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Solving Stochastic Dynamic Programming Problems: A Mixed Complementarity Approach

Author

Listed:
  • Wonjun Chang

    (CRA International)

  • Michael C. Ferris

    (University of Wisconsin-Madison
    Optimization Group, Wisconsin Institutes for Discovery)

  • Youngdae Kim

    (Mathematics and Computer Science Division, Argonne National Laboratory)

  • Thomas F. Rutherford

    (University of Wisconsin-Madison
    Optimization Group, Wisconsin Institutes for Discovery)

Abstract

We present a mixed complementarity problem (MCP) formulation of continuous state dynamic programming problems (DP-MCP). We write the solution to projection methods in value function iteration (VFI) as a joint set of optimality conditions that characterize maximization of the Bellman equation; and approximation of the value function. The MCP approach replaces the iterative component of projection based VFI with a one-shot solution to a square system of complementary conditions. We provide three numerical examples to illustrate our approach.

Suggested Citation

  • Wonjun Chang & Michael C. Ferris & Youngdae Kim & Thomas F. Rutherford, 2020. "Solving Stochastic Dynamic Programming Problems: A Mixed Complementarity Approach," Computational Economics, Springer;Society for Computational Economics, vol. 55(3), pages 925-955, March.
    • Handle: RePEc:kap:compec:v:55:y:2020:i:3:d:10.1007_s10614-019-09921-y
    DOI: 10.1007/s10614-019-09921-y
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    References listed on IDEAS

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    Cited by:

    1. Guo, Peijun, 2022. "Dynamic focus programming: A new approach to sequential decision problems under uncertainty," European Journal of Operational Research, Elsevier, vol. 303(1), pages 328-336.

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