-

 

 

 




Optimization Online





 

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

Citation:

Download: [PDF]

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

Modify/Update this entry


  Visitors Authors More about us Links
  Subscribe, Unsubscribe
Digest Archive
Search, Browse the Repository

 

Submit
Update
Policies
Coordinator's Board
Classification Scheme
Credits
Give us feedback
Optimization Journals, Sites, Societies
Mathematical Optimization Society