Number | Title | ECTS | Hours | Lecturers |
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227-0690-11L | Large-Scale Convex Optimization | 4 credits | 2V + 2U | G. Banjac |
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. |
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. |