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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Vydané v:	Computational optimization and applications Ročník 92; číslo 2; s. 375 - 435
Hlavní autori:	Audet, Charles, Bigeon, Jean, Couderc, Romain, Kokkolaras, Michael
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	New York Springer US 01.11.2025 Springer Nature B.V
Predmet:	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
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