The focus of the course is to provide in depth study of convex optimization tools in the context of important problems related to various engineering applications. On the one hand, these are very useful tools in order to understand, model and analyze correctly real problems, and on the other hand, these are also the key tools to design optimal or close-to-optimal solutions for these problems. The course covers the following topics: convex sets and convex functions, linear and quadratic programming, semi-definite programming, duality, Pareto optimization, first-order and second-order iterative optimization algorithms, interior-point methods. The various optimization techniques will be continuously illustrated to solve important engineering problems in different areas, such as approximation and fitting, statistical signal processing, classification, problems on graphs and communication networks, control, computational geometry, data analytics, machine learning, task scheduling and portfolio optimization.
Either a Final Take-home Exam or a Project work (student choice).
The Final Grade: pass (A or B) or fail (based on 30% of homework grade + 70% of final examination/project grade).
Faculty of Engineering and Science