This work considers the convergence of GMRES for non-singular problems. GMRES is interpreted as the GCR method which allows for simple proofs of the convergence estimates. Preconditioning and weighted norms within GMRES are considered. The objective is to provide a way of choosing the preconditioner and GMRES norm that ensure fast convergence. The main focus of the article is on Hermitian preconditioning (even for non-Hermitian problems). It is proposed to choose a Hermitian preconditioner H and to apply GMRES in the inner product induced by H. If moreover, the problem matrix A is positive definite, then a new convergence bound is proved that depends only on how well H preconditions the Hermitian part of A, and on how non-Hermitian A is. In particular, if a scalable preconditioner is known for the Hermitian part of A, then the proposed method is also scalable. This result is illustrated numerically.
翻译:本文考虑GMRES方法在非奇异问题中的收敛性。GMRES方法被解释为GCR方法,其允许简单的收敛估计证明。考虑GMRES中的预条件和加权范数。目标是提供一种选择预条件和GMRES范数以确保快速收敛的方法。文章的主要焦点是Hermitian(厄米)预处理(即使是针对非Hermitian问题)。建议选择一个Hermitian预处理器H,并在H诱导的内积下应用GMRES方法。如果问题矩阵A还是正定的,则证明了一个新的收敛界限,该界限仅取决于H预处理A的Hermitian部分的效果以及A不具有Hermitian性质的程度。特别是,如果已知Hermitian部分的可伸缩预条件器,则所提出的方法也是可伸缩的。此结果通过数值实例予以说明。