Boosting the Sliding Frank-Wolfe solver for 3D deconvolution
This work addresses efficiency issues in 3D deconvolution for researchers in sparse optimization, though it is incremental as it builds on an existing method.
The paper tackled the computational burden of the Sliding Frank-Wolfe algorithm for 3D deconvolution by introducing a boosted version, achieving the same results in significantly reduced time.
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.