Graph partitioning via recurrent multivalued neural networks

Authors:
Enrique Mérida-Casermeiro;Domingo López-Rodríguez
Affiliations:
Department of Applied Mathematics, University of Málaga, Málaga, Spain;Department of Applied Mathematics, University of Málaga, Málaga, Spain
Venue:
IWANN'05 Proceedings of the 8th international conference on Artificial Neural Networks: computational Intelligence and Bioinspired Systems
Year:
2005

Citing 14
Cited 5

Counting almost minimum cutsets with reliability applications

Mathematical Programming: Series A and B
An application of combinatorial optimization to statistical physics and circuit layout design

Operations Research
Computing edge-connectivity in multigraphs and capacitated graphs

SIAM Journal on Discrete Mathematics
Min-cut clustering

Mathematical Programming: Series A and B
A new approach to the minimum cut problem

Journal of the ACM (JACM)
Minimum cuts in near-linear time

STOC '96 Proceedings of the twenty-eighth annual ACM symposium on Theory of computing
A neural algorithm for MAX-2SAT: performance analysis and circuit implementation

Neural Networks
Multilevel k-way partitioning scheme for irregular graphs

Journal of Parallel and Distributed Computing
A Dynamic Diffusion Optimization Method for Irregular Finite Element Graph Partitioning

The Journal of Supercomputing
Neural Network for Optimization and Combinatorics

Neural Network for Optimization and Combinatorics
An Efficient Multivalued Hopfield Network for the Traveling Salesman Problem

Neural Processing Letters
Computers and Intractability: A Guide to the Theory of NP-Completeness

Computers and Intractability: A Guide to the Theory of NP-Completeness
Array Distribution in Data-Parallel Programs

LCPC '94 Proceedings of the 7th International Workshop on Languages and Compilers for Parallel Computing
Neural implementation of Dijkstra's algorithm

IWANN'03 Proceedings of the Artificial and natural neural networks 7th international conference on Computational methods in neural modeling - Volume 1

Stochastic multivalued network for optimization: application to the graph Maxcut problem

CIMMACS'06 Proceedings of the 5th WSEAS International Conference on Computational Intelligence, Man-Machine Systems and Cybernetics
Stochastic Functional Annealing as Optimization Technique: Application to the Traveling Salesman Problem with Recurrent Networks

KI '07 Proceedings of the 30th annual German conference on Advances in Artificial Intelligence
Two pages graph layout via recurrent multivalued neural networks

IWANN'07 Proceedings of the 9th international work conference on Artificial neural networks
K-pages graph drawing with multivalued neural networks

ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Shortest common superstring problem with discrete neural networks

ICANNGA'09 Proceedings of the 9th international conference on Adaptive and natural computing algorithms

Quantified Score

Hi-index	0.00

Visualization

Abstract

In this work, the well-known Graph Partitioning (GP) problem for undirected weighted graphs has been studied from two points of view: maximizing (MaxCut) or minimizing (MinCut) the cost of the cut induced in the graph by the partition. An unified model, based on a neural technique for optimization problems, has been applied to these two concrete problems. A detailed description of the model is presented, and the technique to minimize an energy function, that measures the goodness of solutions, is fully described. Some techniques to escape from local optima are presented as well. It has proved to be a very competitive and efficient algorithm, in terms of quality of solutions and computational time, when compared to the state-of-the-art methods. Some simulation results are presented in this paper, to show the comparative efficiency of the methods.