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 TextbookKenneth Lange, MM Optimization Algorithms LecturesSchedule is available here and a summary of lectures is available here. Grading
Course Learning OutcomesAfter finishing the course, the attendant will
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 lectures. PrerequisitesBackground in elementary analysis, convex analysis, linear algebra, and statistics. |