The spring semester 2021 will take place online until further notice. Exceptions: Courses that can only be carried out with on-site presence. Please note the information provided by the lecturers.

227-0690-11L  Large-Scale Convex Optimization

SemesterSpring Semester 2021
LecturersG. Banjac
Periodicityyearly recurring course
Language of instructionEnglish


AbstractConvex 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.
ObjectiveStudents should be able to apply the fundamental results in convex analysis and numerical methods to analyze and solve large-scale convex optimization problems.
ContentConvex 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 notesAvailable on the course Moodle platform.
Prerequisites / NoticeSufficient mathematical maturity, in particular in linear algebra and analysis.