Accelerating the convergence of value iteration by using partial transition functions

Edilson F. Arruda, Fabrício O. Ourique, Jason LaCombe and Anthony Almudevar

European Journal of Operational Research, 2013, vol. 229, issue 1, 190-198

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.

Keywords: Dynamic programming; Markov processes; Optimization (search for similar items in EconPapers)
Date: 2013
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Citations: View citations in EconPapers (1)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:ejores:v:229:y:2013:i:1:p:190-198

DOI: 10.1016/j.ejor.2013.02.029

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