Headings with content from the Course syllabus FEP3302 (Spring 2022–) are denoted with an asterisk ( )
Content and learning outcomes
Course disposition
Introduction (Lecture 1)
o MM principle
o A geometric interpretation
o Convexity for Majorization
o Examples
Key Inequalities for MM (Lecture 2 and 3)
o Applications of Jensen’s inequality
o Applications of the Cauchy-Schwarz inequality
o Applications of supporting hyperplane inequality
o Application of quadratic upper bounds
o Application of arithmetic-geometric mean inequality
Majorization and Partial Optimization (Lecture 4)
o Main principle
o Examples
Application in Engineering (Lecture 5 and 6)
o EM algorithm
o Regression
o Estimation with missing data
o Total variation denoising of images
o Factor analysis
o Matrix completion
Course contents
Introduction (Lecture 1)
o MM principle
o A geometric interpretation
o Convexity for Majorization
o Examples
Key Inequalities for MM (Lecture 2 and 3)
o Applications of Jensen’s inequality
o Applications of the Cauchy-Schwarz inequality
o Applications of supporting hyperplane inequality
o Application of quadratic upper bounds
o Application of arithmetic-geometric mean inequality
Majorization and Partial Optimization (Lecture 4)
o Main principle
o Examples
Application in Engineering (Lecture 5 and 6)
o EM algorithm
o Regression
o Estimation with missing data
o Total variation denoising of images
o Factor analysis
o Matrix completion
Intended learning outcomes
LO1: Recognize the concept of MM Principle.
LO2: Incorporate techniques for majorization and minorization into the design of MM
optimization algorithms.
LO3: Implement numerically the MM optimization algorithms in various application.
Literature and preparations
Specific prerequisites
Multi variable analysis, probability theory
Recommended prerequisites
Multi variable calculus, probability theory
Literature
You can find information about course literature either in the course memo for the course offering or in the course room in Canvas.
Examination and completion
Grading scale
P, F
Examination
EXA1 - Examination, 7.0 credits, grading scale: P, F
Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.
The examiner may apply another examination format when re-examining individual students.
If the course is discontinued, students may request to be examined during the following two academic years.
A take-home exam (4-5 problems) or/and group presentations of a simple implementation
All members of a group are responsible for the group's work.
In any assessment, every student shall honestly disclose any help received and sources used.
In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.
Further information
Course room in Canvas
Registered students find further information about the implementation of the course in the course room in Canvas. A link to the course room can be found under the tab Studies in the Personal menu at the start of the course.
