## MM Optimization Algorithms and Applications: Course Information
## Description
The MM principle provides a mechanism for creating optimization algorithms. If the objective function is to be minimized, the key idea is to compute a surrogate function that majorizes the objective function. The solution of the surrogate function minimization is used to compute the next surrogate function that majorizes the objective function. The process is continued until convergence. It has pervasive applications in many engineering application domains. The celebrated EM algorithm in computational statistics is a special case of the MM principle.
## Intended AudiencePhD students in areas of applied mathematics, communication, control, computer sciences, networking, civil engineering. ## Course Textbook
Kenneth Lange, ## Lectures
Schedule is available ## Grading
**Pass/Fail**To pass the course, at least 70% of the grades have to be achieved.
The course evaluation consists of Homework (30%) Take Home Exam (30%) Mini Project (40%)
## Course Learning OutcomesAfter finishing the course, the attendant will Recognize the concept of MM Principle. Incorporate techniques for majorization and minorization into the design of MM optimization algorithms. Implement numerically the MM optimization algorithms in various application.
## Working load2h per Week + homework + one take home exam + mini-project report. ## CreditsThis is a 7 credit course. ## Teaching and learning methodologyThe lectures will be mainly based on blackboard and slides, see ## PrerequisitesBackground in elementary analysis, convex analysis, linear algebra, and statistics. |