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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.
Cited literature [34 references]
https://hal.archives-ouvertes.fr/hal-01077329
Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Friday, October 24, 2014 - 2:40:12 PM
Last modification on : Saturday, January 15, 2022 - 3:58:49 AM
Long-term archiving on: : Sunday, January 25, 2015 - 10:27:06 AM
Contributor : Emilie Chouzenoux Connect in order to contact the contributor
Submitted on : Friday, October 24, 2014 - 2:40:12 PM
Last modification on : Saturday, January 15, 2022 - 3:58:49 AM
Long-term archiving on: : Sunday, January 25, 2015 - 10:27:06 AM
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