High Performance Rearrangement and Multiplication Routines for Sparse Tensor Arithmetic
DOI10.1137/17M1115873zbMath1391.65120arXiv1802.02619MaRDI QIDQ4610135
No author found.
Publication date: 5 April 2018
Published in: SIAM Journal on Scientific Computing (Search for Journal in Brave)
Full work available at URL: https://arxiv.org/abs/1802.02619
sparse tensor productsMATLAB classessparse multidimensional arraysC++ classessparse sorting and permutation
Computational methods for sparse matrices (65F50) Searching and sorting (68P10) Data structures (68P05) Complexity and performance of numerical algorithms (65Y20) Multilinear algebra, tensor calculus (15A69) Numerical algorithms for computer arithmetic, etc. (65Y04)
Uses Software
Cites Work
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Unnamed Item
- Tensor Decompositions and Applications
- Einstein summation for multidimensional arrays.
- Sparsity in higher order methods for unconstrained optimization
- Exploiting Symmetry in Tensors for High Performance: Multiplication with Symmetric Tensors
- Breaking the Curse of Dimensionality, Or How to Use SVD in Many Dimensions
- Parallel Sparse Matrix-Matrix Multiplication and Indexing: Implementation and Experiments
- Direct Methods for Sparse Linear Systems
- Algorithm 862
- Efficient MATLAB Computations with Sparse and Factored Tensors
- The Influence of Caches on the Performance of Sorting
- Efficient representation scheme for multidimensional array operations
- Numerical Polynomial Algebra
- Solution of Linear Systems and Matrix Inversion in the TT-Format
- Symmetric Tensors and Symmetric Tensor Rank
- Algorithms for Numerical Analysis in High Dimensions
- Parallel sparse supports for array intrinsic functions of Fortran 90
This page was built for publication: High Performance Rearrangement and Multiplication Routines for Sparse Tensor Arithmetic