2023-08-16 –, Aula
estimagic is a Python package for nonlinear optimization with or without constraints. It is particularly suited to solving difficult nonlinear estimation problems. On top, it provides functionality to perform statistical inference on estimated parameters.
In this presentation, we give a tour through estimagic's most notable features and explain its position in the ecosystem of Python libraries for numerical optimization.
Challenging numerical optimization problems arise in many places in science and industry, for example, in the calibration of scientific models, engineering, and statistics. Solving them requires high-quality optimizers and diagnostic tools that help select a suitable algorithm and monitor the optimization's progress.
Estimagic provides a unified interface to optimization algorithms from scipy, nlopt, pygmo, and many other libraries. The minimize function feels familiar to users of scipy.optimize who are looking for a more extensive set of supported optimizers. Advanced users can use optional arguments to configure every aspect of the optimization, create a persistent log file, turn local optimizers global with a multistart framework, and more. Estimagic can calculate numerical derivatives in parallel, and many optimizers can leverage parallel hardware without requiring changes to the user's criterion function.
In this presentation, we give a tour through estimagic's most notable features and explain its position in the ecosystem of Python libraries for numerical optimization.
Learning and Teaching Scientific Python
Abstract as a tweet:estimagic is a Python package for nonlinear optimization with or without constraints. Come to Euroscipy2023 to learn to see it in action and learn more about its powerful features!
Expected audience expertise: Domain:some
Expected audience expertise: Python:some
Public link to supporting material: Project Homepage / Git:
Author of estimagic | PhD in economics | Expert in numerical optimization | Building Bandsaws, Pizza Ovens and Furniture