  Non-negative matrix factorization is a problem of dimensionality reduction
and source separation of data that has been widely used in many fields since it
was studied in depth in 1999 by Lee and Seung, including in compression of
data, document clustering, processing of audio spectrograms and astronomy. In
this work we have adapted a minimization scheme for convex functions with
non-differentiable constraints called PALM to solve the NMF problem with
solutions that can be smooth and/or sparse, two properties frequently desired.
