Krylov methods for nonsymmetric linear systems

G Meurant, JD Tebbens - Cham: Springer, 2020 - Springer
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 …

Developing variable s-step CGNE and CGNR algorithms for non-symmetric linear systems

HS Kaveh, M Hajarian, AT Chronopoulos - Journal of the Franklin Institute, 2024 - Elsevier
As is well known, CGNE and CGNR methods as powerful techniques are now commonly
used for solving sparse non-symmetric linear systems. In this article, we propose the …

Scalable non-blocking Krylov solvers for extreme-scale computing

PR Eller - 2019 - ideals.illinois.edu
Krylov solvers are key kernels in many large-scale science and engineering applications for
solving sparse linear systems. Extreme-scale systems have many factors that increase …

[PDF][PDF] GM Bibliography

G Meurant - 2023 - gerard-meurant.fr
[16] P.-A. Absil, R. Mahony, and B. Andrews. Convergence of the iterates of descent
methods for analytic cost functions. SIAM J. Optim., 16 (2): 531–547, 2005.[17] A. Abu-Omar …

Distributed GPU Based Matrix Power Kernel for Geoscience Applications

A Anciaux Sedrakian, T Guignon - SPE Reservoir Simulation …, 2021 - onepetro.org
High-performance computing is at the heart of digital technology which allows to simulate
complex physical phenomena. The current trend for hardware architectures is toward …

[HTML][HTML] IFP Energies Nouvelles Conference: SimRace 2015: Numerical Methods and High Performance Computing for Industrial Fluid Flows

S de Chaisemartin, G Allaire - Oil & Gas Science …, 2017 - ogst.ifpenergiesnouvelles.fr
Numerical simulation faces several challenges today. The different topics it relies on
(scientific modelling, applied mathematics, High Performance Computing (HPC) and …