Semilinear Dynamic Programming: Analysis, Algorithms, and Certainty Equivalence Properties

Fuente: arXiv
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Hauptverfasser: Li, Yuchao, Bertsekas, Dimitri
Format: Preprint
Veröffentlicht: 2025
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author Li, Yuchao
Bertsekas, Dimitri
author_facet Li, Yuchao
Bertsekas, Dimitri
contents We consider a broad class of dynamic programming (DP) problems that involve a partially linear structure and some positivity properties in their system equation and cost function. We address deterministic and stochastic problems, possibly with Markov jump parameters. We focus primarily on infinite horizon problems and prove that under our assumptions, the optimal cost function is linear, and that an optimal policy can be computed efficiently with standard DP algorithms. Moreover, we show that forms of certainty equivalence hold for our stochastic problems, in analogy with the classical linear quadratic optimal control problems.
format Preprint
id arxiv_https___arxiv_org_abs_2501_04668
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Semilinear Dynamic Programming: Analysis, Algorithms, and Certainty Equivalence Properties
Li, Yuchao
Bertsekas, Dimitri
Optimization and Control
We consider a broad class of dynamic programming (DP) problems that involve a partially linear structure and some positivity properties in their system equation and cost function. We address deterministic and stochastic problems, possibly with Markov jump parameters. We focus primarily on infinite horizon problems and prove that under our assumptions, the optimal cost function is linear, and that an optimal policy can be computed efficiently with standard DP algorithms. Moreover, we show that forms of certainty equivalence hold for our stochastic problems, in analogy with the classical linear quadratic optimal control problems.
title Semilinear Dynamic Programming: Analysis, Algorithms, and Certainty Equivalence Properties
topic Optimization and Control
url https://arxiv.org/abs/2501.04668