An efficient parallel algorithm for planarization problem

Author

Wang, Rong Long ; Tang, Zheng ; Cao, Qi Ping

Author_Institution

Fac. of Eng., Toyama Univ., Japan

Volume

49

Issue

3

fYear

2002

fDate

3/1/2002 12:00:00 AM

Firstpage

397

Lastpage

401

Abstract

A parallel algorithm for solving the planarization problem using a gradient ascent learning of Hopfield network is presented. This algorithm which is designed to embed a graph on a plane, uses the Hopfield neural network to get a near-maximal planar subgraph, and increase the energy by modifying weights in a gradient ascent direction to help the network escape from the state of the near-maximal planar subgraph to the state of the maximal planar subgraph or better one. The proposed algorithm is verified by a large number of simulation runs and compared with other parallel algorithms for the planarization problem. The experimental results show that the proposed algorithm can generate as good as or better solutions than the other existing parallel algorithm for the planarization problem

Keywords

Hopfield neural nets; computational geometry; learning (artificial intelligence); parallel algorithms; Hopfield neural network; NP-complete problem; gradient ascent learning; near-maximal planar subgraph; parallel algorithm planarization; Algorithm design and analysis; Antennas and propagation; Circuits; Dynamic range; Optical interferometry; Parallel algorithms; Phased arrays; Planarization; Robustness; Signal resolution;

fLanguage

English

Journal_Title

Circuits and Systems I: Fundamental Theory and Applications, IEEE Transactions on

Publisher

ieee

ISSN

1057-7122

Type

jour

DOI

10.1109/81.989179

Filename

989179