cotengra


Namecotengra JSON
Version 0.6.1 PyPI version JSON
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home_pagehttps://github.com/jcmgray/cotengra
SummaryHyper optimized contraction trees for large tensor networks and einsums.
upload_time2024-05-15 21:38:21
maintainerNone
docs_urlNone
authorJohnnie Gray
requires_python>=3.8
licenseApache
keywords tensor network contraction graph hypergraph partition einsum
VCS
bugtrack_url
requirements No requirements were recorded.
Travis-CI No Travis.
coveralls test coverage No coveralls.
            <p align="left"><img src="https://imgur.com/OM5XyaD.png" alt="cotengra" width="400px"></p>

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`cotengra` is a python library for contracting tensor networks or einsum
expressions involving large numbers of tensors - the main docs can be found
at [cotengra.readthedocs.io](https://cotengra.readthedocs.io/).
Some of the key feautures of `cotengra` include:

* drop-in ``einsum`` replacement
* an explicit **contraction tree** object that can be flexibly built, modified and visualized
* a **'hyper optimizer'** that samples trees while tuning the generating meta-paremeters
* **dynamic slicing** for massive memory savings and parallelism
* support for **hyper** edge tensor networks and thus arbitrary einsum equations
* **paths** that can be supplied to [`numpy.einsum`](https://numpy.org/doc/stable/reference/generated/numpy.einsum.html), [`opt_einsum`](https://dgasmith.github.io/opt_einsum/), [`quimb`](https://quimb.readthedocs.io/en/latest/) among others
* **performing contractions** with tensors from many libraries via [`cotengra`](https://github.com/jcmgray/autoray),
  even if they don't provide `einsum` or `tensordot` but do have (batch) matrix
  multiplication

<p align="center"><img src="https://imgur.com/jMO138y.png" alt="cotengra" width="500px"></p>

            

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