抛物线积分微分优化控制问题的双网格扩展混合有限元逼近法

IF 0.9 4区 数学 Q3 MATHEMATICS, APPLIED
Yan-ping Chen, Jian-wei Zhou, Tian-liang Hou
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引用次数: 0

摘要

本文旨在为抛物线整微分方程支配的最优控制问题构建完全离散化扩展混合有限元法的双网格方案,并讨论先验误差估计。状态变量和共状态变量由最低阶的 Raviart-Thomas 混合有限元离散化,控制变量由片断常数函数近似。时间导数采用后向欧拉法离散。首先,我们定义了一些新的混合椭圆投影,并证明了相应的误差估计,这些误差估计在后续的收敛分析中起着重要作用。其次,我们推导出所有变量的先验误差估计。第三,我们提出了一种双网格方案,并对其收敛性进行了分析。在双网格方案中,抛物线最优控制问题在细网格上的求解被简化为抛物线最优控制问题在更粗网格上的求解和一个解耦线性代数系统在细网格上的求解,所得到的求解仍然保持渐近最优精度。最后,我们给出了一个数值实例来验证理论结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Two-grid Method of Expanded Mixed Finite Element Approximations for Parabolic Integro-differential Optimal Control Problems

This paper aims to construct a two-grid scheme of fully discretized expanded mixed finite element methods for optimal control problems governed by parabolic integro-differential equations and discuss a priori error estimates. The state variables and co-state variables are discretized by the lowest order Raviart-Thomas mixed finite element, and the control variable is approximated by piecewise constant functions. The time derivative is discretized by the backward Euler method. Firstly, we define some new mixed elliptic projections and prove the corresponding error estimates which play an important role in subsequent convergence analysis. Secondly, we derive a priori error estimates for all variables. Thirdly, we present a two-grid scheme and analyze its convergence. In the two-grid scheme, the solution of the parabolic optimal control problem on a fine grid is reduced to the solution of the parabolic optimal control problem on a much coarser grid and the solution of a decoupled linear algebraic system on the fine grid and the resulting solution still maintains an asymptotically optimal accuracy. At last, a numerical example is presented to verify the theoretical results.

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来源期刊
CiteScore
1.30
自引率
0.00%
发文量
70
审稿时长
3.0 months
期刊介绍: Acta Mathematicae Applicatae Sinica (English Series) is a quarterly journal established by the Chinese Mathematical Society. The journal publishes high quality research papers from all branches of applied mathematics, and particularly welcomes those from partial differential equations, computational mathematics, applied probability, mathematical finance, statistics, dynamical systems, optimization and management science.
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