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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.
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Contributor : Aurélie Boisbunon Connect in order to contact the contributor
Submitted on : Friday, July 18, 2014 - 10:16:09 AM
Last modification on : Wednesday, May 11, 2022 - 3:24:03 AM
Long-term archiving on: : Monday, November 24, 2014 - 7:31:40 PM


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  • HAL Id : hal-01025585, version 1


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⟩



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