论文标题

未记录的回收块Krylov子空间方法用于移位系统

Unprojected recycled block Krylov subspace methods for shifted systems

论文作者

Burke, Liam

论文摘要

在[Soodhalter,SISC,2016年]中首次提出了使用块Krylov子空间方法计算使用子空间回收的一系列线性系统的解决方案,其中提出了一个回收的移位块GMRES算法(RSBGMRES)。这样的方法使用移位系统与Sylvester方程的等效性,并利用由Sylvester Operator生成的Block Krylov子空间的移位不变性。这避免了初始残留物跨越相同的子空间的需求,并允许使用可行的Krylov子空间方法,并通过回收利用用于求解移位系统的序列。 在本文中,我们建议使用未投影的Krylov子空间开发这些类型的方法。在这样做时,我们展示了如何克服基于预计的Krylov子空间(例如RSBGMRES)开发方法的困难,同时还允许实用方法适合众所周知的残留投影框架。此外,当投影仪申请昂贵时,未提示的方法是有利的,这使它们对高性能计算应用具有重大兴趣。我们开发了未投影的RSBFOM和未记录的RSBGMRE。我们还开发了一个程序,用于通过增强的块Krrylov子空间提取依赖移位的谐波ritz向量,以便在每个算法的每个循环之后选择新的回收子空间。数值实验证明了我们方法的有效性。

The use of block Krylov subspace methods for computing the solution to a sequence of shifted linear systems using subspace recycling was first proposed in [Soodhalter, SISC 2016], where a recycled shifted block GMRES algorithm (rsbGMRES) was proposed. Such methods use the equivalence of the shifted system to a Sylvester equation and exploit the shift invariance of the block Krylov subspace generated from the Sylvester operator. This avoids the need for initial residuals to span the same subspace and allows for a viable restarted Krylov subspace method with recycling for solving sequences of shifted systems. In this paper we propose to develop these types of methods using unprojected Krylov subspaces. In doing so we show how one can overcome the difficulties associated with developing methods based on projected Krylov subspaces such as rsbGMRES, while also allowing for practical methods to fit within a well known residual projection framework. In addition, unprojected methods are known to be advantageous when the projector is expensive to apply, making them of significant interest for High-Performance Computing applications. We develop an unprojected rsbFOM and unprojected rsbGMRES. We also develop a procedure for extracting shift dependent harmonic Ritz vectors over an augmented block Krylov subspace for shifted systems yielding an approach for selecting a new recycling subspace after each cycle of the algorithm. Numerical experiments demonstrate the effectiveness of our methods.

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