EL3300/SF3849 Convex optimization with engineering applications, 6cr

General information

Examiners: Ulf Jönsson (Mathematics), Anders Forsgren (Mathematics)

(The course is given by two professors from mathematics this time, as Mikael Johansson is on leave.)

The course consists of 24h lectures, given during Period 2, 2010.

Course literature: S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004, ISBN: 0521833787

Aim

After completed course, you 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 by decomposition techniques;
• 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
• Decomposition and large-scale optimization
• Applications in estimation, data fitting, control and communications

Course requirements

There is one version of the course given this time, the 6-credit version

1. The 6-credit version requires successful completion of home work assignments and the presentation of a short lecture on a special topic

There will be a total of four sets of hand-ins 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. The presentation should be limited to 10 minutes.

Prerequisites

Schedule