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

Autor:	Vincent Guigues, Alexander Shapiro, Yi Cheng
Jazyk:	angličtina
Rok vydání:	2021
Předmět:	Optimization and Control (math.OC) Applied Mathematics FOS: Mathematics Management Science and Operations Research Mathematics - Optimization and Control Industrial and Manufacturing Engineering Software
Popis:	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.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_dedup___::6901b6452b4a0c923756e0ca7ecfe9da http://arxiv.org/abs/2112.09757 Zobrazit plný text záznamu