Content

• Introduction to convex optimization: concepts in convex analysis (duality, proximal operators), illustrations in supervised learning.
• Convex optimization algorithms (Gradient, Proximal Gradient, Conditional Gradient, ADMM).
• Stochastic gradient and incremental algorithms (SGD, SAGA, SVRG).
• Distributed optimisation algorithms, stochastic algorithms, asynchronous methods.

Evaluation

3 ETCS: report on a practical session (1/3) + presentation of a research article (2/3)