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arXiv · 2408.15690

Assessing solution quality in risk-averse stochastic programs

E. Ruben van Beesten·Nick W. Koning·David P. Morton
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Abstract

In optimization problems, the quality of a candidate solution can be characterized by the optimality gap. For most stochastic optimization problems, this gap must be statistically estimated. We show that for risk-averse problems, standard estimators are optimistically biased, which compromises the statistical guarantee on the optimality gap. We introduce estimators for risk-averse problems that do not suffer from this bias. Our method relies on using two independent samples, each estimating a different component of the optimality gap. Our approach extends a broad class of optimality gap estimation methods from the risk-neutral case to the risk-averse case, such as the multiple replications procedure and its one- and two-sample variants. We show that our approach is tractable and leads to high-quality optimality gap estimates for spectral and quadrangle risk measures. Our approach can further make use of existing bias and variance reduction techniques.

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E. Ruben van Beesten, Nick W. Koning, David P. Morton. 2024-08-28. Assessing solution quality in risk-averse stochastic programs. https://arxiv.org/abs/2408.15690

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