  Beurling LASSO generalizes the LASSO problem to finite Radon measures
regularized via their total variation. Despite its theoretical appeal, this
space is hard to parametrize, which poses an algorithmic challenge. We propose
a formulation of continuous convolutional source separation with Beurling LASSO
that avoids the explicit computation of the measures and instead employs the
duality transform of the proximal mapping.
