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Lipschitzian Estimates in Discrete-Time Constrained Stochastic Optimal Control

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M. Papi, S. Sbaraglia

Optimization and Control · Submitted 5 Jun 2002

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim We derive accurate estimates of the Lipschitz constant of the value function, by means of a regularity result of the multifunction that defines the admissible control set.

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arXiv IDmath/0206037 (version 1)
Pith IDMBZO2RWA, Pith's permanent record of this paper and its review
CitationM. Papi, S. Sbaraglia. "Lipschitzian Estimates in Discrete-Time Constrained Stochastic Optimal Control." arXiv:math/0206037 (2002). https://pith.science/paper/MBZO2RWA
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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,526 input tokens.

What the abstract itself states, as probabilities: claims first 0.03 · states limitation 0.08 · human participants 0.03 · controlled comparison 0.02 · randomized assignment 0.01 · states quantitative result 0.16 · compares with prior results 0.04 · mentions code or data availability 0.02.

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arXiv listing abstract v1. Results and the main claim are underlined; .

This paper is devoted to the analysis of a finite horizon discrete-time stochastic optimal control problem, in presence of constraints. We study the regularity of the value function which comes from the dynamic programming algorithm. We derive accurate estimates of the Lipschitz constant of the value function, by means of a regularity result of the multifunction that defines the admissible control set. In the last section we discuss an application to an optimal asset-allocation problem.

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#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper is devoted to the analysis of a finite horizon discrete-time stochastic optimal control problem, in presence of constraints. background 0.81 0.58 0.04 asserted 0.96
s1 We study the regularity of the value function which comes from the dynamic programming algorithm. contribution 0.99 0.91 0.05 asserted 0.93
s2 We derive accurate estimates of the Lipschitz constant of the value function, by means of a regularity result of the multifunction that defines the admissible control set. result 0.64 0.96 0.71 asserted 1.00
s3 In the last section we discuss an application to an optimal asset-allocation problem. contribution 0.82 0.81 0.04 asserted 0.87

What kind of paper the classifier reports this is: theoretical result 1.00 · evidence from proof or derivation 1.00.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper is devoted to the analysis of a finite horizon discrete-time stochastic optimal control problem, in presence of constraints. We study the regularity of the value function which comes from the dynamic programming algorithm. We derive accurate estimates of the Lipschitz constant of the value function, by means of a regularity result of the multifunction that defines the admissible control set. In the last section we discuss an application to an optimal asset-allocation problem.

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