  The proximal gradient descent method, well-known for composite optimization,
can be completely described by the concept of proximal gradient mapping. In
this paper, we highlight our previous two discoveries of proximal gradient
mapping--norm monotonicity and refined descent, with which we are able to
extend the recently proposed potential function-based framework from gradient
descent to proximal gradient descent.
