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.
Students should be able to apply the fundamental results in convex analysis and numerical methods to analyze and solve large-scale convex optimization problems.
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.
Available on the course Moodle platform.
Prerequisites / Notice
Sufficient mathematical maturity, in particular in linear algebra and analysis.
Performance assessment information (valid until the course unit is held again)