The computation of matrix functions using quadrature formulas and rational approximations of very large structured matrices using tensor trains (TT), and quantized tensor trains (QTT) is considered here. The focus is on matrices with a small TT/QTT rank. Some analysis of the error produced by the use of the TT/QTT representation and the underlying approximation formula used is also provided. Promising experiments on exponential, power, Mittag-Leffler and logarithm function of multilevel Toeplitz matrices, that are among those which generate a low TT/QTT rank representation, are also provided, confirming that the proposed approach is feasible
AbstractThis paper considers formulas and fast algorithms for the inversion and factorization of non...
This paper deals with the best low multilinear rank approximation of higher-order tensors. Given a t...
special session "Tensor Computations in Linear and Multilinear Algebra"Tensor decompositions permit ...
The computation of matrix functions using quadrature formulas and rational approximations of very la...
AbstractA general proposal is presented for fast algorithms for multilevel structured matrices. It i...
We consider a class of multilevel matrices, which arise from the discretization of linear diffusion ...
We propose new algorithms for singular value decomposition (SVD) of very large-scale ma-trices based...
A general proposal is presented for fast algorithms for multilevel structured ma-trices. It is based...
Abstract A fast approximate inversion algorithm is proposed for two-level Toeplitz matrices (block T...
This paper suggests an elementary introduction to recent developments of low-rank matrix approxi-mat...
AbstractQuantics tensor train (QTT), a new data-sparse format for one- and multi-dimensional vectors...
We present a new mixed precision algorithm to compute low-rank matrix and tensor approximations, a f...
In the present paper, we give a survey of the recent results and outline future prospects of the ten...
The computation of the matrix exponential is a ubiquitous operation in numerical mathematics, and fo...
We propose a new method for the efficient approximation of a class of highly os-cillatory weighted i...
AbstractThis paper considers formulas and fast algorithms for the inversion and factorization of non...
This paper deals with the best low multilinear rank approximation of higher-order tensors. Given a t...
special session "Tensor Computations in Linear and Multilinear Algebra"Tensor decompositions permit ...
The computation of matrix functions using quadrature formulas and rational approximations of very la...
AbstractA general proposal is presented for fast algorithms for multilevel structured matrices. It i...
We consider a class of multilevel matrices, which arise from the discretization of linear diffusion ...
We propose new algorithms for singular value decomposition (SVD) of very large-scale ma-trices based...
A general proposal is presented for fast algorithms for multilevel structured ma-trices. It is based...
Abstract A fast approximate inversion algorithm is proposed for two-level Toeplitz matrices (block T...
This paper suggests an elementary introduction to recent developments of low-rank matrix approxi-mat...
AbstractQuantics tensor train (QTT), a new data-sparse format for one- and multi-dimensional vectors...
We present a new mixed precision algorithm to compute low-rank matrix and tensor approximations, a f...
In the present paper, we give a survey of the recent results and outline future prospects of the ten...
The computation of the matrix exponential is a ubiquitous operation in numerical mathematics, and fo...
We propose a new method for the efficient approximation of a class of highly os-cillatory weighted i...
AbstractThis paper considers formulas and fast algorithms for the inversion and factorization of non...
This paper deals with the best low multilinear rank approximation of higher-order tensors. Given a t...
special session "Tensor Computations in Linear and Multilinear Algebra"Tensor decompositions permit ...