Your first citation is incorrect. It is "VAN DER WALT" (missing V in yours) Bryan
On Jan 15, 2016, at 10:36 AM, Li Jiajia <jiajiali@gatech.edu> wrote:
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 2016.
Best regards! Jiajia Li
------------------------------------------ E-mail: jiajiali@gatech.edu Tel: +1 (404)9404603 Computational Science & Engineering Georgia Institute of Technology
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