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Information Relaxations, Duality, and Convex Stochastic Dynamic Programs

Author

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  • David B. Brown

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

  • James E. Smith

    (Fuqua School of Business, Duke University, Durham, North Carolina 27708)

Abstract

We consider the information relaxation approach for calculating performance bounds for stochastic dynamic programs (DPs). This approach generates performance bounds by solving problems with relaxed nonanticipativity constraints and a penalty that punishes violations of these nonanticipativity constraints. In this paper, we study DPs that have a convex structure and consider gradient penalties that are based on first-order linear approximations of approximate value functions. When used with perfect information relaxations, these penalties lead to subproblems that are deterministic convex optimization problems. We show that these gradient penalties can, in theory, provide tight bounds for convex DPs and can be used to improve on bounds provided by other relaxations, such as Lagrangian relaxation bounds. Finally, we apply these results in two example applications: first, a network revenue management problem that describes an airline trying to manage seat capacity on its flights; and second, an inventory management problem with lead times and lost sales. These are challenging problems of significant practical interest. In both examples, we compute performance bounds using information relaxations with gradient penalties and find that some relatively easy-to-compute heuristic policies are nearly optimal.

Suggested Citation

  • David B. Brown & James E. Smith, 2014. "Information Relaxations, Duality, and Convex Stochastic Dynamic Programs," Operations Research, INFORMS, vol. 62(6), pages 1394-1415, December.
  • Handle: RePEc:inm:oropre:v:62:y:2014:i:6:p:1394-1415
    DOI: 10.1287/opre.2014.1322
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    Cited by:

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    4. Santiago R. Balseiro & David B. Brown, 2019. "Approximations to Stochastic Dynamic Programs via Information Relaxation Duality," Operations Research, INFORMS, vol. 67(2), pages 577-597, March.
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    9. David B. Brown & James E. Smith, 2020. "Index Policies and Performance Bounds for Dynamic Selection Problems," Management Science, INFORMS, vol. 66(7), pages 3029-3050, July.
    10. David B. Brown & Martin B. Haugh, 2017. "Information Relaxation Bounds for Infinite Horizon Markov Decision Processes," Operations Research, INFORMS, vol. 65(5), pages 1355-1379, October.
    11. Mei, Xiaoling & Nogales, Francisco J., 2018. "Portfolio selection with proportional transaction costs and predictability," Journal of Banking & Finance, Elsevier, vol. 94(C), pages 131-151.
    12. Alessio Trivella & Danial Mohseni-Taheri & Selvaprabu Nadarajah, 2023. "Meeting Corporate Renewable Power Targets," Management Science, INFORMS, vol. 69(1), pages 491-512, January.
    13. Qihang Lin & Selvaprabu Nadarajah & Negar Soheili, 2020. "Revisiting Approximate Linear Programming: Constraint-Violation Learning with Applications to Inventory Control and Energy Storage," Management Science, INFORMS, vol. 66(4), pages 1544-1562, April.
    14. Yuhang Ma & Paat Rusmevichientong & Mika Sumida & Huseyin Topaloglu, 2020. "An Approximation Algorithm for Network Revenue Management Under Nonstationary Arrivals," Operations Research, INFORMS, vol. 68(3), pages 834-855, May.
    15. David A. Goldberg & Martin I. Reiman & Qiong Wang, 2021. "A Survey of Recent Progress in the Asymptotic Analysis of Inventory Systems," Production and Operations Management, Production and Operations Management Society, vol. 30(6), pages 1718-1750, June.
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    18. Daniel R. Jiang & Lina Al-Kanj & Warren B. Powell, 2020. "Optimistic Monte Carlo Tree Search with Sampled Information Relaxation Dual Bounds," Operations Research, INFORMS, vol. 68(6), pages 1678-1697, November.

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