This course is a graduate course, given jointly by the School of Electrical 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.
Course memo Spring 2021
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Course presentation
Headings denoted with an asterisk ( * ) is retrieved from the course syllabus version Spring 2019
Content and learning outcomes
Course contents
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Convex sets
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Convex functions
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Convex optimization
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Linear and quadratic programming
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Geometric and semidefinite programming
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Duality
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Smooth unconstrained minimization
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Sequential unconstrained minimization
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Interior-point methods
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Decomposition and large-scale optimization
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Applications in estimation, data fitting, control and communications
Intended learning outcomes
After completed course, the student should be able to
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characterize fundamental aspects of convex optimization (convex functions, convex sets, convex optimization and duality);
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characterize and formulate linear, quadratic, geometric and semidefinite programming problems;
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implement, in a high level language such as Matlab, crude versions of modern methods for solving convex optimization problems, e.g., interior methods;
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solve large-scale structured problems by decomposition techniques;
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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 2021.
Lectures will be given via Zoom, https://kth-se.zoom.us/j/68296620263
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.
(There may be slight changes in the schedule. It is still preliminary.)
Detailed plan
| L# | Date | Time | Venue | Topic | Lecturer |
|---|---|---|---|---|---|
| 1 | Wed Mar 24 | 13-15 | Zoom | Introduction | MB/AF/JJ |
| 2 | Fri Mar 26 | 13-15 | Zoom | Convexity | AF |
| 3 | Wed Mar 31 | 13-15 | Zoom | Linear programming and the simplex method | AF |
| 4 |
Wed Apr 7 |
13-15 | Zoom | Lagrangian relaxation, duality and optimality for linearly constrained problems | AF |
| 5 | Fri Apr 9 | 13-15 | Zoom | Sensitivity and multiobjective optimization | MB |
| 6 | Wed Apr 14 | 13-15 | Zoom | Convex programming and semidefinite programming | AF |
| 7 | Fri Apr 16 | 13-15 | Zoom | Smooth convex unconstrained and equality-constrained minimization | AF |
| 8 | Wed Apr 21 | 13-15 | Zoom | Applications of conic programming | MB |
| 9 | Fri Apr 23 | 13-15 | Zoom | Interior methods | AF |
| 10 | Wed Apr 28 | 13-15 | Zoom | Large-scale optimization | JJ |
| 11 | Fri Apr 30 | 13-15 | Zoom | Applications | MB |
| 12 | Wed May 5 | 10-12 | Zoom | Applications | JJ |
Hand-in dates for homework assignments
Hand-in dates for the four homework assignments, specified in Examination and Completion below, are April 7, April 16, April 23 and May 5. 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 Wednesday May 12.
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:
Funka - compensatory support for students with disabilities
Please inform the course coordinator if you need compensatory support during the course. Present a certificate from Funka.
Course registration
PhD students from KTH register through e-ISP and 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.
Further information
No information inserted
Contacts
Course Coordinator
Teachers
Examiner
Round Facts
Start date
Missing mandatory information
Course offering
- Spring 2021-61149
Language Of Instruction
English