We study a class of convex-concave saddle-point problems of the form min
x maxy < Kx,y > +fP(x)-h*(y)
where K is a linear operator, fP is the sum of a convex function f with a Lipschitz-continuous gradient and the indicator function of a bounded convex polytope P, and h* is a convex (possibly nonsmooth) function.
Such problem arises, for example, as a Lagrangian relaxation of various discrete optimization problems.
Our main assumptions are the existence of an efficient linear minimization oracle (lmo) for P and an efficient proximal map for h* which motivate the solution via a blend of proximal primal-dual algorithms and Frank-Wolfe algorithms.
In case h* is the indicator function of a linear constraint and function f is quadratic, we show a O(1/n2) convergence rate on the dual objective, requiring O(nlogn) calls of lmo. If the problem comes from the constrained optimization problem
then we additionally get bound O(1/n2) both on the primal gap and on the infeasibility gap. In the most general case, we show a O(1/n) convergence rate of the primal-dual gap again requiring O(nlogn) calls of lmo. To the best of our knowledge, this improves on the known convergence rates for the considered class of saddle-point problems. We show applications to labeling problems frequently appearing in machine learning and computer vision.