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 …

This paper presents a domain decomposition-type method for solving real symmetric
(Hermitian) eigenvalue problems in which we seek all eigenpairs in an interval [α; β] or a few …

Preconditioned iterative methods for numerical solution of large matrix eigenvalue problems
are increasingly gaining importance in various application areas, ranging from material …

Efficient computation of extreme eigenvalues of large-scale linear Hermitian eigenproblems
can be achieved by preconditioned conjugate gradient (PCG) methods. In this paper, we …

We propose a block preconditioned harmonic projection (BPHP) method for solving large-
scale nonlinear eigenproblems of the form T(λ)v=0. Similar to classical preconditioned …

In block eigenvalue algorithms, such as the subspace iteration algorithm and the locally
optimal block preconditioned conjugate gradient (LOBPCG) algorithm, a large block size is …

An efficient and robust restart strategy is important for any Krylov-based method for
eigenvalue problems. The tensor infinite Arnoldi method (TIAR) is a Krylov-based method for …

Variational principles are very powerful tools when studying self-adjoint linear operators on
a Hilbert space H. Bounds for eigenvalues, comparison theorems, interlacing results, and …

This paper addresses the question of what exactly is an analogue of the preconditioned
steepest descent (PSD) algorithm in the case of a symmetric indefinite system with an SPD …

We give a general approach of the Jacobi-Davidson method based on any vector iteration to
solve a linear or nonlinear eigenvalue problem. The influence of solving the Jacobi …