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Uniform convergence of multigrid V-cycle iterations for indefinite and nonsymmetric problems

机译:不确定和非对称问题的多重网格V周期迭代的一致收敛

摘要

In this paper, we present an analysis of a multigrid method for nonsymmetric and/or indefinite elliptic problems. In this multigrid method various types of smoothers may be used. One type of smoother which we consider is defined in terms of an associated symmetric problem and includes point and line, Jacobi, and Gauss-Seidel iterations. We also study smoothers based entirely on the original operator. One is based on the normal form, that is, the product of the operator and its transpose. Other smoothers studied include point and line, Jacobi, and Gauss-Seidel. We show that the uniform estimates for symmetric positive definite problems carry over to these algorithms. More precisely, the multigrid iteration for the nonsymmetric and/or indefinite problem is shown to converge at a uniform rate provided that the coarsest grid in the multilevel iteration is sufficiently fine (but not depending on the number of multigrid levels).
机译:在本文中,我们对非对称和/或不确定椭圆问题的多重网格方法进行了分析。在这种多网格方法中,可以使用各种类型的平滑器。我们考虑的一种平滑器是根据相关的对称问题定义的,包括点和线,Jacobi和Gauss-Seidel迭代。我们还完全基于原始运算符来研究平滑器。一种是基于范式的形式,即运算符及其转置的乘积。研究的其他平滑器包括点和线,Jacobi和Gauss-Seidel。我们表明,对称正定问题的统一估计会延续到这些算法中。更准确地说,如果多级迭代中最粗糙的网格足够精细(但不取决于多网格级的数量),则用于非对称和/或不确定问题的多网格迭代将以均匀的速率收敛。

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