Convex Optimization with Applications
Konvex optimering med tillämpningar

FRT016F, 7.5 credits

Valid from: Autumn 2019
Decided by: FN1/Anders Gustafsson
Date of establishment: 2013-03-03

General Information

Division: Automatic Control
Course type: Third-cycle course
Teaching language: English

Aim

The goal of the course is to give students the tools and training to recognize convex optimization problems that arise in applications to present the basic theory of such problems, concentrating on results that are useful in computation to give students a thorough understanding of how such problems are solved, and some experience in solving them to give students the background required to use the methods in their own research work or applications

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• * have knowledge about theory for convex sets and functions
• * understand how application problems can be formulated as convex optimization problems
• * have demonstrated understanding of how efficient algorithms are implemented and works

Competences and Skills

For a passing grade the doctoral student must

• * have demonstrated skills in calculations with convex functions
• * be able to reformulate practical application problems as convex optimization problems
• * demonstrated ability in handling some existing program package for convex optimization and ability in writing code for simpler algorithms

Judgement and Approach

For a passing grade the doctoral student must demonstrate the ability to critically evaluate and compare different formulations of convex optimization problems and different algorithms for different quality criteria

Course Contents

The course has three parts * Basic theory for convex sets and functions * Experience of formulation of application problems as convex optimization problems * Knowledge and experience of efficient optimization algorithms

Course Literature

Boyd, S. & Vandenberghe, L.: Convex Optimization. Cambridge University Press.
Freely available on http://www.stanford.edu/~boyd/cvxbook/

Instruction Details

Types of instruction: Lectures, exercises

Examination Details

Examination formats: Written exam, written assignments, miscellaneous. Weekly handin problems Take-home exam Students should take an active role in the weekly exercise sessions
Grading scale: Failed, pass
Examiner:

Admission Details

Assumed prior knowledge: Linera algebra, calculus in several variables, probability theory

Further Information

Replaces FRT015F.

Course Occasion Information

Contact and Other Information

Course coordinator: Emil Vladu <emil.vladu@control.lth.se>