optimagic 0.5.0rc1

Tools to solve difficult numerical optimization problems.

optimagic

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:

pip install optimagic

or

$ 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@Unpublished{Gabler2024,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/OpenSourceEconomics/optimagic}
}

Acknowledgements

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