Variable Metric Forward–Backward Algorithm for Minimizing the Sum of a Differentiable Function and a Convex Function

Emilie Chouzenoux (), Jean-Christophe Pesquet and Audrey Repetti
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Emilie Chouzenoux: Université Paris-Est Marne-la-Vallée
Jean-Christophe Pesquet: Université Paris-Est Marne-la-Vallée
Audrey Repetti: Université Paris-Est Marne-la-Vallée

Journal of Optimization Theory and Applications, 2014, vol. 162, issue 1, No 7, 107-132

Abstract: Abstract We consider the minimization of a function G defined on ${ \mathbb{R} } ^{N}$ , which is the sum of a (not necessarily convex) differentiable function and a (not necessarily differentiable) convex function. Moreover, we assume that G satisfies the Kurdyka–Łojasiewicz property. Such a problem can be solved with the Forward–Backward algorithm. However, the latter algorithm may suffer from slow convergence. We propose an acceleration strategy based on the use of variable metrics and of the Majorize–Minimize principle. We give conditions under which the sequence generated by the resulting Variable Metric Forward–Backward algorithm converges to a critical point of G. Numerical results illustrate the performance of the proposed algorithm in an image reconstruction application.

Keywords: Nonconvex optimization; Nonsmooth optimization; Majorize–Minimize algorithms; Forward–Backward algorithm; Image reconstruction; Proximity operator (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (14)

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DOI: 10.1007/s10957-013-0465-7

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