227-0690-11L  Advanced Topics in Control (Spring 2020)

SemesterSpring Semester 2020
LecturersG. Banjac
Periodicityyearly recurring course
Language of instructionEnglish
CommentNew topics are introduced every year.


AbstractAdvanced Topics in Control (ATIC) covers advanced research topics in control theory. It is offered each Spring semester with the topic rotating from year to year. Repetition for credit is possible, with consent of the instructor.
ObjectiveDuring Spring 2020 the course will cover a range of topics in large-scale convex optimization. The students should be able to apply various numerical methods to solve large-scale optimization problems arising in control, machine learning, signal processing, and finance.
ContentConvex analysis and methods for large-scale optimization. Topics will include: convex sets and functions ; duality theory ; optimality and infeasibility conditions ; structured optimization problems ; gradient-based methods ; operator splitting methods ; distributed and decentralized optimization ; applications in various research areas.
Lecture notesCopies of the projection slides will be made available on the course Moodle platform.
LiteratureThe course will be largely based on the Large-Scale Convex Optimization course taught at Lund University: Link
Prerequisites / NoticeSufficient mathematical maturity, in particular in linear algebra and analysis.