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https://github.com/awslabs/tensor-message-passing

https://github.com/awslabs/tensor-message-passing

Science Score: 10.0%

This score indicates how likely this project is to be science-related based on various indicators:

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    CITATION.cff file
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    codemeta.json file
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    .zenodo.json file
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    DOI references
  • ✓
    Academic publication links
    Links to: arxiv.org
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    Scientific vocabulary similarity
    Low similarity (8.3%) to scientific vocabulary
Last synced: 10 months ago · JSON representation

Repository

Basic Info
  • Host: GitHub
  • Owner: awslabs
  • License: mit-0
  • Language: Jupyter Notebook
  • Default Branch: main
  • Size: 684 KB
Statistics
  • Stars: 4
  • Watchers: 3
  • Forks: 2
  • Open Issues: 3
  • Releases: 0
Created about 3 years ago · Last pushed about 2 years ago
Metadata Files
Readme Contributing License Code of conduct

README.md

Tensor Network Message Passing Algorithms

This repository contains the code used to generate the results in One-step replica symmetry breaking in the language of tensor networks

Install

Just clone this repo, optionally create an environment (conda create -n tnmpa python=3.11), and run poetry install from the project's directory tensor-message-passing

Examples

In tnmpa/examples you can find a few jupyter notebooks that demonstrate how to use the code. The files ksat_* compute solutions of kSAT instances. The files compare_* compute some high-level comparisons between massage passing, exact and tensor network contractions. Finally, quimb_bp.ipynb showcases the power of vectorized belief propagation for large tensor networks.

Security

See CONTRIBUTING for more information.

License

This library is licensed under the MIT-0 License. See the LICENSE file.

Owner

  • Name: Amazon Web Services - Labs
  • Login: awslabs
  • Kind: organization
  • Location: Seattle, WA

AWS Labs

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Last synced: about 2 years ago

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  • Total pull requests: 9
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  • Total pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.11
  • Merged pull requests: 5
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  • Bot pull requests: 6
Past Year
  • Issues: 0
  • Pull requests: 9
  • Average time to close issues: N/A
  • Average time to close pull requests: 23 days
  • Issue authors: 0
  • Pull request authors: 2
  • Average comments per issue: 0
  • Average comments per pull request: 0.11
  • Merged pull requests: 5
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  • Bot pull requests: 6
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dependencies (10)

Dependencies

poetry.lock pypi
  • appnope 0.1.3
  • asttokens 2.2.1
  • autoray 0.6.3
  • backcall 0.2.0
  • black 23.3.0
  • cffi 1.15.1
  • cfgv 3.3.1
  • click 8.1.3
  • colorama 0.4.6
  • comm 0.1.3
  • contourpy 1.1.0
  • cycler 0.11.0
  • cytoolz 0.12.1
  • debugpy 1.6.7
  • decorator 5.1.1
  • distlib 0.3.6
  • executing 1.2.0
  • filelock 3.12.2
  • fonttools 4.40.0
  • identify 2.5.24
  • importlib 1.0.4
  • iniconfig 2.0.0
  • ipykernel 6.23.2
  • ipython 8.14.0
  • isort 5.12.0
  • jedi 0.18.2
  • jupyter-client 8.2.0
  • jupyter-core 5.3.1
  • kiwisolver 1.4.4
  • llvmlite 0.40.0
  • matplotlib 3.7.1
  • matplotlib-inline 0.1.6
  • mypy-extensions 1.0.0
  • nest-asyncio 1.5.6
  • networkx 3.1
  • nodeenv 1.8.0
  • numba 0.57.0
  • numpy 1.24.3
  • opt-einsum 3.3.0
  • packaging 23.1
  • parso 0.8.3
  • pathspec 0.11.1
  • pexpect 4.8.0
  • pickleshare 0.7.5
  • pillow 10.0.1
  • platformdirs 3.6.0
  • pluggy 1.0.0
  • pre-commit 3.3.3
  • prompt-toolkit 3.0.38
  • psutil 5.9.5
  • ptyprocess 0.7.0
  • pure-eval 0.2.2
  • pycparser 2.21
  • pygments 2.15.1
  • pyparsing 3.1.0
  • pytest 7.3.2
  • python-dateutil 2.8.2
  • pywin32 306
  • pyyaml 6.0
  • pyzmq 25.1.0
  • quimb 1.5.0
  • scipy 1.9.3
  • setuptools 67.8.0
  • six 1.16.0
  • stack-data 0.6.2
  • toolz 0.12.0
  • tornado 6.3.3
  • tqdm 4.65.0
  • traitlets 5.9.0
  • virtualenv 20.23.1
  • wcwidth 0.2.6
pyproject.toml pypi
  • black ^23.3.0
  • importlib ^1.0.4
  • ipykernel ^6.23.2
  • ipython ^8.14.0
  • isort ^5.12.0
  • llvmlite ^0.40.0
  • matplotlib ^3.7.1
  • networkx ^3.1
  • numba ^0.57.0
  • pre-commit ^3.3.3
  • pytest ^7.3.2
  • python ^3.11
  • quimb ^1.5.0