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Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019
Content and learning outcomes
Course contents
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
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.
Learning activities
The course consists of 24h lectures, given during Period 4, spring 2023.
Lectures will be given in Room 3721, Lindstedtsvägen 25, KTH.
There will be four set of homeworks, including peer grading, and an oral presentation of a selected topic. Lecture notes, homework assignment and other material related to the course will be posted in Canvas.
Video recordings from the lectures when the course given in spring 2021 are available in Canvas, as a complement.
Detailed plan
L#
Date
Time
Venue
Topic
Lecturer
1
Tue Mar 21
13-15
Room 3721
Introduction
MB/AF/JJ
2
Fri Mar 24
13-15
Room 3721
Convexity
AF
3
Tue Mar 28
13-15
Room 3721
Linear programming and the simplex method
AF
4
Fri Mar 31
13-15
Room 3721
Lagrangian relaxation, duality and optimality for linearly constrained problems
AF
5
Tue Apr 4
10-12
Room 3721
Sensitivity and multiobjective optimization
MB
6
Tue Apr 18
13-15
Room 3721
Convex programming and semidefinite programming
AF
7
Fri Apr 21
13-15
Room 3721
Smooth convex unconstrained and equality-constrained minimization
AF
8
Tue Apr 25
13-15
Room 3721
Conic programming, dual decomposition and subgradient methods
MB
9
Fri Apr 28
13-15
Room 3721
Interior methods
AF
10
Tue May 2
13-15
Room 3721
Large-scale optimization
JJ
11
Fri May 5
13-15
Room 3721
Applications
MB
12
Tue May 9
13-15
Room 3721
Applications
JJ
Hand-in dates for homework assignments
Hand-in dates for the four homework assignments, specified in Examination and Completion below, are April 4, April 18, April 28 and May 9. Late homework solutions are not accepted.
Research presentation day
The presentations of a short lecture on a special topic, specified in Examination and Completion below, will be held on Tuesday May 16.
Preparations before course start
Literature
Course literature: S. Boyd and L. Vandenberghe, Convex Optimization, Cambridge University Press, 2004, ISBN: 0521833787
Support for students with disabilities
Students at KTH with a permanent disability can get support during studies from Funka:
PhD students from KTH register through regular registration procedures. Assistance can be obtained by sending e-mail to phdadm@math.kth.se.
PhD students from other universities must fill out this form and send signed copy by e-mail to phdadm@math.kth.se.
Examination and completion
Grading scale
P, F
Examination
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.
Ethical approach
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.