Large-scale Convex Optimization
Storskalig konvex optimering

FRT225F, 7.5 credits

Valid from: Autumn 2020
Decided by: Professor Thomas Johansson
Date of establishment: 2020-11-13

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. We present the basic theory of such problems, concentrating on results that are useful in algorithms for numerically solving the problems. The course will give the students a thorough understanding of how such problems are solved and give them experience in solving them, with the aim of giving the students the background required to use the methods in their own research.

Goals

Knowledge and Understanding

For a passing grade the doctoral student must

• Have knowledge about theory for convex sets, convex functions, duality, and algorithm convergence
• Understand how application problems can be formulated as convex optimization problems and how they are solved
• Have demonstrated understanding of how efficient algorithms for large-scale problems are implemented and work

Competences and Skills

For a passing grade the doctoral student must

• Demonstrate the ability to evaluate if an optimization problem is convex
• Formulate convex optimization problems in a standardized manner that is suitable for algorithms
• Be able to implement and apply a suitable algorithm for a given convex optimization problem and prove its convergence

Course Literature

Material developed by teacher.

Instruction Details

Types of instruction: Lectures, exercises

Examination Details

Examination formats: Written exam, oral exam, written assignments. Written or oral exam and written assignments during course.
Grading scale: Failed, pass
Examiner:

Admission Details

Course Occasion Information

Contact and Other Information

Course coordinators: