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 …

The computational solution of problems can be restricted by the availability of solution
methods for linear (ized) systems of equations. In conjunction with iterative methods …

Solution of sequences of complex symmetric linear systems of the form Ajxj= bj, j= 0,..., s, Aj=
A+ αjEj, A Hermitian, E0,..., E a complex diagonal matrices and α0,..., αa scalar complex …

Standard preconditioners, like incomplete factorizations, perform well when the coefficient
matrix is diagonally dominant, but often fail on general sparse matrices. We experiment with …

Large sparse linear systems of equations are ubiquitous in science, engineering and
beyond. This open access monograph focuses on factorization algorithms for solving such …

In this paper, we propose continuous time neural network and modified continuous time
neural networks for solving a multi-linear system with M-tensors. Theoretically, we prove that …

This paper describes and tests a parallel message-passing code for constructing sparse
approximate inverse preconditioners using Frobenius norm minimization. The sparsity …

We describe a novel technique for computing a sparse incomplete factorization of a general
symmetric positive definite matrix A. The factorization is not based on the Cholesky algorithm …

A common problem in statistics is to compute sample vectors from a multivariate Gaussian
distribution with zero mean and a given covariance matrix A. A canonical approach to the …

Dependence between iterations in sparse computations causes inefficient use of memory
and computation resources. This paper proposes sparse fusion, a technique that generates …