Local Linear Convergence of Inertial Forward-Backward Splitting for Low Complexity Regularization

Even in supposedly quasi-static situations such as stress-strain experiments, episodes of shocks or quick rearrangements might happen (and are actually experimentally

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MANIFOLDS OF SMOOTH MAPS III : THE PRINCIPAL BUNDLE OF EMBEDDINGS OF A NON-COMPACT SMOOTH MANIFOLD.. by

In this paper, we consider the Forward–Backward proximal splitting algorithm to minimize the sum of two proper closed convex functions, one of which having a Lipschitz

We prove convergence in the sense of subsequences for functions with a strict stan- dard model, and we show that convergence to a single critical point may be guaranteed, if the

Section 1 provides Riemannian versions of 3 classical results of geomet- ric measure theory: Allard’s regularity theorem, the link between first variation and mean curvature in the

Under the assumption that the non-smooth part of the objective is partly smooth relative to an active smooth manifold, we show that iFB-type methods (i) identify the active manifold

Keywords: Regularization, regression, inverse problems, model consistency, partial smoothness, sensitivity analysis, sparsity, low-rank.. Samuel Vaiter was affiliated with CNRS