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Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation
Hugo Raguet · loic landrieu

Fri Jul 13 09:15 AM -- 12:00 PM (PDT) @ Hall B #118

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.

Author Information

Hugo Raguet (LIVE (CNRS))
loic landrieu (IGN)

Reasearcher in MATIS team at IGN. Focus on structured learning and optimization with application to remote sensing.

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