PLANC: Parallel Low Rank Approximation with Non-negativity Constraints
Published in ACM Transactions on Mathematical Software, 2021
Recommended citation: Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Michael A. Matheson, and Haesun Park. 2021. PLANC: Parallel Low-rank Approximation with Nonnegativity Constraints. ACM Trans. Math. Softw. 47, 3, Article 20 (September 2021), 37 pages. https://doi.org/10.1145/3432185 http://hayakb95.github.io/files/TOMS_PLANC.pdf
PLANC is a software package for computing low-rank tensor and matrix approximations with nonnegativity constraints. The package supports shared and distributed memory algorithms. Sparse and dense NMF are supported along with Dense CP of general tensors.
Recommended citation: ‘Srinivas Eswar, Koby Hayashi, Grey Ballard, Ramakrishnan Kannan, Michael A. Matheson, and Haesun Park. 2021. PLANC: Parallel Low-rank Approximation with Nonnegativity Constraints. ACM Trans. Math. Softw. 47, 3, Article 20 (September 2021), 37 pages. https://doi.org/10.1145/3432185’ ACM Transactions on Mathematical Software.