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Adaptive Sampling Strategies for Risk-averse Stochastic Optimization with Constraints by Beiser, Florian; Keith, Brendan; Urbainczyk, Simon; Wohlmuth, Barbara is a scholarly article available to read on EtoBox.

What is Adaptive Sampling Strategies for Risk-averse Stochastic Optimization with Constraints about?

We introduce adaptive sampling methods for stochastic programs with deterministic constraints. First, we propose and analyze a variant of the stochastic projected gradient method where the sample size used to approximate the reduced gradient is determined on-the-fly and updated adaptively. This method is applicable to a broad class of expectation-based risk measures and leads to a significant reduction in the individual gradient evaluations used to estimate the objective function gradient. Numerical experiments with expected risk minimization and conditional value-at-risk minimization support this conclusion and demonstrate practical performance and efficacy for both risk-neutral and risk-averse problems. Second, we propose an SQP-type method based on similar adaptive sampling principles. The benefits of this method are demonstrated in a simplified engineering design application featuring risk-averse shape optimization of a steel shell structure subject to uncertain loading conditions and model uncertainty.

Author

Beiser, Florian; Keith, Brendan; Urbainczyk, Simon; Wohlmuth, Barbara

Published

2020

Language

EN