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This is a reasonable resource, which I have consulted before, but it's from 2016 so the title should be updated. OptEinsum is also worth checking out if you us

• ovi256 8y ago
  >it's from 2016 so the title should be updated I will try to update the title. There are no changes since then that diminish this document's value - the einsum notation is still the same. The biggest change since then is that the numpy.einsum implementation has been tremendously optimized and has become the benchmark in capabilities that other libraries follow. Edit: Opt_Einsum is amazing and was a big part of the optimization efforts - it has been merged fully into numpy since v1.12. See https://github.com/dgasmith/opt_einsum#news-opt_einsum-will-be-in-numpy-112-and-blas-features-in-numpy-114-call-opt_einsum-as-npeinsum-optimizetrue-this-repository-contains-more-advanced-features-such-as-dask-or-tensorflow-backends-as-well-as-a-testing-ground-for-newer-features-in-this-ecosystem https://github.com/dgasmith/opt_einsum#news-opt_einsum-will-...
  • rax 8y ago
    I agree about the value of the article. I guess I got tilted as I was hoping for another good, more fresh, post of similar quality! Using the Optimize kewarg is a must for any summation involving a few inputs, which is why I brought it up. It is one of the things which is not discussed in a post from 2016.
  • dgasmith 8y ago
    Note that `opt_einsum` is no longer fully merged with NumPy (and likely will not be) as we now support a wider range of inputs, backends (TensorFlow, Dask, etc), and more powerful optimization algorithms for expressions with hundreds of tensors. Disclaimer: I am the author of the `opt_einsum` package.