Convex Predictor–Nonconvex Corrector Optimization Strategy with Application to Signal Decomposition
Laura Girometti,
Martin Huska,
Alessandro Lanza
et al.
Abstract:Many tasks in real life scenarios can be naturally formulated as nonconvex optimization problems. Unfortunately, to date, the iterative numerical methods to find even only the local minima of these nonconvex cost functions are extremely slow and strongly affected by the initialization chosen. We devise a predictor–corrector strategy that efficiently computes locally optimal solutions to these problems. An initialization-free convex minimization allows to predict a global good preliminary candidate, which is th… Show more
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