Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Optimization and Control
2018-05-22 v2
Abstract
We present an extension of the cut-pursuit algorithm, introduced by Landrieu and Obozinski (2017), to the graph total-variation regularization of functions with a separable nondifferentiable part. We propose a modified algorithmic scheme as well as adapted proofs of convergence. We also present a heuristic approach for handling the cases in which the values associated to each vertex of the graph are multidimensional. The performance of our algorithm, which we demonstrate on difficult, ill-conditioned large-scale inverse and learning problems, is such that it may in practice extend the scope of application of the total-variation regularization.
Cite
@article{arxiv.1802.04383,
title = {Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation},
author = {Hugo Raguet and Loïc Landrieu},
journal= {arXiv preprint arXiv:1802.04383},
year = {2018}
}