## Master M2 MVA: Convex Optimization, Algorithms and Applications.## DescriptionThe objective of this course is to learn to recognize, transform and solve a broad class of convex optimization problems arising in various fields such as machine learning, finance or signal processing. The course starts with a basic primer on convex analysis followed by a quick overview of convex duality theory. The second half of the course is focused on algorithms, including first-order and interior point methods, together with bounds on their complexity. The course ends with illustrations of these techniques in various applications. ## Course organizationLocation: E.N.S., 45 rue d'Ulm, 75005 Paris. Schedule (2017-2018): Mondays from 13h30 until 16h30. (Warning: note room changes below!) Oct. 2, 2017: Amphi Dussane Oct. 9, 2017: Amphi Dussane Oct. 16, 2017: Amphi Dussane Oct. 23, 2017: Amphi Jean Jaurès, 29 rue d'Ulm. Nov. 6, 2017: Amphi Dussane Nov. 13, 2017: Salle Actes
## OrganisationThe course is split in three parts. Six lectures of three hours each. Modelling Convex sets, functions and problems Duality
Algorithms Interior point methods Complexity First-order methods, acceleration
Applications Machine learning and statistics Signal processing Combinatorial problems Finance
## ReferencesConvex Optimization, S. Boyd and L. Vandenberghe, Cambridge University Press. Introductory Lectures on Convex Optimization, Y. Nesterov, Springer. Lectures on Modern Convex Optimization, A. Nemirovski and A. Ben-Tal, SIAM.
## Notes## ExercicesMany of the exercises are taken from the textbook by Boyd et Vandenberghe. Please turn in your home work in class. Late homework will not be graded. DM1: Due Monday October 16 in class. Homework 1 DM2: Due Monday October 23 in class. Homework 2 DM3: Due Monday November 13 before class. Homework 3 (updated on Nov. 8). Please mail your code to dm.daspremont@gmail.com in a single zip file called lastname-firstname-DM3.zip. For numerical exercises, you can use Juliabox if you don't have access to a machine with MATLAB, Python or Julia.
## ExamFinal exam, in class, December 19 2017, 13h30-16h30 amphi Curie at ENS Cachan. |