92 citations · 113 across the 9 of their papers we have counts for
8 papers · 1 filter
Graph Neural Networks and Applied Linear Algebra
Nicholas S. Moore, Eric C. Cyr, Peter Ohm +2
Sparse matrix computations are ubiquitous in scientific computing. With the recent interest in scientific machine learning, it is natural to ask how sparse matrix computations can…
Monolithic Algebraic Multigrid Preconditioners for the Stokes Equations
Alexey Voronin, Scott MacLachlan, Luke N. Olson +1
We investigate a novel monolithic algebraic multigrid (AMG) preconditioner for the Taylor-Hood () and Scott-Vogelius ($\pmb{\mathbb{P}}_2/\mathbb{P…
Compression and Reduced Representation Techniques for Patch-Based Relaxation
Graham Harper, Ray Tuminaro
Patch-based relaxation refers to a family of methods for solving linear systems which partitions the matrix into smaller pieces often corresponding to groups of adjacent degrees of…
Low-order preconditioning of the Stokes equations
Alexey Voronin, Yunhui He, Scott MacLachlan +2
A well-known strategy for building effective preconditioners for higher-order discretizations of some PDEs, such as Poisson's equation, is to leverage effective preconditioners for…
Monolithic Multigrid for Magnetohydrodynamics
J. H. Adler, T. Benson, E. C. Cyr +3
The magnetohydrodynamics (MHD) equations model a wide range of plasma physics applications and are characterized by a nonlinear system of partial differential equations that strong…
An Algebraic Sparsified Nested Dissection Algorithm Using Low-Rank Approximations
Léopold Cambier, Chao Chen, Erik G Boman +3
We propose a new algorithm for the fast solution of large, sparse, symmetric positive-definite linear systems, spaND -- sparsified Nested Dissection. It is based on nested dissecti…
An algebraic multigrid method for mixed discretizations of the Navier-Stokes equations
Andrey Prokopenko, Raymond S. Tuminaro
Algebraic multigrid (AMG) preconditioners are considered for discretized systems of partial differential equations (PDEs) where unknowns associated with different physical quantiti…
Krylov-subspace recycling via the POD-augmented conjugate-gradient method
Kevin Carlberg, Virginia Forstall, Ray Tuminaro
This work presents a new Krylov-subspace-recycling method for efficiently solving sequences of linear systems of equations characterized by varying right-hand sides and symmetric-p…