Fully Polynomial Time Approximation Schemes for Stochastic Dynamic Programs
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Orlin_Fully polynomial.pdf
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Author(s)
Halman, Nir
Klabjan, Diego
Li, Chung-Lun
Orlin, James B
Simchi-Levi, David
Date Issued
October 2014
Journal
SIAM Journal on Discrete Mathematics
Publisher
Society for Industrial and Applied Mathematics
Citation
Halman, Nir; Klabjan, Diego; Li, Chung-Lun; Orlin, James and Simchi-Levi, David. “Fully Polynomial Time Approximation Schemes for Stochastic Dynamic Programs.” SIAM Journal on Discrete Mathematics 28, no. 4 (January 2014): 1725–1796. © 2014 Society for Industrial and Applied Mathematics
Version
Final published version
Abstract
We present a framework for obtaining fully polynomial time approximation schemes (FPTASs) for stochastic univariate dynamic programs with either convex or monotone single-period cost functions. This framework is developed through the establishment of two sets of computational rules, namely, the calculus of K-approximation functions and the calculus of K-approximation sets. Using our framework, we provide the first FPTASs for several NP-hard problems in various fields of research such as knapsack models, logistics, operations management, economics, and mathematical finance. Extensions of our framework via the use of the newly established computational rules are also discussed.
MIT Department
Massachusetts Institute of Technology. Department of Civil and Environmental Engineering
Massachusetts Institute of Technology. Institute for Data, Systems, and Society
Sloan School of Management
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Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
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DOI of Published Version
https://doi.org/10.1137/130925153
