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A Nonlinear Programming Method For Dynamic Programming

Author

Listed:
  • Cai, Yongyang
  • Judd, Kenneth L.
  • Lontzek, Thomas S.
  • Michelangeli, Valentina
  • Su, Che-Lin

Abstract

A nonlinear programming formulation is introduced to solve infinite-horizon dynamic programming problems. This extends the linear approach to dynamic programming by using ideas from approximation theory to approximate value functions. Our numerical results show that this nonlinear programming is efficient and accurate, and avoids inefficient discretization.

Suggested Citation

  • Cai, Yongyang & Judd, Kenneth L. & Lontzek, Thomas S. & Michelangeli, Valentina & Su, Che-Lin, 2017. "A Nonlinear Programming Method For Dynamic Programming," Macroeconomic Dynamics, Cambridge University Press, vol. 21(2), pages 336-361, March.
    • Handle: RePEc:cup:macdyn:v:21:y:2017:i:02:p:336-361_00
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    Cited by:

    1. Yongyang Cai & Kenneth Judd & Greg Thain & Stephen Wright, 2015. "Solving Dynamic Programming Problems on a Computational Grid," Computational Economics, Springer;Society for Computational Economics, vol. 45(2), pages 261-284, February.
    2. Ulmer, Marlin W. & Thomas, Barrett W., 2020. "Meso-parametric value function approximation for dynamic customer acceptances in delivery routing," European Journal of Operational Research, Elsevier, vol. 285(1), pages 183-195.

    More about this item

    JEL classification:

    • C61 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Optimization Techniques; Programming Models; Dynamic Analysis
    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques

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