https://github.com/jcmgray/cotengra

Hyper optimized contraction trees for large tensor networks and einsums
https://github.com/jcmgray/cotengra

contraction-order einsum opt-einsum quimb tensor tensor-contraction tensor-network tensor-networks

Last synced: 10 months ago
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Hyper optimized contraction trees for large tensor networks and einsums

• Host: GitHub
• URL: https://github.com/jcmgray/cotengra
• Owner: jcmgray
• License: apache-2.0
• Created: 2019-01-22T16:22:26.000Z (about 7 years ago)
• Default Branch: main
• Last Pushed: 2025-05-14T00:01:43.000Z (10 months ago)
• Last Synced: 2025-05-14T01:57:25.348Z (10 months ago)
• Topics: contraction-order, einsum, opt-einsum, quimb, tensor, tensor-contraction, tensor-network, tensor-networks
• Language: Python
• Homepage: https://cotengra.readthedocs.io
• Size: 11.3 MB
• Stars: 204
• Watchers: 5
• Forks: 33
• Open Issues: 10
• Metadata Files:
  • Readme: README.md
  • License: LICENSE.md

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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`` and ``ncon`` 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

* **simulated annealing** as an alternative optimizing and slicing strategy

* 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 [`autoray`](https://github.com/jcmgray/autoray),

  even if they don't provide `einsum` or `tensordot` but do have (batch) matrix

  multiplication