Metadata-Version: 2.1
Name: mopti
Version: 0.10.2
Summary: Tools for experimental design and multi-objective optimization
Home-page: https://github.com/basf/mopti
Author: BASF SE
License: BSD-3
Keywords: Bayesian optimization,Multi-objective optimization,Experimental design
Platform: UNKNOWN
Classifier: Development Status :: 4 - Beta
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: License :: OSI Approved :: BSD License
Classifier: Topic :: Scientific/Engineering
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Developers
Requires-Python: >=3.7
Description-Content-Type: text/markdown
Provides-Extra: testing
License-File: LICENSE

# 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 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/ <br/>
**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.


