The numerical rank determination frequently occurs in matrix computation when the conventional exact rank of a hidden matrix is desired to be recovered. This paper presents a Matlab package RankRev that implements two efficient algorithms for computing the numerical rank and numerical subspaces of a matrix along with updating/downdating capabilities for making adjustment to the results when a row or column is inserted/deleted. The package and the underlying algorithms are accurate, reliable, and much more efficient than the singular value decomposition when the matrix is of low rank or low nullity
Rank-revealing ULV and URV factorizations are useful tools to determine the rank and to compute ...
Erbay, Hasan/0000-0002-7555-541XWOS: 000236068400047The truncated ULV decomposition (TULVD) provides...
International audienceUpdating a linear least squares solution can be critical for near real-time si...
The numerical rank determination frequently occurs in matrix computation when the conventional exact...
Erbay, Hasan/0000-0002-7555-541XWOS: 000273521400010This article presents an URV-based matrix decomp...
AbstractThe most widely used stable methods for numerical determination of the rank of a matrix A ar...
The SPQR RANK package contains routines that calculate the numerical rank of large, sparse, numerica...
Erbay, Hasan/0000-0002-7555-541XWOS: 000240086000013Traditionally, the singular value decomposition ...
A method is proposed for estimating the numerical rank of a sparse matrix. The method uses orthogona...
Randomized algorithms are given for computing the rank of a matrix over a field of characteristic ze...
We consider the problem of computing the rank of an m × nmatrix A over a field. We present a randomi...
Usage of the Sherman-Morrison-Woodbury formula to update linear systems after low rank modifications...
Completing a matrix from a small subset of its entries, i.e., matrix completion is a challenging pro...
For the problems of low-rank matrix completion, the efficiency of the widely used nuclear norm techn...
Abstract. We introduce the problem of rank matrix factorisation (RMF). That is, we consider the deco...
Rank-revealing ULV and URV factorizations are useful tools to determine the rank and to compute ...
Erbay, Hasan/0000-0002-7555-541XWOS: 000236068400047The truncated ULV decomposition (TULVD) provides...
International audienceUpdating a linear least squares solution can be critical for near real-time si...
The numerical rank determination frequently occurs in matrix computation when the conventional exact...
Erbay, Hasan/0000-0002-7555-541XWOS: 000273521400010This article presents an URV-based matrix decomp...
AbstractThe most widely used stable methods for numerical determination of the rank of a matrix A ar...
The SPQR RANK package contains routines that calculate the numerical rank of large, sparse, numerica...
Erbay, Hasan/0000-0002-7555-541XWOS: 000240086000013Traditionally, the singular value decomposition ...
A method is proposed for estimating the numerical rank of a sparse matrix. The method uses orthogona...
Randomized algorithms are given for computing the rank of a matrix over a field of characteristic ze...
We consider the problem of computing the rank of an m × nmatrix A over a field. We present a randomi...
Usage of the Sherman-Morrison-Woodbury formula to update linear systems after low rank modifications...
Completing a matrix from a small subset of its entries, i.e., matrix completion is a challenging pro...
For the problems of low-rank matrix completion, the efficiency of the widely used nuclear norm techn...
Abstract. We introduce the problem of rank matrix factorisation (RMF). That is, we consider the deco...
Rank-revealing ULV and URV factorizations are useful tools to determine the rank and to compute ...
Erbay, Hasan/0000-0002-7555-541XWOS: 000236068400047The truncated ULV decomposition (TULVD) provides...
International audienceUpdating a linear least squares solution can be critical for near real-time si...