## 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. Amphi Dussane Schedule (2018-2019): Mondays from 13h30 until 16h30. Oct. 2018: 1, 8, 15, 22 (no class on Oct. 29). Nov. 2018: 5, 12.
## 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 15 in class. From the Convex Optimization textbook: Ex. 2.12, 3.21, 3.32, 3.36 DM2: Due Monday October 22 in class. Homework 2
## ExamFinal exam, in class, November 26 2018, 9h00-12h00 amphi Curie at ENS Cachan. |