles, Bigeon, Jean, Couderc, Romain, Kokkolaras, Michael
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.11.2025 Springer Nature B.V
Subjects:	Algorithms Approximation Black boxes Constraints Convex and Discrete Geometry Design engineering Design optimization Epistemology Fuzzy sets Lagrangian function Management Science Mathematics Mathematics and Statistics Methods Monte Carlo simulation Operations Research Operations Research/Decision Theory Optimization Probability Random variables Risk Signal processing Statistics Uncertainty Conditional value-at-risk Multi-timescale stochastic approximation Truncated Gaussian gradient estimator Risk-averse optimization Mixed aleatory/epistemic uncertainties Constrained blackbox optimization
ISSN:	0926-6003, 1573-2894
Online Access:	Get full text
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