You can download the slides of this talk.

Abstract: Consider a network of N agents (computing units) having private objective functions and seeking to find a minimum of the aggregate objective. The aim is to design iterative algorithms where, at a each iteration, an agent updates a local estimate of the minimizer based on the sole knowledge of its private function and the information received from its neighbors. In this talk, i will first provide an overview of standard distributed optimization methods. Then, i will explain how recent and generic results in stochastic optimization can be used in order to design asynchronous and adaptive distributed optimization algorithms.

You can download the slides of this talk.

Abstract: Real-world graphs (a.k.a. complex networks) are ubiquitous: the web, Facebook, brain networks, protein interaction networks, etc. Studying these graphs and solving computational problems on them (say maximum clique, partitioning or dense subgraph) has applications in many fields. I will first show that the structure of some real-world graphs is special. In particular, they are not adversarial and some difficult problems (say NP-hard problems) can be solved on some huge real-world graphs (say 2G edges or more). I will then present two works along the lines of “understanding and leveraging the structure of real-world graphs in order to build better algorithms”: (i) Enumerating all k-cliques and (ii) Computing the density-friendly graph decomposition. The latter one has been published in WWW2017.