Cellular neural networks for NP-hard optimization

Authors:
Mária Ercsey-Ravasz;Tamás Roska;Zoltán Néda
Affiliations:
Department of Physics, University of Notre Dame, Notre Dame, IN;Faculty of Information Technology, Péter Pázmany Catholic University, Budapest, Hungary and Computer and Automation Research Institute, Hungarian Academy of Sciences, Budapest, Hungary;Faculty of Physics, Babes-Bolyai University, Cluj-Napoca, Romania
Venue:
EURASIP Journal on Advances in Signal Processing - CNN technology for spatiotemporal signal processing
Year:
2009

Citing 3
Cited 0

An introduction to econophysics: correlations and complexity in finance

An introduction to econophysics: correlations and complexity in finance
Statistical mechanics, three-dimensionality and NP-completeness: I. Universality of intracatability for the partition function of the Ising model across non-planar surfaces (extended abstract)

STOC '00 Proceedings of the thirty-second annual ACM symposium on Theory of computing
Theoretical Models in Biology: The Origin of Life, the Immune System, and the Brain

Theoretical Models in Biology: The Origin of Life, the Immune System, and the Brain

Quantified Score

Hi-index	0.00

Visualization

Abstract

A cellular neural/nonlinear network (CNN) is used for NP-hard optimization. We prove that a CNN in which the parameters of all cells can be separately controlled is the analog correspondent of a two-dimensional Ising-type (Edwards-Anderson) spin-glass system. Using the properties of CNN, we show that one single operation (template) always yields a localminimum of the spin-glass energy function. This way, a very fast optimization method, similar to simulated annealing, can be built. Estimating the simulation time needed on CNN-based computers, and comparing it with the time needed on normal digital computers using the simulated annealing algorithm, the results are astonishing. CNN computers could be faster than digital computers already at 10 × 10 lattice sizes. The local control of the template parameters was already partially realized on some of the hardwares, we think this study could further motivate their development in this direction.