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A computational comparison of the dinic and network simplex methods for maximum flow

  • Chapter II Network Flows
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Abstract

We study the implementation of two fundamentally different algorithms for solving the maximum flow problem: Dinic's method and the network simplex method. For the former, we present the design of a storage-efficient implementation. For the latter, we develop a "steepest-edge" pivot selection criterion that is easy to include in an existing network simplex implementation. We compare the computational efficiency of these two methods on a personal computer with a set of generated problems of up to 4 600 nodes and 27 000 arcs.

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References

  1. M.J. Best and K. Ritter,Linear Programming: Active Set Analysis and Computer Programs (Prentice-Hall, Englewood Cliffs, New Jersey, 1985).

    Google Scholar 

  2. R.V. Cherkasky, Algorithms of construction of maximum flow in networks with complexityO(V 2E) operations, Mathematical Methods of Solution of Economical Problems 7(1977)112 (in Russian).

    Google Scholar 

  3. W.H. Cunningham, A network simplex method, Math. Progr. 1(1976)105.

    Google Scholar 

  4. W.H. Cunningham, Theoretical properties of the network simplex method, Math. of Oper. Res. 4(1979)196.

    Google Scholar 

  5. T-Y. Cheung, Computational comparison of eight methods for the maximum network flow problem, ACM Trans. on Math. Software 6(1980)1.

    Google Scholar 

  6. E.A. Dinic, Algorithms for solution of a problem of maximum flow in networks with power estimation, Sov. Math. Doklady 11(1970)1277.

    Google Scholar 

  7. J. Edmonds and R.M. Karp, Theoretical improvements in algorithmic efficiency for network flow problems, J. Assoc. Computing Machinery 19(1972)248.

    Google Scholar 

  8. S. Even,Graph Algorithms (Computer Science Press, Potomac, Maryland, 1979).

    Google Scholar 

  9. L.R. Ford, Jr. and D.R. Fulkerson, Maximal flow through a network, Can. J. Math. 8(1956)399.

    Google Scholar 

  10. L.R. Ford, Jr. and D.R. Fulkerson,Flows in Networks (Princeton University Press, New Jersey, 1962).

    Google Scholar 

  11. Z. Galil, AnO(V 5/3 E 2/3) algorithm for the maximum flow problem, Acta Informatica 14(1980)221.

    Google Scholar 

  12. Z. Galil and A. Naamad, AnO(EV log2 V) algorithm for the maximum flow problem, J. Comput. Syst. Sci. 21(1980)203.

    Google Scholar 

  13. A.V. Goldberg and R.E. Tarjan, A new approach to the maximum flow problem,18th Annual ACM Symp. on Theory of Computing (1986) pp. 136–146.

  14. F. Glover, D. Klingman, J. Mote and D. Whitman, An extended abstract of an indepth algorithmic and computational study for maximum flow problems, Discr. Appl. Math. 2(1980)251.

    Google Scholar 

  15. D. Goldfarb, Steepest edge simplex algorithms for network flow problems, Technical Report RC 6649, T.J. Watson Research Laboratory, IBM Corporation, Yorktown Heights, New York (1977).

    Google Scholar 

  16. D. Goldfarb and J.K. Reid, A practicable steepest-edge simplex algorithm, Math. Progr. 12(1977)361.

    Google Scholar 

  17. D. Goldfarb and M.D. Grigoriadis, An efficient steepest edge algorithm for the maximum flow problem, paper presented at theXth Int. Symp. on Math. Progr., Montreal, Canada (1979).

  18. M.D. Grigoriadis, An efficient implementation of the network simplex method, Math. Progr. Study 26(1986)83.

    Google Scholar 

  19. M.D. Grigoriadis, RMFGEN — A generator for a class of maximum flow networks (in preparation).

  20. M.D. Grigoriadis and T. Hsu, RNET, SIGMAP Bulletin (1978).

  21. P.M.J. Harris, Pivot selection methods of the Devex LP code, Math. Progr. 5(1973)1.

    Google Scholar 

  22. E. Johnson, Networks and basic solutions, Oper. Res. 14(1966)619.

    Google Scholar 

  23. R.M. Karp, A characterization of the minimum cycle mean in a digraph, Discr. Math. 23(1978)309.

    Google Scholar 

  24. A.V. Karzanov, Determining the maximal flow in a network by the method of preflows, Sov. Math. Doklady 15(1974)434.

    Google Scholar 

  25. H.W. Kuhn and R.E. Quandt, An experimental study of the simplex method, in:Proc. of Symposia in Applied Math., Vol. XV, ed. Metropolis et al. (Amer. Math. Assoc., 1963).

  26. E.L. Lawler,Combinatorial Optimization: Networks and Matroids (Holt, Rinehart and Winston, New York, 1976).

    Google Scholar 

  27. V.M. Malhotra, M. Pramodh Kumar and S.N. Maheshwari, AnO(|V|3) algorithm for finding maximum flows in networks, Inf. Proc. Lett. 7(1978)277.

    Google Scholar 

  28. J.M. Mulvey, Pivot strategies for primal-simplex network codes, J. Assoc. Computing Mach. 25(1978)266.

    Google Scholar 

  29. C. Papadimitriou and K. Steiglitz,Combinatorial Optimization: Algorithms and Complexity (Prentice-Hall, Englewood Cliffs, New Jersey, 1982).

    Google Scholar 

  30. Y. Shiloach, AnO(nI log2 I) maximum flow algorithm, Technical Report Stan-CS-78-802, Computer Science Department, Stanford University, California (1978).

    Google Scholar 

  31. Y. Shiloach and U. Vishkin, AnO(n 2 logn) parallel max-flow algorithm, J. Algorithms 3(1982)128.

    Google Scholar 

  32. D.D. Sleator, AnO(nm logn) algorithm for maximum network flows, Technical Report Stan-CS-80-831, Computer Science Department, Stanford University, California (1978).

    Google Scholar 

  33. D.D. Sleator and R.E. Tarjan, A data structure for dynamic trees, J. Computer and System Sci. 24(1983)362.

    Google Scholar 

  34. R.E. Tarjan,Data Structures and Network Algorithms (Society for Industrial and Applied Mathematics, Philadelphia, Pennsylvania, 1983).

    Google Scholar 

  35. R.E. Tarjan, A simple version of Karzanov's blocking flow algorithm, Oper. Res. Lett. 2(1984)265.

    Google Scholar 

  36. N. Zadeh, More pathological examples for network flow problems, Math. Progr. 5(1973)217.

    Google Scholar 

  37. P. Wolfe and L. Cutler, Experiments in linear programming, in:Recent Advances in Mathematical Programming, ed. Graves and Wolfe (McGraw-Hill, 1963).

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This research was supported in part by the National Science Foundation under Grant Nos. MCS-8113503 and DMS-8512277.

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Goldfarb, D., Grigoriadis, M.D. A computational comparison of the dinic and network simplex methods for maximum flow. Ann Oper Res 13, 81–123 (1988). https://doi.org/10.1007/BF02288321

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