Our Research

Speaker: Martin Jaggi, post-doctoral researcher at Ecole Polytechnique, Palaiseau, France

Coordinate descent on one hand, and the Frank-Wolfe algorithm on the other hand are two of the earliest known first-order methods for convex optimization. Here we will combine the two methods to obtain a new randomized block-coordinate optimization algorithm for block-separable constrained problems, which appear for example in machine learning and computer vision. This is motivated by the hope to combine the advantages of the two methods, namely the cheap iteration complexity of coordinate descent, and the sparse iterates and primal-dual convergence guarantees from Frank-Wolfe. Read more