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

Baasansuren Jadamba (), Akhtar A. Khan () and Miguel Sama ()
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Baasansuren Jadamba: Rochester Institute of Technology
Akhtar A. Khan: Rochester Institute of Technology
Miguel Sama: Universidad Nacional de Educación a Distancia

A chapter in Mathematical Analysis in Interdisciplinary Research, 2021, pp 417-429 from Springer

Abstract: 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.

Keywords: Optimal control; Stochastic PDEs; Stochastic approximation; Regularization; Projected stochastic subgradient; 35R30; 49N45; 65J20; 65J22; 65M30 (search for similar items in EconPapers)
Date: 2021
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Persistent link: https://EconPapers.repec.org/RePEc:spr:spochp:978-3-030-84721-0_19

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DOI: 10.1007/978-3-030-84721-0_19

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