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The Sliding Frank-Wolfe Algorithm for the BLASSO

Abstract : This paper showcases the theoretical and numerical performance of the Sliding Frank-Wolfe, which is a novel optimization algorithm to solve the BLASSO sparse spikes super-resolution problem. The BLASSO is a continuous (i.e. off-the-grid or grid-less) counterpart to the well-known 1 sparse regularisation method (also known as LASSO or Basis Pursuit). Our algorithm is a variation on the classical Frank-Wolfe (also known as conditional gradient) which follows a recent trend of interleaving convex optimization updates (corresponding to adding new spikes) with non-convex optimization steps (corresponding to moving the spikes). Our main theoretical result is that this algorithm terminates in a finite number of steps under a mild non-degeneracy hypothesis. We then target applications of this method to several instances of single molecule fluorescence imaging modalities, among which certain approaches rely heavily on the inversion of a Laplace transform. Our second theoretical contribution is the proof of the exact support recovery property of the BLASSO to invert the 1-D Laplace transform in the case of positive spikes. On the numerical side, we conclude this paper with an extensive study of the practical performance of the Sliding Frank-Wolfe on different instantiations of single molecule fluorescence imaging, including convolutive and non-convolutive (Laplace-like) operators. This shows the versatility and superiority of this method with respect to alternative sparse recovery technics.
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Contributor : Françoise Grélaud <>
Submitted on : Tuesday, November 24, 2020 - 12:32:39 PM
Last modification on : Wednesday, December 2, 2020 - 9:16:51 PM


The Sliding Frank-Wolfe Algori...
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  • HAL Id : hal-03012568, version 1


Quentin Denoyelle, Vincent Duval, Gabriel Peyré, Emmanuel Soubies. The Sliding Frank-Wolfe Algorithm for the BLASSO. Workshop on Signal Processing with Adaptative Sparse Structured Representations - SPARS 2019, Sep 2019, Toulouse, France. ⟨hal-03012568⟩



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