Richard J. Anderson
- AG91.F. Alizadeh and A. V. Goldberg. Experiments with the Push-Relabel Method for the Maximum Flow Problem on a Connection Machine. Paper presented at the DIMACS Implementation Challenge Workshop, 1991.Google Scholar
- AS92.R.J. Anderson and :I. C. Setubal. Goldberg~s Algorithm for Maximum Flow in Perspective: a Computational Study. Submitted for inclusion in the DI- MACS Implementation Challenge Workshop Proceedings, 1992.Google Scholar
- BMPT91.E. Balas, D. Miller, J. Pekny. and P. Toth. A Parallel Shortest Augmenting Path Algorithm for the Assignment Problem. JACM, 38(4):985-1004, 1991. Google ScholarDigital Library
- CM89.J. Cheriyan and S. N. Maheshwari. Analysis of Preflow Push Algorithras for Maximum Network Flow. SIAM Journal on Computing, 18(6):1057-1086, 1989. Google ScholarDigital Library
- DM89.U. Derigs and W. Meier. Implementing Goldberg's Max-Flow Algorithm a Computational Investigation. ZOR Methods and Models of Operation.s Research, 33:383-403, 1989.Google Scholar
- Din70.E.A. Dinic. Algorithm for Solution of a Problem of Maximum Flow in a Network With Power Estimation. Soviet Math. Dokl., 11:1277-1280, 1970.Google Scholar
- Gol87.A.V. Goldberg. Efficient Graph Algorithms for Sequential and Parallel Computers. Ph. D. dissertation, Massachussetts Institute of Technology, Cambridge, Mass., Jan. 1987.Google Scholar
- GT88.A. V. Goldberg and R. E. Tarjan. A New Approach to the Maximum- Flow Problem. Journal of the A CM, 35(4):921-940, 1988. Google ScholarDigital Library
- GG88.D. Goldfarb and M. Grigoriadis. A Computational Comparison of the Dinic and Network Simplex Methods for Maximum Flow. Annals of Operations Research, 13:83-123, 1988.Google ScholarCross Ref
- Law76.E. L. Lawler. Combinatorial Optimization: Networks and Matroids. Holt, Rinehart, and Winston, New York, 1976.Google Scholar
- PS82.C. H. Papadimitriou and K. Steiglitz. Combinatorial Optimization: Algorithms and Complexity. Prentice- Hall, Englewood Cliffs, N. J., 1982. Google ScholarDigital Library
- Tar83.R.E. Tarjan. Data Structures and Network Algorithms. SIAM, Philadelphia, Pennsylvania, 1983. Google ScholarDigital Library
Index Terms
- On the parallel implementation of Goldberg's maximum flow algorithm
Recommendations
Implementation of Parallel Genetic Algorithm Based on CUDA
ISICA '09: Proceedings of the 4th International Symposium on Advances in Computation and IntelligenceGenetic Algorithm (GA) is a powerful tool for science computing, while Parallel Genetic Algorithm (PGA) further promotes the performance of computing. However, the traditional parallel computing environment is very difficult to set up, much less the ...
An efficient crossover architecture for hardware parallel implementation of genetic algorithm
In this article a new architecture for hardware implementation of genetic algorithm in reconfigurable embedded systems is presented. The main idea is based on the efficient use of a genetic algorithm's crossover operator to enhance the speed of ...
The Implementation and Comparison of Two Kinds of Parallel Genetic Algorithm Using Matlab
DCABES '10: Proceedings of the 2010 Ninth International Symposium on Distributed Computing and Applications to Business, Engineering and ScienceTwo kinds of parallel genetic algorithm (PGA) are implemented in this paper based on the MATLAB® Parallel Computing Toolbox™ and Distributed Computing Server™ software. Parallel for-loops, SPMD (Single Program Multiple Data) block and co-distributed ...
Comments