This article surveys preconditioning techniques for the iterative solution of large linear
systems, with a focus on algebraic methods suitable for general sparse matrices. Covered …

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 …

Accelerating numerical algorithms for solving sparse linear systems on parallel architectures
has attracted the attention of many researchers due to their applicability to many …

Algebraic multigrid (AMG) methods are often robust and effective solvers for solving the
large and sparse linear systems that arise from discretized PDEs and other problems …

Topology optimization is a powerful tool for global and multiscale design of structures,
microstructures, and materials. The computational bottleneck of topology optimization is the …

Abstract Motivated by the Cayley–Hamilton theorem, a novel adaptive procedure, called a
Power Sparse Approximate Inverse (PSAI) procedure, is proposed that uses a different …

Preconditioning for linear systems Page 1 PRECONDITIONING FOR LINEAR SYSTEMS
Giampaolo Mele Emil Ringh David Ek Federico Izzo Parikshit Upadhyaya Elias Jarlebring Page …

We discuss the parallel implementation of a two-level algebraic ILU (k)-based domain
decomposition preconditioner using the PETSc library. We present strategies to improve …

Numerical methods for subsurface modeling are currently being extended to account for
geomechanical effects. These include locally mass conservative discretizations such as …

The solution of large sparse linear systems is required in many scientific fields such as
computational fluid dynamics, computational electromagnetism, computational finance, etc …