Robert beat me to it on einsum, but also check tensordot for general tensor
contraction.
On Fri, Jan 15, 2016 at 9:30 AM, Nathaniel Smith <n...@pobox.com> wrote:
> On Jan 15, 2016 8:36 AM, "Li Jiajia" <jiaji...@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.
> NumPy has support for working with multidimensional tensors, if you like,
> but it doesn't really use the tensor language and notation (preferring
> instead to think in terms of "arrays" as a somewhat more computationally
> focused and less mathematically focused conceptual framework).
> Which is to say that I actually have no idea what all those jargon terms
> you're asking about mean :-) I am suspicious that NumPy supports more of
> those operations than you have marked, just under different names/notation,
> but really can't tell either way for sure without knowing what exactly they
> are.
> (It is definitely correct though that NumPy includes no support for sparse
> tensors, and NumPy itself is not multi-threaded beyond what we get for free
> through the BLAS, though there are external libraries that can perform
> multi-threaded computations on top of data stored in numpy arrays.)
> -n
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