Add to ORKGInternational audienceStructured low-rank approximation is the problem of minimizing a weighted Frobenius distance to a given matrix among all matrices of fixed rank in a linear space of matrices. We study exact solutions to this problem by way of computational algebraic geometry. A particular focus lies on Hankel matrices, Sylvester matrices and generic linear spaces
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
Hankel matrices are closely related to linear time-invariant (LTI) models, which are widely used in ...
In this paper we illustrate some optimization challenges in the structured low rank approximation (S...
International audienceStructured Low-Rank Approximation is a problem arising in a wide range of appl...
Structured Low-Rank Approximation is a problem arising in a wide range of applications in Numerical ...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
Abstract. We consider the problem of approximating an affinely structured matrix, for example, a Han...
A number of problems in system theory, signal processing, and computer algebra fit into a generic st...
The low-rank approximation problem is to approximate optimally, with respect to some norm, a matrix ...
AbstractThis paper concerns the construction of a structured low rank matrix that is nearest to a gi...
Abstract—The low-rank approximation problem is to approx-imate optimally, with respect to some norm,...
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the dat...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
Hankel matrices are closely related to linear time-invariant (LTI) models, which are widely used in ...
In this paper we illustrate some optimization challenges in the structured low rank approximation (S...
International audienceStructured Low-Rank Approximation is a problem arising in a wide range of appl...
Structured Low-Rank Approximation is a problem arising in a wide range of applications in Numerical ...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
In this paper, we consider the so-called structured low rank approximation (SLRA) problem as a probl...
Abstract. We consider the problem of approximating an affinely structured matrix, for example, a Han...
A number of problems in system theory, signal processing, and computer algebra fit into a generic st...
The low-rank approximation problem is to approximate optimally, with respect to some norm, a matrix ...
AbstractThis paper concerns the construction of a structured low rank matrix that is nearest to a gi...
Abstract—The low-rank approximation problem is to approx-imate optimally, with respect to some norm,...
Rank deficiency of a data matrix is equivalent to the existence of an exact linear model for the dat...
Matrix low-rank approximation is intimately related to data modelling; a problem that arises frequen...
Hankel matrices are closely related to linear time-invariant (LTI) models, which are widely used in ...
In this paper we illustrate some optimization challenges in the structured low rank approximation (S...