  Sparse Blind Source Separation (sparse BSS) is a key method to analyze
multichannel data in fields ranging from medical imaging to astrophysics.
However, since it relies on seeking the solution of a non-convex penalized
matrix factorization problem, its performances largely depend on the
optimization strategy. In this context, Proximal Alternating Linearized
Minimization (PALM) has become a standard algorithm which, despite its
theoretical grounding, generally provides poor practical separation results. In
this work, we propose a novel strategy that combines a heuristic approach with
PALM. We show its relevance on realistic astrophysical data.
