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Course PM
(The information on this page is for the previous course round. It will be updated.)¶
SF3847 Convex optimization with engineering applications, 6cr, 2018/2019 General information This course is a graduate course, given jointly by the Department of Information Science and 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.
Examiners: Mats Bengtsson (Information Science and Engineering), Anders Forsgren (Mathematics) and Joakim Jaldén (Information Science and Engineering).
The course consists of 24h lectures, given during Period 4, spring 2019.
Course literature: S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004, ISBN: 0521833787
Aim After completed course, students will 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;
* give examples of applications of convex optimization within statistics, communications, signal processing and control.
Syllabus
* Convex sets
* Convex functions
* Convex optimization
* Linear and quadratic programming
* Geometric and semidefinite programming
* Duality
* Smooth unconstrained minimization
* Sequential unconstrained minimization
* Interior-point methods
* Large-scale optimization
* Applications in estimation, data fitting, control and communications
Course registration PhD students from KTH register through e-ISP and regular course registration procedures. Clarification: First, make sure the course is included in your e-ISP. This you do together with your main academic advisor. Then, send an e-mail to Anders Forsgren.
PhD students from other universities must fill out this form and send signed and scanned copy by e-mail to Anders Forsgren.
Course requirements For passing the course, successful completion of homework assignments and presentation of a research paper in a short lecture at the presentation day are required.
There will be a total of four sets of hand-ins distributed during the course. Hand-in dates are March 28, April 9, April 23 and May 2. 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.
Prerequisites The course requires basic knowledge of calculus and linear algebra. Please contact the lecturers if you are uncertain about your prerequisities.
Schedule Lectures will be given in Room F11, Lindstedtsvägen 22, KTH.
Lecture notes will be posted in Canvas.
Lecture Date Time Venue Activity Lecturer 1 Tue Mar 19 13-15 Room F11 Introduction MB/AF/JJ 2 Thu Mar 21 13-15 Room F11 Convexity AF 3 Tue Mar 26 13-15 Room F11 Linear programming and the simplex method AF 4 Thu Mar 28 13-15 Room F11 Lagrangian relaxation, duality and optimality for linearly constrained problems AF 5 Tue Apr 2 13-15 Room F11 Sensitivity and multiobjective optimization MB 6 Thu Apr 4 13-15 Room F11 Convex programming and semidefinite programming AF 7 Tue Apr 9 8-10 Room F11 Applications of conic programming MB 8 Thu Apr 11 13-15 Room F11 Smooth convex unconstrained and equality-constrained minimization AF 9 Tue Apr 23 13-15 Room F11 Interior methods AF 10 Thu Apr 25 13-15 Room F11 Large-scale optimization JJ 11 Tue Apr 30 13-15 Room F11 Applications MB 12 Thu May 2 13-15 Room F11 Applications JJ Research paper presentations will be held on Thursday May 9.