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.
The documentation is hosted at https://optimagic.readthedocs.io
The package can be installed via pip or conda. To do so, type the following commands in a terminal:
pip install optimagicor
$ conda config --add channels conda-forge
$ conda install optimagicThe 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.
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)
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@Unpublished{Gabler2024,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}
}
We thank all institutions that have funded or supported optimagic (formerly estimagic)
