nips nips2001 nips2001-180 nips2001-180-reference knowledge-graph by maker-knowledge-mining
Source: pdf
Author: Alan L. Yuille, Anand Rangarajan
Abstract: We introduce the Concave-Convex procedure (CCCP) which constructs discrete time iterative dynamical systems which are guaranteed to monotonically decrease global optimization/energy functions. 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, and Generalized Iterative Scaling (GIS). CCCP can be used both as a new way to understand existing optimization algorithms and as a procedure for generating new algorithms. 1
[1] J.N. Darroch and D. Ratcliff.
[2] R. Durbin, R. Szeliski and A.L. Yuille.
[3] LM. Elfadel
[4] R. Hathaway.
[5] J. Kosowsky and A.L. Yuille.
[6] E. Mjolsness and C. Garrett.
[7] A. Rangarajan, S. Gold, and E. Mjolsness.
[8] A. Rangarajan, A.L. Yuille, S. Gold. and E. Mjolsness.
[9] R. Sinkhorn.
[10] F.R. Waugh and R.M . Westervelt.
[11] A.L. Yuille.
[12] A.L. Yuille and J.J. Kosowsky.
[13] A.L. Yuille.