### Poster

## One-sided Frank-Wolfe algorithms for saddle problems

### Vladimir Kolmogorov · Thomas Pock

Keywords: [ Convex Optimization ]

Abstract:
We study a class of convex-concave saddle-point problems of the form
$\min_x\max_y \langle Kx,y\rangle+f_{\cal P}(x)-h^*(y)$ where $K$ is
a linear operator, $f_{\cal P}$ is the sum of a convex function $f$
with a Lipschitz-continuous gradient and the indicator function of a
bounded convex polytope ${\cal P}$, and $h^\ast$ 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 {\em linear
minimization oracle} ($lmo$) for $f_{\cal P}$ and an efficient {\em
proximal map} ($prox$) 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 $O(1/n^2)$
convergence rate on the dual objective, requiring $O(n \log n)$
calls of $lmo$. If the problem comes from the constrained
optimization problem $\min_{x\in\mathbb
R^d}\{f_{\cal P}(x)\:|\:Ax-b=0\}$ then we additionally get bound
$O(1/n^2)$ 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 $O(n\log n)$ 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.

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