Golem: An Algorithm for Robust Experiment and Process Optimization
Project description
Golem: An algorithm for robust experiment and process optimization
Golem
is an algorithm for robust optimization. It can be used in conjunction with any optimization algorithms or
design of experiment strategy of choice. Golem
helps identifying optimal solutions that are robust to input uncertainty,
thus ensuring the reproducible performance of optimized experimental protocols and processes. It can be used to analyze
the robustness of past experiments, or to guide experiment planning algorithms toward robust solutions on the fly. For
more details on the algorithm and its behaviour please refer to the publication and
the documentation.
Installation
Golem
can be installed with pip
:
pip install matter-golem
Dependencies
The installation requires:
python >= 3.7
numpy
scipy >= 1.4
pandas
scikit-learn
Citation
Golem
is research software. If you make use of it in scientific publications, please cite the following article:
@misc{golem,
title={Golem: An algorithm for robust experiment and process optimization},
author={Matteo Aldeghi and Florian Häse and Riley J. Hickman and Isaac Tamblyn and Alán Aspuru-Guzik},
year={2021},
eprint={2103.03716},
archivePrefix={arXiv},
primaryClass={math.OC}
}
License
Golem
is distributed under an MIT License.
Project details
Release history Release notifications | RSS feed
Download files
Download the file for your platform. If you're not sure which to choose, learn more about installing packages.
Source Distribution
Built Distribution
Hashes for matter_golem-1.0-cp37-cp37m-macosx_10_9_x86_64.whl
Algorithm | Hash digest | |
---|---|---|
SHA256 | ebf8f644dbcd724917336816fb7b0eb1ae2cea9f7679b53b3fd1691ac98890cd |
|
MD5 | 8329d675f57a2e078567191c80f1a12a |
|
BLAKE2b-256 | c25dea1ec4f9b6886f660e8884f737a8233f577fa9966f3974a171c2ff82f9c3 |