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