Robust penalty function method for an uncertain multi-time control optimization problems

被引:16
|
作者
Jayswal, Anurag [1 ]
Preeti [1 ]
Arana-Jimenez, Manuel [2 ]
机构
[1] Indian Sch Mines, Dept Math & Comp, Indian Inst Technol, Dhanbad 826004, Jharkhand, India
[2] Univ Cadiz, Fac SSCC & Commun, Dept Stat & Operat Res, Cadiz, Spain
关键词
Absolute value penalty function method; Convexity; Multi-time control optimization problem; Robust necessary optimality conditions; Robust optimal solution;
D O I
10.1016/j.jmaa.2021.125453
中图分类号
O29 [应用数学];
学科分类号
070104 ;
摘要
This paper gives some new results on multi-time first-order PDE constrained control optimization problem in the face of data uncertainty (MCOPU). We obtain the robust sufficient optimality conditions for (MCOPU). Further, we construct an unconstrained multi-time control optimization problem (MCOPU),, corresponding to (MCOPU) via absolute value penalty function method. Then, we show that the robust optimal solution to the constrained problem and a robust minimizer to the unconstrained problem are equivalent under suitable hypotheses. Moreover, we give some non-trivial examples to validate the results established in this paper. (c) 2021 Elsevier Inc. All rights reserved.
引用
收藏
页数:15
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