Sparse linear least squares problems containing a few relatively dense rows occur frequently in practice. Straightforward solution of these problems could cause catastrophic fill and delivers extremely poor performance. This paper studies a scheme for solving such problems efficiently by handling dense rows and sparse rows separately. How a sparse matrix is partitioned into dense rows and sparse rows determines the efficiency of the overall solution process. A new algorithm is proposed to find a partition of a sparse matrix which leads to satisfactory or even optimal performance. Extensive numerical experiments are performed to demonstrate the effectiveness of the proposed scheme. A MATLAB implementation is included
In this dissertation, we describe LSHSS, a robust solver based on the Hermitian and skew-Hermitian s...
Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readil...
International audienceIn this paper, we are interested in computing the solution of an overdetermine...
AbstractWe examine a direct method based on an LU decomposition of the rectangular coefficient matri...
AbstractWe describe a direct method for solving sparse linear least squares problems. The storage re...
AbstractWe consider systems of equations of the form AATx = b, where A is a sparse matrix having a s...
Abstract. We present a fast algorithm for linear least squares problems governed by hierarchi-cally ...
Large-scale overdetermined linear least squares problems arise in many practical applications. One p...
Abstract. We present a fast algorithm for linear least squares problems governed by hierarchi-cally ...
We recently introduced a sparse stretching strategy for handling dense rows that can arise in large-...
The solution of nearly square overdetermined linear systems is studied. The sparse QR technique is c...
Abstract. On many high-speed computers the dense matrix technique is preferable to sparse matrix tec...
AbstractWe present a new parallel algorithm for computing a least-squares solution to a sparse overd...
. The linear least squares problem arises in many areas of sciences and engineerings. When the coef...
If A is the (sparse) coefficient matrix of linear equality constraints, for what nonsingular T is fi...
In this dissertation, we describe LSHSS, a robust solver based on the Hermitian and skew-Hermitian s...
Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readil...
International audienceIn this paper, we are interested in computing the solution of an overdetermine...
AbstractWe examine a direct method based on an LU decomposition of the rectangular coefficient matri...
AbstractWe describe a direct method for solving sparse linear least squares problems. The storage re...
AbstractWe consider systems of equations of the form AATx = b, where A is a sparse matrix having a s...
Abstract. We present a fast algorithm for linear least squares problems governed by hierarchi-cally ...
Large-scale overdetermined linear least squares problems arise in many practical applications. One p...
Abstract. We present a fast algorithm for linear least squares problems governed by hierarchi-cally ...
We recently introduced a sparse stretching strategy for handling dense rows that can arise in large-...
The solution of nearly square overdetermined linear systems is studied. The sparse QR technique is c...
Abstract. On many high-speed computers the dense matrix technique is preferable to sparse matrix tec...
AbstractWe present a new parallel algorithm for computing a least-squares solution to a sparse overd...
. The linear least squares problem arises in many areas of sciences and engineerings. When the coef...
If A is the (sparse) coefficient matrix of linear equality constraints, for what nonsingular T is fi...
In this dissertation, we describe LSHSS, a robust solver based on the Hermitian and skew-Hermitian s...
Large-scale optimization algorithms frequently require sparse Hessian matrices that are not readil...
International audienceIn this paper, we are interested in computing the solution of an overdetermine...