43 citations · 113 across the 11 of their papers we have counts for
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Neumann Series in GMRES and Algebraic Multigrid Smoothers
Stephen Thomas, Arielle Carr, Paul Mullowney +2
Neumann series underlie both Krylov methods and algebraic multigrid smoothers. A low-synch modified Gram-Schmidt (MGS)-GMRES algorithm is described that employs a Neumann series to…
ILU Smoothers for Low Mach Navier-Stokes Pressure Solvers
Stephen Thomas, Arielle Carr, Paul Mullowney +2
Incomplete LU (ILU) smoothers are effective in the algebraic multigrid (AMG) -cycle for reducing high-frequency components of the error. However, the requisite direct triangular…
Performance of Low Synchronization Orthogonalization Methods in Anderson Accelerated Fixed Point Solvers
Shelby Lockhart, David J. Gardner, Carol S. Woodward +2
Anderson Acceleration (AA) is a method to accelerate the convergence of fixed point iterations for nonlinear, algebraic systems of equations. Due to the requirement of solving a le…
Linear solvers for power grid optimization problems: a review of GPU-accelerated linear solvers
Kasia Swirydowicz, Eric Darve, Wesley Jones +5
The linear equations that arise in interior methods for constrained optimization are sparse symmetric indefinite and become extremely ill-conditioned as the interior method converg…
Low-Synch Gram-Schmidt with Delayed Reorthogonalization for Krylov Solvers
Daniel Bielich, Julien Langou, Stephen Thomas +3
The parallel strong-scaling of Krylov iterative methods is largely determined by the number of global reductions required at each iteration. The GMRES and Krylov-Schur algorithms e…
Two-Stage Gauss--Seidel Preconditioners and Smoothers for Krylov Solvers on a GPU cluster
Luc Berger-Vergiat, Brian Kelley, Sivasankaran Rajamanickam +5
Gauss-Seidel (GS) relaxation is often employed as a preconditioner for a Krylov solver or as a smoother for Algebraic Multigrid (AMG). However, the requisite sparse triangular solv…