Metadata-Version: 2.1
Name: moocore
Version: 0.1.6
Summary: Core Algorithms for Multi-Objective Optimization
Author-email: Manuel López-Ibáñez <manuel.lopez-ibanez@manchester.ac.uk>, Fergus Rooney <fergus.rooney@outlook.com>
Project-URL: Documentation, https://multi-objective.github.io/moocore/python/
Project-URL: Homepage, https://multi-objective.github.io/moocore/python/
Project-URL: Source, https://github.com/multi-objective/moocore/
Project-URL: Tracker, https://github.com/multi-objective/moocore/issues
Keywords: math,Mathematics,Multi-criteria Decision-Making,operations research,Optimization
Classifier: Intended Audience :: Science/Research
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Classifier: Programming Language :: Python :: 3 :: Only
Classifier: Programming Language :: Python :: 3.10
Classifier: Programming Language :: Python :: 3.11
Classifier: Programming Language :: Python :: 3.12
Classifier: Programming Language :: Python :: 3.13
Requires-Python: >=3.10
Description-Content-Type: text/markdown
Requires-Dist: cffi>=1.15.1
Requires-Dist: numpy>=1.23
Requires-Dist: platformdirs

**moocore**: Core Algorithms for Multi-Objective Optimization
=============================================================

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[ [**Homepage**][py-moocore-homepage] ]
[ [**GitHub**][py-moocore-github] ]


**Contributors:**
    [Manuel López-Ibáñez](https://lopez-ibanez.eu),
    Fergus Rooney.

---------------------------------------

Introduction
============

The goal of **moocore** is to collect fast implementations of core mathematical functions and algorithms for multi-objective optimization. These functions include:

 * Identifying and filtering dominated vectors.
 * Quality metrics such as (weighted) hypervolume, epsilon, IGD, etc.
 * Computation of the Empirical Attainment Function. The empirical attainment function (EAF) describes the probabilistic
distribution of the outcomes obtained by a stochastic algorithm in the
objective space.

**Keywords**: empirical attainment function, summary attainment surfaces, EAF
differences, multi-objective optimization, bi-objective optimization,
performance measures, performance assessment


Install
-------

You can install the latest released using `pip`:

```bash
python3 -m pip install moocore
```


Or to build the latest development version from github:

```bash
python3 -m pip install 'git+https://github.com/multi-objective/moocore.git#egg=moocore&subdirectory=python'
```

You can also install binary development wheels for your operating system. See the list of wheels here (https://github.com/multi-objective/moocore/tree/wheels), click in the wheel you wish to install then copy the **View Raw** link. For example,

```bash
python3 -m pip install https://github.com/multi-objective/moocore/raw/refs/heads/wheels/moocore-0.1.5.dev0-py3-none-macosx_10_9_universal2.whl
```

If the URL does not have the word `raw` then you are not using the raw link.


R package
---------

There is also a `moocore` package for R: https://multi-objective.github.io/moocore/r


[py-build-badge]: https://github.com/multi-objective/moocore/actions/workflows/python.yml/badge.svg?event=push
[py-build-link]: https://github.com/multi-objective/moocore/actions/workflows/python.yml
[py-coverage-badge]: https://codecov.io/gh/multi-objective/moocore/branch/main/graph/badge.svg?flag=python
[py-coverage-link]: https://app.codecov.io/gh/multi-objective/moocore/tree/main/python
[py-moocore-github]: https://github.com/multi-objective/moocore/tree/main/python#readme
[py-moocore-homepage]: https://multi-objective.github.io/moocore/python
[py-moocore-pypi]: https://pypi.org/project/moocore/
