tensorr: tensorr: sparse tensors in R

In tensorr: Sparse Tensors in R

Description

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

Details

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.

References

Many of the dense and sparse implementation ideas were adapted 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.

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.