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A Regularized Stochastic Subgradient Projection Method for an Optimal Control Problem in a Stochastic Partial Differential Equation

In: Mathematical Analysis in Interdisciplinary Research

Author

Listed:
  • Baasansuren Jadamba

    (Rochester Institute of Technology)

  • Akhtar A. Khan

    (Rochester Institute of Technology)

  • Miguel Sama

    (Universidad Nacional de Educación a Distancia)

Abstract

This work studies an optimal control problem in a stochastic partial differential equation. We present a new regularized stochastic subgradient projection iterative method for a general stochastic optimization problem. By using the martingale theory, we provide a convergence analysis for the proposed method. We test the iterative scheme’s feasibility on the considered optimal control problem. The numerical results are encouraging and demonstrate the utility of a stochastic approximation framework in control problems with data uncertainty.

Suggested Citation

  • Baasansuren Jadamba & Akhtar A. Khan & Miguel Sama, 2021. "A Regularized Stochastic Subgradient Projection Method for an Optimal Control Problem in a Stochastic Partial Differential Equation," Springer Optimization and Its Applications, in: Ioannis N. Parasidis & Efthimios Providas & Themistocles M. Rassias (ed.), Mathematical Analysis in Interdisciplinary Research, pages 417-429, Springer.
    • Handle: RePEc:spr:spochp:978-3-030-84721-0_19
    DOI: 10.1007/978-3-030-84721-0_19
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