非线性混合约束半线性椭圆型最优控制问题近似解的误差估计

IF 1.4 4区 数学 Q2 MATHEMATICS, APPLIED
B. T. Kien, N. Tuan
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引用次数: 2

摘要

摘要本文给出了具有混合点约束的半线性椭圆最优控制问题近似解收敛的一些充分条件。在完全控制离散化的情况下,我们用有限元方法建立了离散最优控制问题。我们证明了如果严格二阶充分条件成立,则得到了离散最优控制问题的近似解与原问题的最优解之间的一些误差估计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Error Estimates for Approximate Solutions to Seminlinear Elliptic Optimal Control Problems with Nonlinear and Mixed Constraints
Abstract This paper gives some sufficient conditions for convergence of approximate solutions to seminlinear elliptic optimal control problems with mixed pointwise constraints. We build discrete optimal control problems by the finite element method in type of the full control discretization. We show that if the strictly second-order sufficient condition is valid, then some error estimates between approximate solutions of discrete optimal control problems and optimal solutions of the original problem are obtained.
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来源期刊
CiteScore
2.40
自引率
8.30%
发文量
74
审稿时长
6-12 weeks
期刊介绍: Numerical Functional Analysis and Optimization is a journal aimed at development and applications of functional analysis and operator-theoretic methods in numerical analysis, optimization and approximation theory, control theory, signal and image processing, inverse and ill-posed problems, applied and computational harmonic analysis, operator equations, and nonlinear functional analysis. Not all high-quality papers within the union of these fields are within the scope of NFAO. Generalizations and abstractions that significantly advance their fields and reinforce the concrete by providing new insight and important results for problems arising from applications are welcome. On the other hand, technical generalizations for their own sake with window dressing about applications, or variants of known results and algorithms, are not suitable for this journal. Numerical Functional Analysis and Optimization publishes about 70 papers per year. It is our current policy to limit consideration to one submitted paper by any author/co-author per two consecutive years. Exception will be made for seminal papers.
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