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epbo

Implementation code for the paper "Bayesian Optimization via Exact Penalty"

https://github.com/jiangyan-zhao/epbo

Science Score: 57.0%

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

  • ✓
    CITATION.cff file
    Found CITATION.cff file
  • ✓
    codemeta.json file
    Found codemeta.json file
  • ✓
    .zenodo.json file
    Found .zenodo.json file
  • ✓
    DOI references
    Found 3 DOI reference(s) in README
  • ○
    Academic publication links
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    Academic email domains
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    Institutional organization owner
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    JOSS paper metadata
  • ○
    Scientific vocabulary similarity
    Low similarity (6.1%) to scientific vocabulary

Keywords

bayesian-optimization constrained-bayesian-optimization global-optimization gradient-free-optimization
Last synced: 4 months ago · JSON representation ·

Repository

Implementation code for the paper "Bayesian Optimization via Exact Penalty"

Basic Info
  • Host: GitHub
  • Owner: Jiangyan-Zhao
  • License: agpl-3.0
  • Language: R
  • Default Branch: main
  • Homepage:
  • Size: 1.96 MB
Statistics
  • Stars: 3
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Topics
bayesian-optimization constrained-bayesian-optimization global-optimization gradient-free-optimization
Created over 2 years ago · Last pushed almost 2 years ago
Metadata Files
Readme Changelog License Citation

README.md

EPBO: Bayesian Optimization via Exact Penalty

An R package that implements a series of constrained Bayesian optimization algorithms. (under development)

Implementation of the EPBO method proposed in

Jiangyan Zhao & Jin Xu (07 Feb 2024): Bayesian Optimization via Exact Penalty, Technometrics, DOI: 10.1080/00401706.2024.2315937

If you have used our code for research purposes, please cite the publication mentioned above. For the sake of simplicity, we provide the Bibtex format:

```bibtex @Article{Zhao2024EPBO, author = {Zhao, Jiangyan and Xu, Jin}, journal = {Technometrics}, title = {Bayesian Optimization via Exact Penalty}, year = {2024}, doi = {10.1080/00401706.2024.2315937}, }

@software{ZhaoEPBOBayesianOptimization2023, author = {Zhao, Jiangyan and Xu, Jin}, month = dec, title = {{EPBO: Bayesian Optimization via Exact Penalty}}, url = {https://github.com/Jiangyan-Zhao/EPBO}, version = {0.1.0}, year = {2023} } ```

Owner

  • Name: Jiangyan Zhao
  • Login: Jiangyan-Zhao
  • Kind: user
  • Location: Shanghai, China
  • Company: East China Normal University

Ph.D. Candidate in Statistics

Citation (CITATION.cff) 

cff-version: 1.2.0
message: "If you use this software, please cite it as below."
authors:
- family-names: "Zhao"
  given-names: "Jiangyan"
- family-names: "Xu"
  given-names: "Jin"
title: "EPBO: Bayesian Optimization via Exact Penalty"
version: 0.1.0
date-released: 2023-12-27
url: "https://github.com/Jiangyan-Zhao/EPBO"
license: AGPL-3.0

GitHub Events

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Dependencies

DESCRIPTION cran
  • R >= 2.14 depends
  • DiceKriging * imports
  • laGP * imports
  • matrixStats * imports
  • mvtnorm * imports
  • tgp * imports
  • stats * suggests
  • testthat >= 3.0.0 suggests
  • utils * suggests