When simulating a mechanism from science or engineering, or an industrial process, one is
frequently required to construct a mathematical model, and then resolve this model …

This article addresses the inference of physics models from data, from the perspectives of
inverse problems and model reduction. These fields develop formulations that integrate data …

The numerical solution of linear elliptic partial differential equations most often involves a
finite element or finite difference discretization. To preserve sparsity, the arising system is …

We are interested in the numerical solution of nonsymmetric linear systems arising from the
discretization of convection–diffusion partial differential equations with separable …

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 …

Abstract The Barzilai and Borwein gradient method has received a significant amount of
attention in different fields of optimization. This is due to its simplicity, computational …

Preconditioning for Krylov methods often relies on operator theory when mesh independent
estimates are looked for. The goal of this paper is to contribute to the long development of …

Matrices or operators in two-by-two block form with square blocks arise in numerous
important applications, such as in optimal control problems for PDEs. The problems are …

The conjugate gradient and minimum residual methods for self-adjoint problems in Hilbert
space are considered. Linear and superlinear convergence results with respect to both Q …

Hessian preconditioners are the key to efficient numerical solution of large-scale distributed
parameter PDE-constrained inverse problems with highly informative data. Such inverse …