Description: I served twice (06.2024 block course and 02.2023 – 06.2023) as a teaching assistant for Dr. Michael Muehlebach at Eidgenössische Technische Hochschule Zürich for the course ``Large-Scale Convex Optimization’’. I was mainly responsible for the colloquia and designing exercises and exams.

Abstract

Convex optimization has revolutionized modern decision making and underpins many scientific and engineering disciplines. To enable its use in modern large-scale applications, we require new analytical methods that address limitations of existing solutions. This course is intended to provide a comprehensive overview of convex analysis and numerical methods for large-scale optimization.

Learning Objective

Students should be able to apply the fundamental results in convex analysis and numerical methods to analyze and solve large-scale convex optimization problems.

Content

Convex analysis and methods for large-scale optimization. Topics will include: convex sets and functions ; duality theory ; optimality and infeasibility conditions ; structured optimization problems ; gradient-based methods ; operator splitting methods ; distributed and decentralized optimization ; applications in various research areas.