This course is a graduate course, given jointly by the School of Electrical Engineering, and the Department of Mathematics at KTH. The course is primarily not intended for students with focus on optimization, but rather aimed for students from other areas.
Content and learning outcomes
Linear and quadratic programming
Geometric and semidefinite programming
Smooth unconstrained minimization
Sequential unconstrained minimization
Decomposition and large-scale optimization
Applications in estimation, data fitting, control and communications
Intended learning outcomes
After completed course, the student should be able to
characterize fundamental aspects of convex optimization (convex functions, convex sets, convex optimization and duality);
characterize and formulate linear, quadratic, geometric and semidefinite programming problems;
implement, in a high level language such as Matlab, crude versions of modern methods for solving convex optimization problems, e.g., interior methods;
solve large-scale structured problems by decomposition techniques;
give examples of applications of convex optimization within statistics, communications, signal processing and control.
Literature and preparations
The course requires basic knowledge of calculus and linear algebra.
S. Boyd och L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004, ISBN: 0521833787
Examination and completion
If the course is discontinued, students may request to be examined during the following two academic years.
- INL1 - Assignment, 6.0 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.
Other requirements for final grade
Successful completion of homework assignments and the presentation of a short lecture on a special topic.
There will be a total of four sets of homework assignments distributed during the course. Late homework solutions are not accepted.
The short lecture should sum up the key ideas, techniques and results of a (course-related) research paper in a clear and understandable way to the other attendees.
Opportunity to complete the requirements via supplementary examination
Opportunity to raise an approved grade via renewed examination
- 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 about the course can be found on the Course web at the link below. Information on the Course web will later be moved to this site.Course web FSF3847