Add to ORKGThis paper develops a suite of algorithms for constructing low-rank approximations of an input matrix from a random linear image of the matrix, called a sketch. These methods can preserve structural properties of the input matrix, such as positive-semidefiniteness, and they can produce approximations with a user-specified rank. The algorithms are simple, accurate, numerically stable, and provably correct. Moreover, each method is accompanied by an informative error bound that allows users to select parameters a priori to achieve a given approximation quality. These claims are supported by computer experiments
A methodology for using random sketching in the context of model order reduction for high-dimensiona...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provi...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
This paper describes a suite of algorithms for constructing low-rank approximations of an input matr...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-reve...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the e...
It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-di...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-rev...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
Randomized algorithms for low-rank matrix approximation are investigated, with the emphasis on the f...
Randomized sampling techniques have recently proved capable of efficiently solving many standard pro...
Low-rank matrix approximation is an integral component of tools such as principal component analysis...
A methodology for using random sketching in the context of model order reduction for high-dimensiona...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provi...
This paper develops a suite of algorithms for constructing low-rank approximations of an input matri...
This paper describes a suite of algorithms for constructing low-rank approximations of an input matr...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-reve...
In this work, we propose a new randomized algorithm for computing a low-rank approximation to a give...
We consider the problem of approximating a given matrix by a low-rank matrix so as to minimize the e...
It is often desirable to reduce the dimensionality of a large dataset by projecting it onto a low-di...
Low-rank matrix approximations, such as the truncated singular value decomposition and the rank-rev...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
Randomized algorithms for low-rank matrix approximation are investigated, with the emphasis on the f...
Randomized sampling techniques have recently proved capable of efficiently solving many standard pro...
Low-rank matrix approximation is an integral component of tools such as principal component analysis...
A methodology for using random sketching in the context of model order reduction for high-dimensiona...
This paper argues that randomized linear sketching is a natural tool for on-the-fly compression of d...
We study the frequent problem of approximating a target matrix with a matrix of lower rank. We provi...