https://github.com/optimagic-dev/optimagic

optimagic is a Python package for numerical optimization. It is a unified interface to optimizers from SciPy, NlOpt and other packages. optimagic's minimize function works just like SciPy's, so you don't have to adjust your code. You simply get more optimizers for free. On top you get diagnostic tools, parallel numerical derivatives and more.
https://github.com/optimagic-dev/optimagic

numerical-differentiation numerical-optimization parallel-computing python

Last synced: 2 months ago
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optimagic is a Python package for numerical optimization. It is a unified interface to optimizers from SciPy, NlOpt and other packages. optimagic's minimize function works just like SciPy's, so you don't have to adjust your code. You simply get more optimizers for free. On top you get diagnostic tools, parallel numerical derivatives and more.

• Host: GitHub
• URL: https://github.com/optimagic-dev/optimagic
• Owner: optimagic-dev
• License: mit
• Created: 2019-01-18T09:43:04.000Z (almost 7 years ago)
• Default Branch: main
• Last Pushed: 2025-10-20T13:21:17.000Z (2 months ago)
• Last Synced: 2025-10-21T19:58:33.258Z (2 months ago)
• Topics: numerical-differentiation, numerical-optimization, parallel-computing, python
• Language: Python
• Homepage: https://optimagic.readthedocs.io/
• Size: 27.3 MB
• Stars: 307
• Watchers: 7
• Forks: 47
• Open Issues: 44
• Metadata Files:
  • Readme: README.md
  • Changelog: CHANGES.md
  • License: LICENSE
  • Code of conduct: .github/CODE_OF_CONDUCT.md
  • Citation: CITATION

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README

          
# optimagic

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## Introduction

*optimagic* is a Python package for numerical optimization. It is a unified interface to

optimizers from SciPy, NlOpt and many other Python packages.

*optimagic*'s `minimize` function works just like SciPy's, so you don't have to adjust

your code. You simply get more optimizers for free. On top you get powerful diagnostic

tools, parallel numerical derivatives and more.

*optimagic* was formerly called *estimagic*, because it also provides functionality to

perform statistical inference on estimated parameters. *estimagic* is now a subpackage

of *optimagic*.

## Documentation

The documentation is hosted at https://optimagic.readthedocs.io

## Installation

The package can be installed via pip or conda. To do so, type the following commands in

a terminal:

```bash

pip install optimagic

```

or

```bash

$ conda config --add channels conda-forge

$ conda install optimagic

```

The first line adds conda-forge to your conda channels. This is necessary for conda to

find all dependencies of optimagic. The second line installs optimagic and its

dependencies.

## Installing optional dependencies

Only `scipy` is a mandatory dependency of optimagic. Other algorithms become available

if you install more packages. We make this optional because most of the time you will

use at least one additional package, but only very rarely will you need all of them.

For an overview of all optimizers and the packages you need to install to enable them

see {ref}`list_of_algorithms`.

To enable all algorithms at once, do the following:

`conda install nlopt`

`pip install Py-BOBYQA`

`pip install DFO-LS`

`conda install petsc4py` (Not available on Windows)

`conda install cyipopt`

`conda install pygmo`

`pip install fides>=0.7.4 (Make sure you have at least 0.7.1)`

## Citation

If you use optimagic for your research, please do not forget to cite it.

```

@Unpublished{Gabler2024,

  Title  = {optimagic: A library for nonlinear optimization},

  Author = {Janos Gabler},

  Year   = {2022},

  Url    = {https://github.com/optimagic-dev/optimagic}

}

```

## Acknowledgements

We thank all institutions that have funded or supported optimagic (formerly estimagic)