A Framework for Batched and GPU-Resident Factorization Algorithms Applied to Block Householder Transformations

2015 ◽  
pp. 31-47 ◽  
Author(s):  
Azzam Haidar ◽  
Tingxing Tim Dong ◽  
Stanimire Tomov ◽  
Piotr Luszczek ◽  
Jack Dongarra
Keyword(s):  
Factorization Algorithms ◽  
Householder Transformations

Factorization Algorithms for Temporal Psychovisual Modulation Display

2016 ◽  
Vol 18 (4) ◽  
pp. 614-626 ◽  
Author(s):  
Zhongpai Gao ◽  
Guangtao Zhai ◽  
Jiantao Zhou
Keyword(s):  
Factorization Algorithms

Faster Lyndon Factorization Algorithms for SLP and LZ78 Compressed Text

2013 ◽  
pp. 174-185 ◽  
Author(s):  
Tomohiro I ◽  
Yuto Nakashima ◽  
Shunsuke Inenaga ◽  
Hideo Bannai ◽  
Masayuki Takeda
Keyword(s):  
Factorization Algorithms

scholarly journals Introduction to Matrix Factorization for Recommender Systems

2021 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Singular Value Decomposition ◽  
Online Education ◽  
Recommender Systems ◽  
Matrix Factorization ◽  
Gradient Descent ◽  
Large Scale ◽  
Singular Value ◽  
Factorization Algorithms ◽  
Value Decomposition ◽  
Interaction History

Recommender systems aim to personalize the experience of user by suggesting items to the user based on the preferences of a user. The preferences are learned from the user’s interaction history or through explicit ratings that the user has given to the items. The system could be part of a retail website, an online bookstore, a movie rental service or an online education portal and so on. In this paper, I will focus on matrix factorization algorithms as applied to recommender systems and discuss the singular value decomposition, gradient descent-based matrix factorization and parallelizing matrix factorization for large scale applications.

DATA REPLICATION IN DENSE MATRIX FACTORIZATION

1993 ◽  
Vol 03 (04) ◽  
pp. 419-430 ◽  
Author(s):  
J. MALARD ◽  
C.C. PAIGE
Keyword(s):  
Matrix Factorization ◽  
Message Passing ◽  
Data Replication ◽  
Lu Factorization ◽  
Dense Matrix ◽  
Software Libraries ◽  
Numerical Software ◽  
Factorization Algorithms ◽  
Performance Gains

Gossiping is proposed as the preferred communication primitive for replicating pivot data in dense matrix factorization on message passing multicomputer. Performance gains are demonstrated on a hypercube for LU factorization algorithms based on gossiping as opposed to broadcasting. This finding has consequences for the design of numerical software libraries.

scholarly journals Performance Analysis and Optimisation of Two-sided Factorization Algorithms for Heterogeneous Platform

2015 ◽  
Vol 51 ◽  
pp. 180-190 ◽  
Author(s):  
Khairul Kabir ◽  
Azzam Haidar ◽  
Stanimire Tomov ◽  
Jack Dongarra
Keyword(s):  
Performance Analysis ◽  
Heterogeneous Platform ◽  
Factorization Algorithms
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