Fully Polynomial Time Approximation Schemes for Stochastic Dynamic Programs

  • Nir Halman
  • Diego Klabjan
  • James B. Orlin
  • David Simchi-Levi
  • Chung-Lun Li
    • Categories Approximation Algorithms, Dynamic Programming Short URL: https://optimization-online.org/?p=12459

    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.

    Citation

    SIAM J. Discrete Math. 28, pp. 1725-1796, 2014.