The SPQR RANK package contains routines that calculate the numerical rank of large, sparse, numerically rank-deficient matrices. The routines can also calculate orthonormal bases for numer-ical null spaces, approximate pseudoinverse solutions to least squares problems involving rank-deficient matrices, and basic solutions to these problems. The algorithms are based on SPQR from SuiteSparseQR (ACM Transactions on Mathematical Software 38, Article 8, 2011). SPQR is a high-performance routine for forming QR factorizations of large, sparse matrices. It returns an estimate for the numerical rank that is usually, but not always, correct. The new routines improve the accuracy of the numerical rank calculated by SPQR and reliably determine the nume...
AbstractAn algorithm is presented for computing a column permutation Π and a O̧R factorization AΠ = ...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...
The paper presents two parallel algorithms for finding the rank of a rectangular matrix and two para...
AbstractThe most widely used stable methods for numerical determination of the rank of a matrix A ar...
This article describes a suite of codes as well as associated testing and timing drivers for computi...
The numerical rank determination frequently occurs in matrix computation when the conventional exact...
We address the problem of solving linear least-squares problems min——Ax−b—— when A is a sparse m-by-...
In many applications—latent semantic indexing, for example—it is required to obtain a reduced rank a...
For the problems of low-rank matrix completion, the efficiency of the widely used nuclear norm techn...
A method is proposed for estimating the numerical rank of a sparse matrix. The method uses orthogona...
AbstractIn this paper we present an experimental comparison of several numerical tools for computing...
We introduce an algorithm for reliably computing quantities associated with several types of semipar...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
: Two algorithms are proposed for evaluating the rank of an arbitrary polynomial matrix. They rely u...
International audienceUpdating a linear least squares solution can be critical for near real-time si...
AbstractAn algorithm is presented for computing a column permutation Π and a O̧R factorization AΠ = ...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...
The paper presents two parallel algorithms for finding the rank of a rectangular matrix and two para...
AbstractThe most widely used stable methods for numerical determination of the rank of a matrix A ar...
This article describes a suite of codes as well as associated testing and timing drivers for computi...
The numerical rank determination frequently occurs in matrix computation when the conventional exact...
We address the problem of solving linear least-squares problems min——Ax−b—— when A is a sparse m-by-...
In many applications—latent semantic indexing, for example—it is required to obtain a reduced rank a...
For the problems of low-rank matrix completion, the efficiency of the widely used nuclear norm techn...
A method is proposed for estimating the numerical rank of a sparse matrix. The method uses orthogona...
AbstractIn this paper we present an experimental comparison of several numerical tools for computing...
We introduce an algorithm for reliably computing quantities associated with several types of semipar...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
: Two algorithms are proposed for evaluating the rank of an arbitrary polynomial matrix. They rely u...
International audienceUpdating a linear least squares solution can be critical for near real-time si...
AbstractAn algorithm is presented for computing a column permutation Π and a O̧R factorization AΠ = ...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...
The paper presents two parallel algorithms for finding the rank of a rectangular matrix and two para...