Hi all,
I’m a PhD student in Georgia Tech. Recently, we’re working on a survey
paper about tensor algorithms: basic tensor operations, tensor decomposition
and some tensor applications. We are making a table to compare the capabilities
of different software and planning to include NumPy. We’d like to make sure
these parameters are correct to make a fair compare. Although we have looked
into the related documents, please help us to confirm these. Besides, if you
think there are more features of your software and a more preferred citation,
please let us know. We’ll consider to update them. We want to show NumPy
supports tensors, and we also include "scikit-tensor” in our survey, which is
based on NumPy.
Please let me know any confusion or any advice!
Thanks a lot! :-)
Notice:
1. “YES/NO” to show whether or not the software supports the operation or has
the feature.
2. “?” means we’re not sure of the feature, and please help us out.
3. “Tensor order” means the maximum number of tensor dimensions that users can
do with this software.
4. For computational cores,
1) "Element-wise Tensor Operation (A * B)” includes element-wise
add/minus/multiply/divide, also Kronecker, outer and Katri-Rao products. If the
software contains one of them, we mark “YES”.
2) “TTM” means tensor-times-matrix multiplication. We distinguish TTM
from tensor contraction. If the software includes tensor contraction, it can
also support TTM.
3) For “MTTKRP”, we know most software can realize it through the above
two operations. We mark it “YES”, only if an specified optimization for the
whole operation.
<> <>Software Name <>
NumPy
Computational Cores
Element-wise Tensor Operation (A * B)
YES
Tensor Contraction (A Xmn B)
NO
TTM ( A Xn B)
NO
Matriced Tensor Times Khatri-Rao Product (MTTKRP)
NO
Tensor Decomposition
CP
NO
Tucker
NO
Hierarchical Tucker (HT)
NO
Tensor Train (TT)
NO
Tensor Features
Tensor Order
Arbitrary
Dense Tensors
YES
Sparse Tensors
NO ?
Parallelized
NO ?
Software Information
Application Domain
General
Programming Environment
Python
Latest Version
1.10.4
Release Date
2016
Citation:
1. AN DER WALT, S., COLBERT, S., AND VAROQUAUX, G. The NumPy array: A structure
for efficient numerical computation. Computing in Science Engineering 13, 2
(March 2011), 22–30.
2. OLIPHANT, T. E. Python for scientific computing. Computing in Science
Engineering 9, 3 (May 2007), 10–20.
3. NumPy (Version1.10.4).Available from http://www.numpy.org, Jan
<http://www.numpy.org, Jan> 2016.
Best regards!
Jiajia Li
------------------------------------------
E-mail: jiaji...@gatech.edu
Tel: +1 (404)9404603
Computational Science & Engineering
Georgia Institute of Technology
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