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WEDDERBURN RANK REDUCTION AND KRYLOV SUBSPACE METHOD FOR TENSOR APPROXIMATION. PART 1: TUCKER CASE.
- Source :
-
SIAM Journal on Scientific Computing . 2012, Vol. 34 Issue 1, pA2-A27. 27p. - Publication Year :
- 2012
-
Abstract
- New algorithms are proposed for the Tucker approximation of a 3-tensor accessed only through a tensor-by-vector-by-vector multiplication subroutine. In the matrix case, the Krylov methods are methods of choice to approximate the dominant column and row subspaces of a sparse or structured matrix given through a matrix-by-vector operation. Using the Wedderburn rank reduction formula, we propose a matrix approximation algorithm that computes the Krylov subspaces and can be generalized to 3-tensors. The numerical experiments show that on quantum chemistry data the proposed tensor methods outperform the minimal Krylov recursion of Savas and Eldén. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 10648275
- Volume :
- 34
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- SIAM Journal on Scientific Computing
- Publication Type :
- Academic Journal
- Accession number :
- 76320773
- Full Text :
- https://doi.org/10.1137/100792056