The concave-convex procedure
AL Yuille, A Rangarajan - Neural computation, 2003 - ieeexplore.ieee.org
The concave-convex procedure (CCCP) is a way to construct discrete-time iterative
dynamical systems that are guaranteed to decrease global optimization and energy
functions monotonically. This procedure can be applied to almost any optimization problem,
and many existing algorithms can be interpreted in terms of it. In particular, we prove that all
expectation-maximization algorithms and classes of Legendre minimization and variational
bounding algorithms can be reexpressed in terms of CCCP. We show that many existing …
dynamical systems that are guaranteed to decrease global optimization and energy
functions monotonically. This procedure can be applied to almost any optimization problem,
and many existing algorithms can be interpreted in terms of it. In particular, we prove that all
expectation-maximization algorithms and classes of Legendre minimization and variational
bounding algorithms can be reexpressed in terms of CCCP. We show that many existing …
The concave-convex procedure (CCCP)
AL Yuille, A Rangarajan - Advances in neural information …, 2001 - proceedings.neurips.cc
Abstract We introduce the Concave-Convex procedure (CCCP) which con (cid: 173) structs
discrete time iterative dynamical systems which are guar (cid: 173) anteed to monotonically
decrease global optimization/energy func (cid: 173) tions. It can be applied to (almost) any
optimization problem and many existing algorithms can be interpreted in terms of CCCP. In
particular, we prove relationships to some applications of Legendre transform techniques.
We then illustrate CCCP by applications to Potts models, linear assignment, EM algorithms …
discrete time iterative dynamical systems which are guar (cid: 173) anteed to monotonically
decrease global optimization/energy func (cid: 173) tions. It can be applied to (almost) any
optimization problem and many existing algorithms can be interpreted in terms of CCCP. In
particular, we prove relationships to some applications of Legendre transform techniques.
We then illustrate CCCP by applications to Potts models, linear assignment, EM algorithms …
