Risk-Averse Stochastic Optimal Control: an efficiently computable statistical upper bound
Optimization and Control
2023-05-04 v3
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.
Cite
@article{arxiv.2112.09757,
title = {Risk-Averse Stochastic Optimal Control: an efficiently computable statistical upper bound},
author = {Vincent Guigues and Alexander Shapiro and Yi Cheng},
journal= {arXiv preprint arXiv:2112.09757},
year = {2023}
}