In this talk we review some classical algorithms for solving structured convex optimization problems, passing from gradient descent to proximal iterations and going further to modern proximal primal-dual splitting algorithms in the case of more complicated objective functions. We put special attention to constrained convex optimization, in which we accelerate the performance of the algorithms by including additional projections onto a selection of the constraints. Applications to mean-field games and image processing are included.

Zoom meeting ID: 802 764 2791