Optimization theory is nowadays a well-developed area, both in the theoretical and practical aspects. This graduate course introduces the basic concepts for solving optimization problems and illustrates this theory with many recent applications in signal processing, communication systems and machine learning.
Students attending this course will:
- Develop a solid theoretical basis for solving convex optimization problems arising in industry and research.
- Learn manipulations to unveil the hidden convexity of optimization problems and relaxation techniques to treat non-convex optimization problems.
- Be able to characterize the solution of convex and non-convex optimization problems either analytically or algorithmically.
- Learn the usage of some of the more popular optimization toolboxes.