In this paper we review the technique of hierarchical matrices and put it into the context of black-box solvers for large linear systems. Numerical examples for several classes of problems from medium- to large-scale illustrate the applicability and efficiency of this technique. We compare the results with those of several direct solvers (which typically scale quadratically in the matrix size) as well as an iterative solver (algebraic multigrid) which scales linearly (if it converges in O(1) steps)
. For a sparse linear system Ax = b, preconditioners of the form C = D + L + U , where D is the blo...
Iterative methods are currently the solvers of choice for large sparse linear systems of equations. ...
Many physical phenomena may be studied through modeling and numerical simulations, commonplace in sc...
Factorization based preconditioning algorithms, most notably incomplete LU (ILU) factorization, have...
Although some preconditioners are available for solving dense linear systems, there are still many m...
The dissertation presents some fast direct solvers and efficient preconditioners mainly for sparse m...
Solving large-scale systems of linear equations [] { } {}bxA = is one of the most expensive and cr...
This article surveys preconditioning techniques for the iterative solution of large linear systems, ...
Inversion of sparse matrices with standard direct solve schemes is robust but computationally expens...
University of Minnesota Ph.D. dissertation. June 2015. Major: Computer Science. Advisor: Yousef Saad...
International audienceWe consider the problem of choosing low-rank factorizations in data sparse mat...
AbstractWe review the influence of the advent of high-performance computing on the solution of linea...
This book describes, in a basic way, the most useful and effective iterative solvers and appropriate...
AbstractThis paper introduces several strategies to deal with pivot blocks in multi-level block inco...
AbstractThe main idea of the “black box” approach in exact linear algebra is to reduce matrix proble...
. For a sparse linear system Ax = b, preconditioners of the form C = D + L + U , where D is the blo...
Iterative methods are currently the solvers of choice for large sparse linear systems of equations. ...
Many physical phenomena may be studied through modeling and numerical simulations, commonplace in sc...
Factorization based preconditioning algorithms, most notably incomplete LU (ILU) factorization, have...
Although some preconditioners are available for solving dense linear systems, there are still many m...
The dissertation presents some fast direct solvers and efficient preconditioners mainly for sparse m...
Solving large-scale systems of linear equations [] { } {}bxA = is one of the most expensive and cr...
This article surveys preconditioning techniques for the iterative solution of large linear systems, ...
Inversion of sparse matrices with standard direct solve schemes is robust but computationally expens...
University of Minnesota Ph.D. dissertation. June 2015. Major: Computer Science. Advisor: Yousef Saad...
International audienceWe consider the problem of choosing low-rank factorizations in data sparse mat...
AbstractWe review the influence of the advent of high-performance computing on the solution of linea...
This book describes, in a basic way, the most useful and effective iterative solvers and appropriate...
AbstractThis paper introduces several strategies to deal with pivot blocks in multi-level block inco...
AbstractThe main idea of the “black box” approach in exact linear algebra is to reduce matrix proble...
. For a sparse linear system Ax = b, preconditioners of the form C = D + L + U , where D is the blo...
Iterative methods are currently the solvers of choice for large sparse linear systems of equations. ...
Many physical phenomena may be studied through modeling and numerical simulations, commonplace in sc...