We consider the minimization of a function G defined on RN , 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.
- School of Engineering & Physical Sciences - Assistant Professor
- School of Mathematical & Computer Sciences - Assistant Professor
- School of Engineering & Physical Sciences, Institute of Sensors, Signals & Systems - Assistant Professor
- School of Mathematical & Computer Sciences, Actuarial Mathematics & Statistics - Assistant Professor
Person: Academic (Research & Teaching)