In this hands-on tutorial, participants will delve into numerical optimization fundamentals and engage with the optimization libraries scipy.optimize and estimagic. estimagic provides a unified interface to many popular libraries such as nlopt or pygmo and provides additional diagnostic tools and convenience features. Throughout the tutorial, participants will get the opportunity to solve problems, enabling the immediate application of acquired knowledge. Topics covered include core optimization concepts, running an optimization with scipy.optimize and estimagic, diagnostic tools, algorithm selection, and advanced features of estimagic, such as bounds, constraints, and global optimization.

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.