Randomized algorithms for the approximations of Tucker and the tensor train decompositions
Sign Up to like & getrecommendations! Published in 2019 at "Advances in Computational Mathematics"
DOI: 10.1007/s10444-018-9622-8
Abstract: Randomized algorithms provide a powerful tool for scientific computing. Compared with standard deterministic algorithms, randomized algorithms are often faster and robust. The main purpose of this paper is to design adaptive randomized algorithms for computing… read more here.
Keywords: tensor train; adaptive randomized; tensor; algorithms approximations ... See more keywords
Randomized Algorithms for Orthogonal Nonnegative Matrix Factorization
Sign Up to like & getrecommendations! Published in 2020 at "Journal of the Operations Research Society of China"
DOI: 10.1007/s40305-020-00322-9
Abstract: Orthogonal nonnegative matrix factorization (ONMF) is widely used in blind image separation problem, document classification, and human face recognition. The model of ONMF can be efficiently solved by the alternating direction method of multipliers and… read more here.
Keywords: orthogonal nonnegative; nonnegative matrix; matrix factorization; randomized algorithms ... See more keywords
Randomized Algorithms for Non-Intrusive Parametric Reduced Order Modeling
Sign Up to like & getrecommendations! Published in 2020 at "AIAA Journal"
DOI: 10.2514/1.j059616
Abstract: This paper demonstrates the development of purely data-driven, nonintrusive parametric reduced-order models for the emulation of high-dimensional field outputs using randomized linear algebra techn... read more here.
Keywords: parametric reduced; reduced order; randomized algorithms; algorithms non ... See more keywords