Sparse Tensors in R

Provides methods to manipulate and store sparse tensors. Tensors are multidimensional generalizations of matrices (two dimensional) and vectors (one dimensional).

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
#> 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