  Dictionary learning for sparse representations is traditionally approached
with sequential atom updates, in which an optimized atom is used immediately
for the optimization of the next atoms. We propose instead a Jacobi version, in
which groups of atoms are updated independently, in parallel. Extensive
numerical evidence for sparse image representation shows that the parallel
algorithms, especially when all atoms are updated simultaneously, give better
dictionaries than their sequential counterparts.
