README.md

In tensorr: Sparse Tensors in R

tensorr: sparse tensors in R

tensorr provides methods to manipulate and store sparse tensors. Tensors are multi-dimensional generalizations of matrices (two dimensional) and vectors (one dimensional).

It has three main goals:

• Provide an efficient format to store sparse tensors in R.
• Provide standard tensor operations such as multiplication and unfolding.
• Provide standard tensor decomposition techniques such as CP and Tucker.

Installation

The development version of tensorr is available on github.

devtools::install_github("zamorarr/tensorr")

Usage

See the introduction vignette for a comprehensive overview. To create a sparse tensor you have to provide the non-zero values, subscripts to the non-zero values, and the overall dimensions of the tensor.

library(tensorr)

subs <- list(c(1,1,1), c(1,1,2))
vals <- c(10, 20)
dims <- c(2,2,2)
x <- sptensor(subs, vals, dims)
x
#> <A 2x2x2 sparse tensor with 2 non-zero entries>
#> subs: <1,1,1> <1,1,2>
#> vals: 10 20

Tensor References

Many of the dense and sparse implementation ideas were adpated from:

• B. W. Bader and T. G. Kolda. Algorithm 862: MATLAB tensor classes for fast algorithm prototyping, ACM Transactions on Mathematical Software 32(4):635-653, December 2006.
• B. W. Bader and T. G. Kolda. Efficient MATLAB computations with sparse and factored tensors, SIAM Journal on Scientific Computing 30(1):205-231, December 2007.
• scikit-tensor

For a review on tensors, see:

• T. G. Kolda and B. W. Bader, Tensor Decompositions and Applications, SIAM Review 51(3):455-500, September 2009

tensorr documentation built on May 2, 2019, 3:26 a.m.