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https://github.com/google-deepmind/funsearch


https://github.com/google-deepmind/funsearch

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README 

        
# FunSearch



This repository accompanies the publication



> Romera-Paredes, B. et al. [Mathematical discoveries from program search with large language models](https://www.nature.com/articles/s41586-023-06924-6). *Nature* (2023)



There are 6 independent directories:



- `cap_set` contains functions discovered by FunSearch that construct large cap

sets, and we also provide those cap sets in a numerical format for convenience.



- `admissible_set` contains functions discovered by FunSearch that construct

large admissible sets, and we also provide those admissible sets in a numerical

format for convenience.



- `bin_packing` contains heuristics discovered by FunSearch for online 1D bin

packing problems, and an evaluation suite to reproduce the results reported in

the paper.



- `cyclic_graphs` contains functions discovered by FunSearch that construct

large independent sets in strong products of cyclic graphs, and we also provide

those sets in a numerical format for convenience.



- `corner_free_sets` contains the discovered sets of indices, in numerical

format, satisfying the combinatorial degeneration constraints described for the

corners-free problem in the Supplementary Information.



- `implementation` contains an implementation of the evolutionary algorithm,

code manipulation routines, and a single-threaded implementation of the

FunSearch pipeline. It does not contain language models for generating new

programs, the sandbox for executing untrusted code, nor the infrastructure for

running FunSearch on our distributed system. This directory is intended to be

useful for understanding the details of our method, and for adapting it for use

with any available language models, sandboxes, and distributed systems.



## Installation



No installation is required. All notebooks can be opened and run in Google

Colab.



## Usage



- `cap_set`: The notebook `cap_set.ipynb` can be opened via

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/funsearch/blob/master/cap_set/cap_set.ipynb).



- `admissible_set`: The notebook `admissible_set.ipynb` can be opened

via

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/funsearch/blob/master/admissible_set/admissible_set.ipynb).



- `bin_packing`: The notebook `bin_packing.ipynb` can be opened via

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/funsearch/blob/master/bin_packing/bin_packing.ipynb).



- `cyclic_graphs`: The notebook `cyclic_graphs.ipynb` can be opened via

[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/google-deepmind/funsearch/blob/master/cyclic_graphs/cyclic_graphs.ipynb).



## Citing this work



If you use the code or data in this package, please cite:



```bibtex

@Article{FunSearch2023,

  author  = {Romera-Paredes, Bernardino and Barekatain, Mohammadamin and Novikov, Alexander and Balog, Matej and Kumar, M. Pawan and Dupont, Emilien and Ruiz, Francisco J. R. and Ellenberg, Jordan and Wang, Pengming and Fawzi, Omar and Kohli, Pushmeet and Fawzi, Alhussein},

  journal = {Nature},

  title   = {Mathematical discoveries from program search with large language models},

  year    = {2023},

  doi     = {10.1038/s41586-023-06924-6}

}

```



## License and disclaimer



Copyright 2023 DeepMind Technologies Limited



All software is licensed under the Apache License, Version 2.0 (Apache 2.0);

you may not use this file except in compliance with the Apache 2.0 license.

You may obtain a copy of the Apache 2.0 license at:

https://www.apache.org/licenses/LICENSE-2.0



All other materials are licensed under the Creative Commons Attribution 4.0

International License (CC-BY). You may obtain a copy of the CC-BY license at:

https://creativecommons.org/licenses/by/4.0/legalcode



Unless required by applicable law or agreed to in writing, all software and

materials distributed here under the Apache 2.0 or CC-BY licenses are

distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND,

either express or implied. See the licenses for the specific language governing

permissions and limitations under those licenses.



This is not an official Google product.