https://github.com/basf/mopti

Lightweight tools for experimental design and multi-objective optimization.
https://github.com/basf/mopti

bayesian-optimization experimental-design multiobjective-optimization optimization python

Last synced: 6 days ago
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Lightweight tools for experimental design and multi-objective optimization.

• Host: GitHub
• URL: https://github.com/basf/mopti
• Owner: basf
• Created: 2021-06-21T13:17:37.000Z (almost 5 years ago)
• Default Branch: main
• Last Pushed: 2025-07-22T14:48:10.000Z (9 months ago)
• Last Synced: 2025-12-04T00:06:45.379Z (4 months ago)
• Topics: bayesian-optimization, experimental-design, multiobjective-optimization, optimization, python
• Language: Python
• Homepage: https://basf.github.io/mopti
• Size: 3.12 MB
• Stars: 29
• Watchers: 3
• Forks: 3
• Open Issues: 9
• Metadata Files:
  • Readme: README.md
  • Contributing: CONTRIBUTING.md
  • License: LICENSE

Awesome Lists containing this project

README

          
## Opti

[![Tests](https://github.com/basf/mopti/actions/workflows/main.yml/badge.svg)](https://github.com/basf/mopti/actions)

[![Docs](https://github.com/basf/mopti/actions/workflows/docs.yml/badge.svg)](https://basf.github.io/mopti/)

[![PyPI](https://img.shields.io/pypi/v/mopti.svg?color=%2334D058)](https://pypi.org/project/mopti)

**Opti is deprecated. Consider using [BoFire](https://github.com/experimental-design/bofire) instead.**

Opti is a Python package for specifying problems in a number of closely related fields, including experimental design, multiobjective optimization, decision making and Bayesian optimization.

**Docs**: https://basf.github.io/mopti/ 


**Code**: https://github.com/basf/mopti

### Why opti? 

Opti ...

* supports mixed continuous, discrete and categorical parameter spaces for system inputs and outputs,

* separates objectives (minimize, maximize, close-to-target) from the outputs on which they operate,

* supports different specific and generic constraints as well as black-box output constraints,

* provides sampling methods for constrained mixed variable spaces,

* json-serializes problems for use in RESTful APIs and json/bson DBs, and

* provides a range of benchmark problems for (multi-objective) optimization and Bayesian optimization.

## BoFire

We are developing a successor of opti called [BoFire](https://github.com/experimental-design/bofire) and recommend using that. To help you

with the transition to BoFire there is https://github.com/experimental-design/bofire-converters.