• Corpus ID: 17696185

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

@article{Kawahara2015ParametricMF,
title={Parametric Maxflows for Structured Sparse Learning with Convex Relaxations of Submodular Functions},
author={Y. Kawahara and Yutaro Yamaguchi},
journal={ArXiv},
year={2015},
volume={abs/1509.03946}
}
• Published 14 September 2015
• Computer Science, Mathematics
• ArXiv
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…

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