Augmented Block-Arnoldi Recycling CFD Solvers

TL;DR

This paper introduces an augmented block-Arnoldi recycling method for Krylov subspace solvers, utilizing advanced orthogonalization techniques to significantly reduce solver iterations in CFD applications.

Contribution

It proposes a novel inverse compact WY modified Gram-Schmidt orthogonalization within an augmented block-Arnoldi framework, improving efficiency of recycled Krylov methods.

Findings

Over 10× reduction in solver iterations in certain cases

Effective orthogonalization using inverse compact WY scheme

Recycling subspace eigen-spectrum can replace preconditioning

Abstract

One of the limitations of recycled GCRO methods is the large amount of computation required to orthogonalize the basis vectors of the newly generated Krylov subspace for the approximate solution when combined with those of the recycle subspace. Recent advancements in low synchronization Gram-Schmidt and generalized minimal residual algorithms, Swirydowicz et al.~\cite{2020-swirydowicz-nlawa}, Carson et al. \cite{Carson2022}, and Lund \cite{Lund2022}, can be incorporated, thereby mitigating the loss of orthogonality of the basis vectors. An augmented Arnoldi formulation of recycling leads to a matrix decomposition and the associated algorithm can also be viewed as a {\it block} Krylov method. Generalizations of both classical and modified block Gram-Schmidt algorithms have been proposed, Carson et al.~\cite{Carson2022}. Here, an inverse compact $WY$ modified Gram-Schmidt algorithm is applied for the inter-block orthogonalization scheme with a block lower triangular correction matrix $T_k$ at iteration $k$. When combined with a weighted (oblique inner product) projection step, the inverse compact $WY$ scheme leads to significant (over 10$\times$ in certain cases) reductions in the number of solver iterations per linear system. The weight is also interpreted in terms of the angle between restart residuals in LGMRES, as defined by Baker et al.\cite{Baker2005}. In many cases, the recycle subspace eigen-spectrum can substitute for a preconditioner.