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arXiv · 2112.09757

Risk-Averse Stochastic Optimal Control: an efficiently computable statistical upper bound

Vincent Guigues·Alexander Shapiro·Yi Cheng
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Abstract

In this paper, we discuss an application of the SDDP type algorithm to nested risk-averse formulations of Stochastic Optimal Control (SOC) problems. We propose a construction of a statistical upper bound for the optimal value of risk-averse SOC problems. This outlines an approach to a solution of a long standing problem in that area of research. The bound holds for a large class of convex and monotone conditional risk mappings. Finally, we show the validity of the statistical upper bound to solve a real-life stochastic hydro-thermal planning problem.

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Vincent Guigues, Alexander Shapiro, Yi Cheng. 2021-12-17. Risk-Averse Stochastic Optimal Control: an efficiently computable statistical upper bound. https://arxiv.org/abs/2112.09757

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