Subject: Algorithmic advances for nonsmooth and nonconvex constrained optimization

Thesis supervisor: Welington DE OLIVEIRA

Abstract: This thesis explores how to solve complex decision-making problems that arise in fields like engineering, data science, and risk analysis. These problems often involve conflicting goals, uncertainty, and difficult mathematical structures that make them hard to solve with standard tools. We develop new algorithms that can handle these challenges by breaking problems into simpler parts and using smart strategies to find good solutions. Our methods are tested on real-world examples, such as designing safer infrastructure and improving image processing. The results show that these techniques are both practical and reliable, opening new possibilities for solving difficult problems in science and industry.

Key words nonsmooth optimization, nonconvex optimization, constrained optimization

The defence will be held in English and will also be broadcast live. To receive the connection link, please contact us.