Accelerating the convergence of value iteration by using partial transition functions
Accelerating the convergence of value iteration by using partial transition functions
This work proposes an algorithm that makes use of partial information to improve the convergence properties of the value iteration algorithm in terms of the overall computational complexity. The algorithm iterates on a series of increasingly refined approximate models that converges to the true model according to an optimal linear rate, which coincides with the convergence rate of the original value iteration algorithm. The paper investigates the properties of the proposed algorithm and features a series of switchover queue examples which illustrates the efficacy of the method.
Dynamic programming, Markov processes, Optimization
190-198
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Ourique, Fabrício O.
c2b933e0-dd92-4260-83f2-c3982f4911e9
Lacombe, Jason
f1426ba8-f27e-45a6-b6d8-d031729d11d0
Almudevar, Anthony
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16 August 2013
Arruda, Edilson F.
8eb3bd83-e883-4bf3-bfbc-7887c5daa911
Ourique, Fabrício O.
c2b933e0-dd92-4260-83f2-c3982f4911e9
Lacombe, Jason
f1426ba8-f27e-45a6-b6d8-d031729d11d0
Almudevar, Anthony
f0998a97-a377-41a9-82d0-0c1de5f33688
Arruda, Edilson F., Ourique, Fabrício O., Lacombe, Jason and Almudevar, Anthony
(2013)
Accelerating the convergence of value iteration by using partial transition functions.
European Journal of Operational Research, 229 (1), .
(doi:10.1016/j.ejor.2013.02.029).
Abstract
This work proposes an algorithm that makes use of partial information to improve the convergence properties of the value iteration algorithm in terms of the overall computational complexity. The algorithm iterates on a series of increasingly refined approximate models that converges to the true model according to an optimal linear rate, which coincides with the convergence rate of the original value iteration algorithm. The paper investigates the properties of the proposed algorithm and features a series of switchover queue examples which illustrates the efficacy of the method.
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Published date: 16 August 2013
Keywords:
Dynamic programming, Markov processes, Optimization
Identifiers
Local EPrints ID: 445936
URI: http://eprints.soton.ac.uk/id/eprint/445936
ISSN: 0377-2217
PURE UUID: b807ddd1-a3f4-4253-bb8a-3b34c9d82332
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Date deposited: 14 Jan 2021 19:16
Last modified: 06 Jun 2024 02:09
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Contributors
Author:
Edilson F. Arruda
Author:
Fabrício O. Ourique
Author:
Jason Lacombe
Author:
Anthony Almudevar
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