Randomized algorithms are given for computing the rank of a matrix over a field of characteristic zero. The matrix is treated as a black box. Only the capability to compute matrix x column-vector and row-vector x matrix products is used. The methods are exact, sometimes called seminumeric. They are appropriate for example for matrices with integer or rational entries. The rank algorithms are probabilistic of the Las Vegas type; the correctness of the result is guaranteed
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
AbstractGiven an m×n matrix A and a positive integer k, we describe a randomized procedure for the a...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...
Randomized algorithms are given for computing the rank of a matrix over a field of characteristic ze...
Abstract. Randomized algorithms are given for computing the rank of a matrix over a field of charact...
We consider the problem of computing the rank of an m × nmatrix A over a field. We present a randomi...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
This survey describes probabilistic algorithms for linear algebraic computations, such as factorizin...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
The purpose of this text is to provide an accessible introduction to a set of recently developed alg...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
AbstractGiven an m×n matrix A and a positive integer k, we describe a randomized procedure for the a...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...
Randomized algorithms are given for computing the rank of a matrix over a field of characteristic ze...
Abstract. Randomized algorithms are given for computing the rank of a matrix over a field of charact...
We consider the problem of computing the rank of an m × nmatrix A over a field. We present a randomi...
Matrices of huge size and low rank are encountered in applications from the real world where large s...
This survey describes probabilistic algorithms for linear algebraic computations, such as factorizin...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
AbstractWe introduce a randomized procedure that, given an m×n matrix A and a positive integer k, ap...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
The purpose of this text is to provide an accessible introduction to a set of recently developed alg...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
International audienceWe want to achieve efficient exact computations, such as the rank, of sparse m...
We propose new effective randomized algorithms for some fundamental matrix computations such as prec...
AbstractGiven an m×n matrix A and a positive integer k, we describe a randomized procedure for the a...
The development of randomized algorithms for numerical linear algebra, e.g. for computing approximat...