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

Vincent Guigues (vincent.guigues***at***gmail.com)
Alexandre Shapiro (ashapiro***at***isye.gatech.edu)
Yi Cheng (cheng.yi***at***gatech.edu)

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.

Keywords: stochastic programming; stochastic optimal control; SDDP; dynamic programming; risk measures; statistical upper bounds

Category 1: Stochastic Programming


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Entry Submitted: 12/17/2021
Entry Accepted: 12/17/2021
Entry Last Modified: 04/19/2022

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