The spring semester 2021 will certainly take place online until Easter. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers.
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.
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.
Lecture notes
Available on the course Moodle platform.
Prerequisites / Notice
Sufficient mathematical maturity, in particular in linear algebra and analysis.
Performance assessment
Performance assessment information (valid until the course unit is held again)
Performance assessment as a semester course