In this focused tutorial, participants will be introduced to the fundamentals of numerical optimization and various optimization libraries, including scipy.optimize and estimagic. The session is divided into three blocks, with each focusing on a specific aspect of optimization:

Introduction to numerical optimization and scipy.optimize
Introduction to estimagic and how to pick optimizers
Strategies and tools for advanced optimization

The tutorial is designed to be hands-on, dedicating ample time to practice sessions. Including numerous smaller practice sessions allows participants to apply their knowledge of each topic immediately.

During the first block, participants will learn the basics of numerical optimization, code up their first optimization problem and solve it with scipy.optimize.

In the second block, we introduce estimagic, as a unified interface to algorithms from scipy, nlopt, pygmo, and others. Participants will try different optimizers and learn how estimagic's diagnostic tools can be used to select the suitable algorithm for a given problem.

The third block will focus on practical strategies for advanced optimization problems. Participants will learn about logging and restarting optimizations, global optimization, bounds, and constraints, as well as derivative-free and noisy optimization problems.

The session will conclude with a summary of the main learnings, ensuring that participants have gained a solid understanding of numerical optimization and the various optimization libraries covered.