On the out-of-sample performance of stochastic dynamic programming and model predictive control
File(s)
Accepted version
Author(s)
Keehan, Dominic
Philpott, Andrew
Anderson, Edward
Type
Journal Article
Abstract
Sample average approximation-based stochastic dynamic programming (SDP) and model predictive control (MPC) are two different methods for approaching multistage stochastic optimization. In this paper we investigate the conditions under which SDP may be outperformed by MPC. We show that, depending on the presence of concavity or convexity, MPC can be interpreted as solving a mean-constrained distributionally ambiguous version of the problem that is solved by SDP. This furnishes performance guarantees when the true mean is known and provides intuition for why MPC performs better in some applications and worse in others. We then study a multistage stochastic optimization problem that is representative of the type for which MPC may be the better choice. We find that this can indeed be the case when the probability distribution of the underlying random variable is skewed or has enough weight in the right-hand tail.
Date Issued
2025-08-01
Date Acceptance
2025-07-11
Citation
INFORMS Journal on Optimization, 2025
ISSN
2575-1492
Publisher
Institute for Operations Research and Management Sciences
Journal / Book Title
INFORMS Journal on Optimization
Copyright Statement
Copyright © 2025, INFORMS. This is the author’s accepted manuscript made available under a CC-BY licence in accordance with Imperial’s Research Publications Open Access policy (www.imperial.ac.uk/oa-policy)
License URL
Publication Status
Published online
Date Publish Online
2025-08-01