  Monotone operator splitting is a powerful paradigm that facilitates parallel
processing for optimization problems where the cost function can be split into
two convex functions. We propose a generalized form of monotone operator
splitting based on Bregman divergence. We show that an appropriate design of
the Bregman divergence leads to faster convergence than conventional splitting
algorithms. The proposed Bregman monotone operator splitting (B-MOS) is applied
to an application to illustrate its effectiveness. B-MOS was found to
significantly improve the convergence rate.
