In this paper we introduce a new approach for reducing communication in Krylov subspace
methods that consists of enlarging the Krylov subspace by a maximum of t vectors per …

This paper introduces a robust preconditioner for general sparse matrices based on low‐
rank approximations of the Schur complement in a Domain Decomposition framework. In …

This paper presents a parallel preconditioning method for distributed sparse linear systems,
based on an approximate inverse of the original matrix, that adopts a general framework of …

Randomized methods are becoming increasingly popular in numerical linear algebra.
However, few attempts have been made to use them in developing preconditioners. Our …

We are interested in the Monte Carlo (MC) sampling of discretized elliptic partial differential
equations (PDEs) with random variable coefficients. The dominant computational load of …

In this paper we present a class of robust and fully algebraic two-level preconditioners for
symmetric positive definite (SPD) matrices. We introduce the notion of algebraic local …

This paper discusses parGeMSLR, a C++/MPI software library for the solution of sparse
systems of linear algebraic equations via preconditioned Krylov subspace methods in …

We propose a variant of the generalized minimal residual (GMRES) method for solving
linear systems of equations with one or multiple right-hand sides. Our method is based on …

A preconditioning strategy is proposed for the iterative solve of large numbers of linear
systems with variable matrix and right-hand side which arise during the computation of …

The performance of an algorithm on any architecture is dependent on the processing unit's
speed for performing floating point operations (flops) and the speed of accessing memory …