Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions

14 Sep 2015Yoshinobu KawaharaYutaro Yamaguchi

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... (read more)

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