Readers: Jie Lu, Richard Combes, Alexandre Proutiere
Prerequisites: Basics in analysis, optimization theory, and probability
Credits: 8hp
Grading: P/F (homework + take-home exam)

Thank you again for taking this course. We hope that you learnt techniques that you can use in your research! The solutions to the take-home exam are available [here]

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The course is based on the course offered at UC Berkeley by Prof. M. Johansson and Prof. L. El Ghaoui in 2012, and the course A. Proutiere gave in Hamilton institute and IIT Mumbai in 2012 and 2013. The course have four parts: In the first part, we explore recent advances in first-order methods for convex optimisation (which constitute the main building block for many of the more advanced algorithms developed later). The second part focuses on algorithms for distributed optimisation under computation and communication constraints. Our starting point here is mathematical decomposition techniques traditionally developed for exploiting structure in large-scale optimisation. The third part is devoted to distributed stochastic optimisation techniques, including stochastic approximation and simulation-based methods. In the last part, we present recent advances in the theory of distributed learning in repeated games.