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- Title
Convex Predictor–Nonconvex Corrector Optimization Strategy with Application to Signal Decomposition.
- Authors
Girometti, Laura; Huska, Martin; Lanza, Alessandro; Morigi, Serena
- 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 then corrected by solving a parameter-free nonconvex minimization. A simple algorithm, such as alternating direction method of multipliers works surprisingly well in producing good solutions. This strategy is applied to the challenging problem of decomposing a 1D signal into semantically distinct components mathematically identified by smooth, piecewise-constant, oscillatory structured and unstructured (noise) parts.
- Subjects
COST functions; CROSS correlation; SIGNALS &; signaling; NOISE; ALGORITHMS
- Publication
Journal of Optimization Theory & Applications, 2024, Vol 202, Issue 3, p1286
- ISSN
0022-3239
- Publication type
Article
- DOI
10.1007/s10957-024-02479-2