  The proximal problem for structured penalties obtained via convex relaxations
of submodular functions is known to be equivalent to minimizing separable
convex functions over the corresponding submodular polyhedra. In this paper, we
reveal a comprehensive class of structured penalties for which penalties this
problem can be solved via an efficiently solvable class of parametric maxflow
optimization. We then show that the parametric maxflow algorithm proposed by
Gallo et al. and its variants, which runs, in the worst-case, at the cost of
only a constant factor of a single computation of the corresponding maxflow
optimization, can be adapted to solve the proximal problems for those
penalties. Several existing structured penalties satisfy these conditions;
thus, regularized learning with these penalties is solvable quickly using the
parametric maxflow algorithm. We also investigate the empirical runtime
performance of the proposed framework.

Machine Learning;Numerical Analysis