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arXiv 1504.00907math.NAcs.NA
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离散不连续伽辽金粗网格用于域分解

The Discretely-Discontinuous Galerkin Coarse Grid for Domain Decomposition

University of British Columbia(不列颠哥伦比亚大学)

Essex Edwards, Robert Bridson

AI总结:

本文提出一种代数方法,通过将问题划分为子域并构造粗基,实现高效的域分解加速,证明了粗解的高阶收敛误差界,并在多种PDE上表现出最优缩放性。

摘要:

我们提出一种代数方法,用于构造高效的粗网格修正以加速域分解。粗问题由原始矩阵和一小组输入向量构成,这些向量张成低次多项式空间,但不使用进一步的网格或连续泛函知识。我们通过将问题划分为子域,并将每个输入向量在每个子域上的限制作为其自身的基函数,来构造粗基。该基类似于在子域大小元素上的不连续伽辽金基。通过伽辽金投影构建粗问题,我们证明了粗解的高阶收敛误差界。在两阶段对称乘法重叠Schwarz预处理器中,所得共轭梯度求解器表现出最优缩放性。收敛需要常数次迭代,与细问题规模无关,在多种标量和向量值的二阶和四阶PDE上均有效。

英文摘要:

We present an algebraic method for constructing a highly effective coarse grid correction to accelerate domain decomposition. The coarse problem is constructed from the original matrix and a small set of input vectors that span a low-degree polynomial space, but no further knowledge of meshes or continuous functionals is used. We construct a coarse basis by partitioning the problem into subdomains and using the restriction of each input vector to each subdomain as its own basis function. This basis resembles a Discontinuous Galerkin basis on subdomain-sized elements. Constructing the coarse problem by Galerkin projection, we prove a high-order convergent error bound for the coarse solutions. Used in a two-level symmetric multiplicative overlapping Schwarz preconditioner, the resulting conjugate gradient solver shows optimal scaling. Convergence requires a constant number of iterations, independent of fine problem size, on a range of scalar and vector-valued second-order and fourth-order PDEs.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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