  In the context of gridless sparse optimization, the Sliding Frank Wolfe
algorithm recently introduced has shown interesting analytical and practical
properties. Nevertheless, is application to large data, such as in the case of
3D deconvolution, is computationally heavy. In this paper, we investigate a
strategy for leveraging this burden, in order to make this method more
tractable for 3D deconvolution. We show that a boosted SFW can achieve the same
results in a significantly reduced amount of time.
