A Dynamical Approach to an Inertial Forward-Backward Algorithm for Convex Minimization

We introduce a new class of forward-backward algorithms for structured convex minimization problems in Hilbert spaces. Our approach relies on the time discretization of a second-order differential system with two potentials and Hessian-driven damping, recently introduced in [H. Attouch, P.-E. Mainge...

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Veröffentlicht in:SIAM journal on optimization Jg. 24; H. 1; S. 232 - 256
Hauptverfasser: Attouch, Hédy, Peypouquet, Juan, Redont, Patrick
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Philadelphia Society for Industrial and Applied Mathematics 01.01.2014
Schlagworte:
Algorithms
Control theory
Mathematics
Methods
Optimization
inertial forward-backward algorithm
convex minimization
dynamical approach
ISSN:1052-6234, 1095-7189
Online-Zugang:Volltext
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Zusammenfassung:We introduce a new class of forward-backward algorithms for structured convex minimization problems in Hilbert spaces. Our approach relies on the time discretization of a second-order differential system with two potentials and Hessian-driven damping, recently introduced in [H. Attouch, P.-E. Mainge, and P. Redont, Differ. Equ. Appl., 4 (2012), pp. 27--65]. This system can be equivalently written as a first-order system in time and space, each of the two constitutive equations involving one (and only one) of the two potentials. Its time dicretization naturally leads to the introduction of forward-backward splitting algorithms with inertial features. Using a Liapunov analysis, we show the convergence of the algorithm under conditions enlarging the classical step size limitation. Then, we specialize our results to gradient-projection algorithms and give some illustrations of sparse signal recovery and feasibility problems. [PUBLICATION ABSTRACT]
Bibliographie:ObjectType-Article-1
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ISSN:1052-6234
1095-7189
DOI:10.1137/130910294