A posteriori error estimates of spectral method for nonlinear parabolic optimal control problem

被引:0
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作者
Lin Li
Zuliang Lu
Wei Zhang
Fei Huang
Yin Yang
机构
[1] Chongqing Three Gorges University,Key Laboratory for Nonlinear Science and System Structure
[2] Chongqing Three Gorges University,Key Laboratory of Intelligent Information Processing and Control
[3] Tianjin University of Finance and Economics,Research Center for Mathematics and Economics
[4] Chongqing Three Gorges University,Key Laboratory of Intelligent Information Processing and Control of Chongqing Municipal Institutions of Higher Education
[5] Xiangtan University,Hunan Key Laboratory for Computation and Simulation in Science and Engineering
[6] Xiangtan University,Key Laboratory of Intelligent Computing and Information Processing of Ministry of Education
关键词
Optimal control problem; Nonlinear parabolic equations; Variational discretization; Spectral method; A posteriori error estimates; 49J20; 65N30;
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摘要
In this paper, we investigate the spectral approximation of optimal control problem governed by nonlinear parabolic equations. A spectral approximation scheme for the nonlinear parabolic optimal control problem is presented. We construct a fully discrete spectral approximation scheme by using the backward Euler scheme in time. Moreover, by using an orthogonal projection operator, we obtain L2(H1)−L2(L2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$L^{2}(H^{1})-L^{2}(L ^{2})$\end{document} a posteriori error estimates of the approximation solutions for both the state and the control. Finally, by introducing two auxiliary equations, we also obtain L2(L2)−L2(L2)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$L^{2}(L^{2})-L^{2}(L^{2})$\end{document} a posteriori error estimates of the approximation solutions for both the state and the control.
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