Skip to Main content Skip to Navigation
Conference papers

Active set strategy for high-dimensional non-convex sparse optimization problems

Abstract : The use of non-convex sparse regularization has attracted much interest when estimating a very sparse model on high dimensional data. In this work we express the optimality conditions of the optimization problem for a large class of non-convex regularizers. From those conditions, we derive an efficient active set strategy that avoids the computing of unnecessary gradients. Numerical experiments on both generated and real life datasets show a clear gain in computational cost w.r.t. the state of the art when using our method to obtain very sparse solutions.
Document type :
Conference papers
Complete list of metadata

Cited literature [19 references]  Display  Hide  Download

https://hal.inria.fr/hal-01025585
Contributor : Aurélie Boisbunon <>
Submitted on : Friday, July 18, 2014 - 10:16:09 AM
Last modification on : Thursday, March 25, 2021 - 11:46:03 AM
Long-term archiving on: : Monday, November 24, 2014 - 7:31:40 PM

File

boisbunon2014active.pdf
Publisher files allowed on an open archive

Identifiers

  • HAL Id : hal-01025585, version 1

Citation

Aurélie Boisbunon, Rémi Flamary, Alain Rakotomamonjy. Active set strategy for high-dimensional non-convex sparse optimization problems. ICASSP - IEEE International Conference on Acoustics Speech and Signal Processing, May 2014, Florence, Italy. ⟨hal-01025585⟩

Share

Metrics

Record views

716

Files downloads

511