In this paper, a successive supersymmetric rank-1 decomposition of a real higher-order supersymmetric tensor is considered. To obtain such a decomposition, we design a greedy method based on iteratively computing the best supersymmetric rank-1 approximation of the residual tensors. We further show that a supersymmetric canonical decomposition could be obtained when the method is applied to an orthogonally diagonalizable supersymmetric tensor, and in particular, when the order is 2, this method generates the eigenvalue decomposition for symmetric matrices. Details of the algorithm designed and the numerical results are reported in this paper.Department of Applied Mathematic
AbstractWe present an algorithm for decomposing a symmetric tensor, of dimension n and order d, as a...
The Singular Value Decomposition (SVD) may be extended to tensors at least in two very different way...
This is the first in a series of papers on rank decompositions of the matrix multiplication tensor. ...
In this paper, we propose three new tensor decompositions for even-order tensors correspond-ing resp...
Abstract. We present an iterative algorithm, called the symmetric tensor eigen-rank-one itera-tive d...
This paper deals with the best low multilinear rank approximation of higher-order tensors. Given a t...
In this paper, we examine structured tensors which have sum-of-squares (SOS) tensor decomposition, a...
In this paper, we examine structured tensors which have sum-of-squares (SOS) tensor decomposition, a...
International audienceWe propose a non iterative algorithm, called SeROAP (Sequential Rank-One Appro...
This is the first in a series of papers on rank decompositions of the matrix multiplication tensor. ...
In this thesis we will explore the extensions of several ideas that have proven very successful in m...
more details in : hal-00490248The Canonical Polyadic (CP) decomposition of a tensor is difficult to ...
Tensor rank decomposition is a useful tool for geometric interpretation of the tensors in the canoni...
This paper is concerned with low multilinear rank approximations to antisymmetric tensors, that is, ...
AbstractIn this paper we review a multilinear generalization of the singular value decomposition and...
AbstractWe present an algorithm for decomposing a symmetric tensor, of dimension n and order d, as a...
The Singular Value Decomposition (SVD) may be extended to tensors at least in two very different way...
This is the first in a series of papers on rank decompositions of the matrix multiplication tensor. ...
In this paper, we propose three new tensor decompositions for even-order tensors correspond-ing resp...
Abstract. We present an iterative algorithm, called the symmetric tensor eigen-rank-one itera-tive d...
This paper deals with the best low multilinear rank approximation of higher-order tensors. Given a t...
In this paper, we examine structured tensors which have sum-of-squares (SOS) tensor decomposition, a...
In this paper, we examine structured tensors which have sum-of-squares (SOS) tensor decomposition, a...
International audienceWe propose a non iterative algorithm, called SeROAP (Sequential Rank-One Appro...
This is the first in a series of papers on rank decompositions of the matrix multiplication tensor. ...
In this thesis we will explore the extensions of several ideas that have proven very successful in m...
more details in : hal-00490248The Canonical Polyadic (CP) decomposition of a tensor is difficult to ...
Tensor rank decomposition is a useful tool for geometric interpretation of the tensors in the canoni...
This paper is concerned with low multilinear rank approximations to antisymmetric tensors, that is, ...
AbstractIn this paper we review a multilinear generalization of the singular value decomposition and...
AbstractWe present an algorithm for decomposing a symmetric tensor, of dimension n and order d, as a...
The Singular Value Decomposition (SVD) may be extended to tensors at least in two very different way...
This is the first in a series of papers on rank decompositions of the matrix multiplication tensor. ...