A priori and a posteriori error estimates of H1‐Galerkin mixed finite element method for parabolic optimal control problems

IF 2 4区 计算机科学 Q3 AUTOMATION & CONTROL SYSTEMS Optimal Control Applications & Methods Pub Date : 2017-01-01 DOI:10.1002/oca.2312
Pratibha Shakya, R. K. Sinha
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引用次数: 4

Abstract

In this exposition, we study both a priori and a posteriori error analysis for the H1‐Galerkin mixed finite element method for optimal control problems governed by linear parabolic equations. The state and costate variables are approximated by the lowest order Raviart‐Thomas finite element spaces, whereas the control variable is approximated by piecewise constant functions. Compared to the standard mixed finite element procedure, the present method is not subject to the Ladyzhenskaya‐Babuska‐Brezzi condition and the approximating finite element spaces are allowed to be of different degree polynomials. A priori error analysis for both the semidiscrete and the backward Euler fully discrete schemes are analyzed, and L∞(L2) convergence properties for the state variables and the control variable are obtained. In addition, L2(L2)‐norm a posteriori error estimates for the state and control variables and L∞(L2) ‐norm for the flux variable are also derived.
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抛物型最优控制问题的H1 - Galerkin混合有限元法的先验和后验误差估计
在这篇文章中,我们研究了H1 - Galerkin混合有限元法在线性抛物方程最优控制问题上的先验和后先验误差分析。状态变量和协态变量由最低阶Raviart - Thomas有限元空间近似,而控制变量由分段常数函数近似。与标准的混合有限元方法相比,该方法不受Ladyzhenskaya‐Babuska‐Brezzi条件的约束,并且允许近似的有限元空间是不同程度的多项式。对半离散和后向欧拉全离散格式进行了先验误差分析,得到了状态变量和控制变量的L∞(L2)收敛性。此外,还推导了状态变量和控制变量的L2(L2) -范数后验误差估计以及通量变量的L∞(L2) -范数。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Optimal Control Applications & Methods
Optimal Control Applications & Methods 工程技术-应用数学
CiteScore
3.90
自引率
11.10%
发文量
108
审稿时长
3 months
期刊介绍: Optimal Control Applications & Methods provides a forum for papers on the full range of optimal and optimization based control theory and related control design methods. The aim is to encourage new developments in control theory and design methodologies that will lead to real advances in control applications. Papers are also encouraged on the development, comparison and testing of computational algorithms for solving optimal control and optimization problems. The scope also includes papers on optimal estimation and filtering methods which have control related applications. Finally, it will provide a focus for interesting optimal control design studies and report real applications experience covering problems in implementation and robustness.
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