AbstractWe describe a set of procedures for computing and updating an LU factorization of a sparse matrix A, where A may be square (possibly singular) or rectangular. The procedures include a Markowitz factorization and a Bartels-Golub update, similar to those of Reid (1976, 1982). The updates provided are addition, deletion or replacement of a row or column of A, and rank-one modification. (Previously, column replacement has been the only update available.)Various design features of the implementation (LUSOL) are described, and computational comparisons are made with the LA05 and MA28 packages of Reid (1976) and Duff (1977)
International audience—The applicability of many signal processing and data analysis techniques is l...
In this paper, we address the problem of preconditioning sequences of large sparse indefinite system...
AbstractIn this work, the solution of a large sparse linear system of equations with an arbitrary sp...
AbstractWe describe a set of procedures for computing and updating an LU factorization of a sparse m...
AbstractWe examine a direct method based on an LU decomposition of the rectangular coefficient matri...
AbstractWe describe how to maintain the triangular factor of a sparse QR factorization when columns ...
In this paper we introduce a new method for the computation of KKT matrices that arise from solving ...
The objective of this work is to compare the developed LU factorization update with results from MIN...
AbstractA new sparse approximate triangular factorization technique for solving large sparse linear ...
AbstractWe describe a direct method for solving sparse linear least squares problems. The storage re...
Given a sparse, symmetric positive definite matrix C and an associated sparse Cholesky factorization...
In this thesis we will present an effective method for solving systems of linear equations with larg...
International audienceWe present new algorithms to detect and correct errors in the lower-upper fact...
We discuss the use of hypergraph partitioning based methods in fill-reducing orderings of sparse mat...
Abstract. Iterative methods are often suitable for solving least-squares problems min kAx, bk2, wher...
International audience—The applicability of many signal processing and data analysis techniques is l...
In this paper, we address the problem of preconditioning sequences of large sparse indefinite system...
AbstractIn this work, the solution of a large sparse linear system of equations with an arbitrary sp...
AbstractWe describe a set of procedures for computing and updating an LU factorization of a sparse m...
AbstractWe examine a direct method based on an LU decomposition of the rectangular coefficient matri...
AbstractWe describe how to maintain the triangular factor of a sparse QR factorization when columns ...
In this paper we introduce a new method for the computation of KKT matrices that arise from solving ...
The objective of this work is to compare the developed LU factorization update with results from MIN...
AbstractA new sparse approximate triangular factorization technique for solving large sparse linear ...
AbstractWe describe a direct method for solving sparse linear least squares problems. The storage re...
Given a sparse, symmetric positive definite matrix C and an associated sparse Cholesky factorization...
In this thesis we will present an effective method for solving systems of linear equations with larg...
International audienceWe present new algorithms to detect and correct errors in the lower-upper fact...
We discuss the use of hypergraph partitioning based methods in fill-reducing orderings of sparse mat...
Abstract. Iterative methods are often suitable for solving least-squares problems min kAx, bk2, wher...
International audience—The applicability of many signal processing and data analysis techniques is l...
In this paper, we address the problem of preconditioning sequences of large sparse indefinite system...
AbstractIn this work, the solution of a large sparse linear system of equations with an arbitrary sp...