Solving large-scale inverse problems using forward-backward based methods.

A. Repetti, E. Chouzenoux, et J.-C. Pesquet.

Séminaire Scube - Supelec,
Gif-sur-Yvette, France, 11 Mars 2016.

Recent developments in imaging and data analysis techniques came along with an increasing need for fast convex optimization methods for solving large scale problems. A simple optimization strategy to minimize the sum of a Lipschitz differentiable function and a non smooth function is the forward-backward algorithm. In this presentation, several approaches to accelerate convergence speed and to reduce complexity of this algorithm will be proposed. More precisely, in a first part, preconditioning methods adapted to non convex minimization problems will be presented, and in a second part, stochastic optimization techniques will be described in the context of convex optimization. The different proposed methods will be used to solve several inverse problems in signal and image processing.




Biomedical and Astronomical Signal Processing group
Institute of Sensors, Signals and Systems
Heriot-Watt University
Edinburgh EH14 4AS
Scotland UK

mail: A.Repetti@hw.ac.uk