Sparse models are widely used in machine learning, statistics, and signal/image processing applications. They usually lead to NP-hard non-convex optimization problems that involve the l0 pseudo-norm. The purpose of this thematic day is to bring together speakers from different teams in order to explore the recent progress made in solving these challenging non-convex optimization problems. We expect to cover a variety of methods that include (but not only) greedy algorithms, continuous relaxations, screening rules, as well as global optimization through branch-and-bound strategies or semidefinite programming.
Full program with abstracts [here]