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New Time Domain Decomposition Methods for Parabolic Optimal Control Problems I: Dirichlet–Neumann and Neumann–Dirichlet Algorithms
SIAM Journal on Numerical Analysis ( IF 2.8 ) Pub Date : 2024-08-23 , DOI: 10.1137/23m1584502
Martin J. Gander 1 , Liu-Di Lu 1
Affiliation  

SIAM Journal on Numerical Analysis, Volume 62, Issue 4, Page 2048-2070, August 2024.
Abstract. We present new Dirichlet–Neumann and Neumann–Dirichlet algorithms with a time domain decomposition applied to unconstrained parabolic optimal control problems. After a spatial semidiscretization, we use the Lagrange multiplier approach to derive a coupled forward-backward optimality system, which can then be solved using a time domain decomposition. Due to the forward-backward structure of the optimality system, three variants can be found for the Dirichlet–Neumann and Neumann–Dirichlet algorithms. We analyze their convergence behavior and determine the optimal relaxation parameter for each algorithm. Our analysis reveals that the most natural algorithms are actually only good smoothers, and there are better choices which lead to efficient solvers. We illustrate our analysis with numerical experiments.


中文翻译:


抛物型最优控制问题的新时域分解方法 I:Dirichlet-Neumann 和 Neumann-Dirichlet 算法



《SIAM 数值分析杂志》,第 62 卷,第 4 期,第 2048-2070 页,2024 年 8 月。

抽象的。我们提出了新的狄利克雷-诺伊曼和诺伊曼-狄利克雷算法,并将时域分解应用于无约束抛物线最优控制问题。经过空间半离散化后,我们使用拉格朗日乘子方法导出耦合的前向-后向最优系统,然后可以使用时域分解来求解。由于最优系统的前向-后向结构,狄利克雷-诺伊曼和诺伊曼-狄利克雷算法可以找到三种变体。我们分析它们的收敛行为并确定每种算法的最佳松弛参数。我们的分析表明,最自然的算法实际上只是良好的平滑器,并且有更好的选择可以产生高效的求解器。我们用数值实验来说明我们的分析。
更新日期:2024-08-23
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