Name
Title | ||
|---|---|---|
A preconditioned Forward-Backward approach with application to large-scale nonconvex spectral unmixing problems |
Abstract | ||
|---|---|---|
Many inverse problems require to minimize a criterion being the sum of a non necessarily smooth function and a Lipschitz differentiable function. Such an optimization problem can be solved with the Forward-Backward algorithm which can be accelerated thanks to the use of variable metrics derived from the Majorize-Minimize principle. The convergence of this approach is guaranteed provided that the criterion satisfies some additional technical conditions. Combining this method with an alternating minimization strategy will be shown to allow us to address a broad class of optimization problems involving large-size signals. An application example to a nonconvex spectral unmixing problem will be presented. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.1109/ICASSP.2014.6853847 | Acoustics, Speech and Signal Processing |
Keywords | DocType | ISSN |
concave programming,inverse problems,signal processing,Lipschitz differentiable function,inverse problems,large scale nonconvex spectral unmixing problems,majorize minimize principle,preconditioned forward backward approach,Block coordinate algorithm,Forward-Backward algorithm,Large-scale problems,Nonconvex optimization,Nonsmooth optimization | Conference | 1520-6149 |
Citations | PageRank | References |
3 | 0.41 | 15 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Audrey Repetti | 1 | 76 | 6.84 |
| Emilie Chouzenoux | 2 | 202 | 26.37 |
| Jean-Christophe Pesquet | 3 | 560 | 46.10 |