Regularization and finite element error estimates for elliptic distributed optimal control problems with energy regularization and state or control constraints

IF 2.9 2区 数学 Q1 MATHEMATICS, APPLIED Computers & Mathematics with Applications Pub Date : 2025-01-13 DOI:10.1016/j.camwa.2024.12.021
Peter Gangl, Richard Löscher, Olaf Steinbach
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Abstract

In this paper we discuss the numerical solution of elliptic distributed optimal control problems with state or control constraints when the control is considered in the energy norm. As in the unconstrained case we can relate the regularization parameter and the finite element mesh size in order to ensure an optimal balance between the error and the cost, and, on the discrete level, an optimal order of convergence which only depends on the regularity of the given target, also including discontinuous target functions. While in most cases, state or control constraints are discussed for the more common L2 regularization, much less is known in the case of energy regularizations. But in this case, and for both control and state constraints, we can formulate first kind variational inequalities to determine the unknown state, from which we can compute the control in a post processing step. Related variational inequalities also appear in obstacle problems, and are well established both from a mathematical and a numerical analysis point of view. Numerical results confirm the applicability and accuracy of the proposed approach.
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具有能量正则化和状态或控制约束的椭圆分布最优控制问题的正则化和有限元误差估计
本文讨论了在能量范数下考虑状态约束或控制约束的椭圆型分布最优控制问题的数值解。在无约束情况下,我们可以将正则化参数和有限元网格尺寸联系起来,以确保误差和代价之间的最佳平衡,并且,在离散水平上,最优收敛顺序仅取决于给定目标的规律性,也包括不连续的目标函数。虽然在大多数情况下,对更常见的L2正则化讨论了状态或控制约束,但对能量正则化的了解要少得多。但在这种情况下,对于控制和状态约束,我们可以制定第一类变分不等式来确定未知状态,从中我们可以在后处理步骤中计算控制。相关的变分不等式也出现在障碍问题中,并且从数学和数值分析的角度都得到了很好的建立。数值结果验证了该方法的适用性和准确性。
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来源期刊
Computers & Mathematics with Applications
Computers & Mathematics with Applications 工程技术-计算机:跨学科应用
CiteScore
5.10
自引率
10.30%
发文量
396
审稿时长
9.9 weeks
期刊介绍: Computers & Mathematics with Applications provides a medium of exchange for those engaged in fields contributing to building successful simulations for science and engineering using Partial Differential Equations (PDEs).
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