Add to ORKGRecently proposed methods for ordering sparse symmetric matrices are discussed and their performance is compared with that of the Minimum Degree and the Minimum Local Fill algorithms. It is shown that these methods applied to symmetrized modified nodal analysis matrices yield orderings significantly better than those obtained from the Minimum Degree and Minimum Local Fill algorithms, in some cases at virtually no extra computational cost. 1
This paper addresses issues related to the minimization of the computational burden in terms of both...
This paper addresses issues related to the minimization of the computational burden in terms of both...
Sparse-matrix solution is a dominant part of execution time in simulating VLSI circuits by a detaile...
Local algorithms for obtaining a pivot ordering for sparse symmetric coefficient matrices are review...
The minimum degree ordering is one of the most widely used algorithms to preorder a symmetric sparse...
This paper presents a new node ordering algorithm to enhance sparse vector methods. The proposed tec...
When performing sparse matrix factorization, the ordering of matrix rows and columns has a dramatic ...
235 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.The effective application of ...
235 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.The effective application of ...
Abstract. Computer simulations of realistic applications usually require solving a set of non-linear...
In this paper we provide a robust reordering scheme for sparse matrices. The scheme relies on the no...
The ability of computers to solve hitherto intractable problems and simulate complex processes using...
The ordering of large sparse symmetric matrices for small pro"le and wavefront or for small ban...
The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse...
The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse ...
This paper addresses issues related to the minimization of the computational burden in terms of both...
This paper addresses issues related to the minimization of the computational burden in terms of both...
Sparse-matrix solution is a dominant part of execution time in simulating VLSI circuits by a detaile...
Local algorithms for obtaining a pivot ordering for sparse symmetric coefficient matrices are review...
The minimum degree ordering is one of the most widely used algorithms to preorder a symmetric sparse...
This paper presents a new node ordering algorithm to enhance sparse vector methods. The proposed tec...
When performing sparse matrix factorization, the ordering of matrix rows and columns has a dramatic ...
235 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.The effective application of ...
235 p.Thesis (Ph.D.)--University of Illinois at Urbana-Champaign, 1997.The effective application of ...
Abstract. Computer simulations of realistic applications usually require solving a set of non-linear...
In this paper we provide a robust reordering scheme for sparse matrices. The scheme relies on the no...
The ability of computers to solve hitherto intractable problems and simulate complex processes using...
The ordering of large sparse symmetric matrices for small pro"le and wavefront or for small ban...
The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse...
The Conjugate Gradient (CG) algorithm is perhaps the best-known iterative technique to solve sparse ...
This paper addresses issues related to the minimization of the computational burden in terms of both...
This paper addresses issues related to the minimization of the computational burden in terms of both...
Sparse-matrix solution is a dominant part of execution time in simulating VLSI circuits by a detaile...