分布式椭圆最优控制问题的质量块离散化和求解器

IF 1.8 3区 数学 Q1 MATHEMATICS
Ulrich Langer, Richard Löscher, Olaf Steinbach, Huidong Yang
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引用次数: 0

摘要

本文的目的是研究在有限元离散化中使用质量块与网格尺寸对由标准跟踪型分布式椭圆最优控制问题所产生的简化一阶最优系统进行正则化的影响,其中涉及解所依赖的正则化(代价)参数。我们证明,质量块不会影响期望状态与计算有限元状态之间的误差,但会导致舒尔补全系统,从而实现快速的矩阵-向量乘法。我们的研究表明,在嵌套迭代设置中使用舒尔补全预处理共轭梯度法,可以在复杂度方面获得渐近最优的求解器。虽然提出的方法与给定目标的规则性无关,但我们特别关注不属于状态空间的不连续期望状态。然而,相应的控制属于,而成本为 。这就促使我们使用来平衡误差和我们愿意接受的最大成本。这可以嵌入到一系列细化有限元网格的嵌套迭代过程中,以便同时控制误差和成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Mass‐lumping discretization and solvers for distributed elliptic optimal control problems
The purpose of this article is to investigate the effects of the use of mass‐lumping in the finite element discretization with mesh size of the reduced first‐order optimality system arising from a standard tracking‐type, distributed elliptic optimal control problem with regularization, involving a regularization (cost) parameter on which the solution depends. We show that mass‐lumping will not affect the error between the desired state and the computed finite element state , but will lead to a Schur‐complement system that allows for a fast matrix‐by‐vector multiplication. We show that the use of the Schur‐complement preconditioned conjugate gradient method in a nested iteration setting leads to an asymptotically optimal solver with respect to the complexity. While the proposed approach is applicable independently of the regularity of the given target, our particular interest is in discontinuous desired states that do not belong to the state space. However, the corresponding control belongs to whereas the cost as . This motivates to use in order to balance the error and the maximal costs we are willing to accept. This can be embedded into a nested iteration process on a sequence of refined finite element meshes in order to control the error and the cost simultaneously.
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来源期刊
CiteScore
3.40
自引率
2.30%
发文量
50
审稿时长
12 months
期刊介绍: Manuscripts submitted to Numerical Linear Algebra with Applications should include large-scale broad-interest applications in which challenging computational results are integral to the approach investigated and analysed. Manuscripts that, in the Editor’s view, do not satisfy these conditions will not be accepted for review. Numerical Linear Algebra with Applications receives submissions in areas that address developing, analysing and applying linear algebra algorithms for solving problems arising in multilinear (tensor) algebra, in statistics, such as Markov Chains, as well as in deterministic and stochastic modelling of large-scale networks, algorithm development, performance analysis or related computational aspects. Topics covered include: Standard and Generalized Conjugate Gradients, Multigrid and Other Iterative Methods; Preconditioning Methods; Direct Solution Methods; Numerical Methods for Eigenproblems; Newton-like Methods for Nonlinear Equations; Parallel and Vectorizable Algorithms in Numerical Linear Algebra; Application of Methods of Numerical Linear Algebra in Science, Engineering and Economics.
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