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

arXiv: https://arxiv.org/abs/2112.09757 · HTML: https://mlsanity.com/paper/2112.09757/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Dec 2021.

## Built on by (most cited first, 1 of 1)

- [Numerical Methods for Convex Multistage Stochastic Optimization](https://mlsanity.com/paper/2303.15672/) (2023)

## Abstract (arXiv)

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.