[Numpy-discussion] Software Capabilities of NumPy in Our Tensor Survey Paper

[Bryan Van de Ven]

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 at 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 at gatech.edu
> Tel: +1 (404)9404603
> Computational Science & Engineering
> Georgia Institute of Technology
> 
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