matmult

%*%,dgRMatrix,matrix-method

%*%,dgRMatrix,float32-method

%*%,float32,dgRMatrix-method

tcrossprod,dgRMatrix,matrix-method

tcrossprod,dgRMatrix,float32-method

%*%,matrix,dgCMatrix-method

%*%,float32,dgCMatrix-method

%*%,dgCMatrix,float32-method

crossprod,matrix,dgCMatrix-method

crossprod,float32,dgCMatrix-method

<p>dense <code>matrix</code> and sparse
<code>Matrix::RsparseMatrix</code> / <code>Matrix::CsparseMatrix</code> matrices.</p>

x, y

<p>Multithreaded <code>%*%</code>, <code>crossprod</code>, <code>tcrossprod</code>
for sparse-dense matrix multiplication</p>

Implements many algorithms for statistical learning on
sparse matrices - matrix factorizations, matrix completion,
elastic net regressions, factorization machines.
Also 'rsparse' enhances 'Matrix' package by providing methods for
multithreaded <sparse, dense> matrix products and native slicing of
the sparse matrices in Compressed Sparse Row (CSR) format.
List of the algorithms for regression problems:
1) Elastic Net regression via Follow The Proximally-Regularized Leader (FTRL)
Stochastic Gradient Descent (SGD), as per McMahan et al(, <doi:10.1145/2487575.2488200>)
2) Factorization Machines via SGD, as per Rendle (2010, <doi:10.1109/ICDM.2010.127>)
List of algorithms for matrix factorization and matrix completion:
1) Weighted Regularized Matrix Factorization (WRMF) via Alternating Least
Squares (ALS) - paper by Hu, Koren, Volinsky (2008, <doi:10.1109/ICDM.2008.22>)
2) Maximum-Margin Matrix Factorization via ALS, paper by Rennie, Srebro
(2005, <doi:10.1145/1102351.1102441>)
3) Fast Truncated Singular Value Decomposition (SVD), Soft-Thresholded SVD,
Soft-Impute matrix completion via ALS - paper by Hastie, Mazumder
et al. (2014, <arXiv:1410.2596>)
4) Linear-Flow matrix factorization, from 'Practical linear models for
large-scale one-class collaborative filtering' by Sedhain, Bui, Kawale et al
(2016, ISBN:978-1-57735-770-4)
5) GlobalVectors (GloVe) matrix factorization via SGD, paper by Pennington,
Socher, Manning (2014, <https://www.aclweb.org/anthology/D14-1162>)
Package is reasonably fast and memory efficient - it allows to work with large
datasets - millions of rows and millions of columns. This is particularly useful
for practitioners working on recommender systems.

matmult: Multithreaded Sparse-Dense Matrix Multiplication

Description

Usage

Arguments

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Details

Examples