The traditional metric for the efficiency of a numerical algorithm has been the number of
arithmetic operations it performs. Technological trends have long been reducing the time to …

Krylov subspace methods (KSMs) are iterative algorithms for solving large, sparse linear
systems and eigenvalue problems. Current KSMs rely on sparse matrix-vector multiply …

This book deals with numerical methods for solving large sparse linear systems of
equations, particularly those arising from the discretization of partial differential equations. It …

The problem of solving large, sparse, linear systems of algebraic equations is vital in
scientific computing, even for applications originating from quite different fields. A Survey of …

With exascale-level computation on the horizon, the art of predicting the cost of
computations has acquired a renewed focus. This task is especially challenging in the case …

In this paper we introduce s-step Conjugate Gradient Method for Symmetric and Positive
Definite (SPD) linear systems of equations and discuss its convergence. In the s-step …

Advancements in the field of high-performance scientific computing are necessary to
address the most important challenges we face in the 21st century. From physical modeling …

Solving systems of algebraic linear equations is among the most frequent problems in
scientific computing. It appears in many areas like physics, engineering, chemistry, biology …

In this paper a class of s-step methods for nonsymmetric linear systems of equations is
introduced. These methods are obtained from nonsymmetric generalizations of the …

A Journey through the History of Numerical Linear Algebra: Back Matter Page 1 Bibliography
[1] A. Abdelfattah, H. Anzt, A. Bouteiller, A. Danalis, JJ Dongarra, M. Gates, A. Haidar, J. Kurzak …