Add to ORKGFor the antisymmetric tensors the paper examines a low-rank approximation which is represented via only three vectors. We describe a suitable low-rank format and propose an alternating least squares structure-preserving algorithm for finding such approximation. The case of partial antisymmetry is also discussed. The algorithms are implemented in Julia programming language and their numerical performance is discussed.Comment: 16 pages, 4 table
The singular value decomposition is among the most important algebraic tools for solving many approx...
We propose a nonnegative tensor decomposition with focusing on the relationship between the modes of...
Abstract. With the notable exceptions of two cases — that tensors of order 2, namely, matrices, alwa...
This paper is concerned with low multilinear rank approximations to antisymmetric tensors, that is, ...
Abstract — We present a new connection between higher-order tensors and affinely structured matrices...
One of the main issues in computing a tensor decomposition is how to choose the number of rank-one c...
Submitted to SIAM Journal on Scientific ComputingComputing low-rank approximations is one of the mos...
Rapport interne de GIPSA-labBecause of the attractiveness of the canonical polyadic (CP) tensor deco...
Computing low-rank approximations is one of the most important and well-studied problems involving t...
Computing low-rank approximations is one of the most important and well-studied problems involving t...
Les tenseurs sont une généralisation d'ordre supérieur des matrices. Ils apparaissent dans une myria...
In this paper, we propose three new tensor decompositions for even-order tensors correspond-ing resp...
In this paper, we propose three new tensor decompositions for even-order tensors correspond-ing resp...
more details in : hal-00490248The Canonical Polyadic (CP) decomposition of a tensor is difficult to ...
Tensors are higher order generalization of matrices. They appear in a myriad of applications. The te...
The singular value decomposition is among the most important algebraic tools for solving many approx...
We propose a nonnegative tensor decomposition with focusing on the relationship between the modes of...
Abstract. With the notable exceptions of two cases — that tensors of order 2, namely, matrices, alwa...
This paper is concerned with low multilinear rank approximations to antisymmetric tensors, that is, ...
Abstract — We present a new connection between higher-order tensors and affinely structured matrices...
One of the main issues in computing a tensor decomposition is how to choose the number of rank-one c...
Submitted to SIAM Journal on Scientific ComputingComputing low-rank approximations is one of the mos...
Rapport interne de GIPSA-labBecause of the attractiveness of the canonical polyadic (CP) tensor deco...
Computing low-rank approximations is one of the most important and well-studied problems involving t...
Computing low-rank approximations is one of the most important and well-studied problems involving t...
Les tenseurs sont une généralisation d'ordre supérieur des matrices. Ils apparaissent dans une myria...
In this paper, we propose three new tensor decompositions for even-order tensors correspond-ing resp...
In this paper, we propose three new tensor decompositions for even-order tensors correspond-ing resp...
more details in : hal-00490248The Canonical Polyadic (CP) decomposition of a tensor is difficult to ...
Tensors are higher order generalization of matrices. They appear in a myriad of applications. The te...
The singular value decomposition is among the most important algebraic tools for solving many approx...
We propose a nonnegative tensor decomposition with focusing on the relationship between the modes of...
Abstract. With the notable exceptions of two cases — that tensors of order 2, namely, matrices, alwa...