Skip to main content
Log in

An efficient cost scaling algorithm for the assignment problem

  • Published:
Mathematical Programming Submit manuscript

Abstract

The cost scaling push-relabel method has been shown to be efficient for solving minimum-cost flow problems. In this paper we apply the method to the assignment problem and investigate implementations of the method that take advantage of assignment's special structure. The results show that the method is very promising for practical use.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R.K. Ahuja, J.B. Orlin, C. Stein and R.E. Tarjan, “Improved algorithms for bipartite network flow,”SIAM Journal on Computing 23 (1994) 906–933.

    Google Scholar 

  2. R.J. Anderson and J.C. Setubal, “Goldberg's algorithm for the maximum flow in perspective: a computational study,” in: D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993) pp. 1–18.

    Google Scholar 

  3. D.P. Bertsekas, “The auction algorithm: a distributed relaxation method for the assignment problem,”Annals of Operations Research 14 (1988) 105–123.

    Google Scholar 

  4. D.P. Bertsekas,Linear Network Optimization: Algorithms and Codes (MIT Press, Cambridge, MA, 1991).

    Google Scholar 

  5. R.G. Bland, J. Cheriyan, D.L. Jensen and L. Ladańyi, “An empirical study of min cost flow algorithms,” in: D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993) pp. 119–156.

    Google Scholar 

  6. D.A. Castañon, “Reverse auction algorithms for the assignment problems,” in: D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993) pp. 407–430.

    Google Scholar 

  7. U. Derigs, “The shortest augmenting path method for solving assignment problems — motivation and computational experience,”Annals of Operations Research 4 (1985–1986) 57–102.

    Google Scholar 

  8. U. Derigs and W. Meier, “Implementing Goldberg's max-flow algorithm — a computational investigation,”Zeitschrift für Operations Research 33 (1989) 383–403.

    Google Scholar 

  9. S. Fujishige, K. Iwano, J. Nakano and S. Tezuka, “A speculative contraction method for the minimum cost flows: toward a practical algorithm,” in: D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993) pp. 219–246.

    Google Scholar 

  10. H.N. Gabow and R.E. Tarjan, “Faster scaling algorithms for network problems,”SIAM Journal on Computing 18 (1989) 1013–1036.

    Google Scholar 

  11. A.V. Goldberg, “Efficient graph algorithms for sequential and parallel computers,” Ph.D. Thesis, Massachusetts Institute of Technology, Cambridge, MA (1987); also: Technical Report TR-374, Laboratory for Computer Science, Massachusetts Institute of Technology, Cambridge, MA (1987).

    Google Scholar 

  12. A.V. Goldberg, “An efficient implementation of a scaling minimum-cost flow algorithm,” in: E. Balas and J. Clausen, eds.,Proceedings of the Third Integer Programming and Combinatorial Optimization Conference (Springer, Berlin, 1993) pp. 251–266.

    Google Scholar 

  13. A.V. Goldberg and R. Kennedy, “Global price updates help,” Technical Report STAN-CS-94-1509, Department of Computer Science, Stanford University, CA (1994).

    Google Scholar 

  14. A.V. Goldberg and M. Kharitonov, “On implementing scaling push-relabel algorithms for the minimumcost flow problem,” in: D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993) pp. 157–198.

    Google Scholar 

  15. A.V. Goldberg, S.A. Plotkin and P.M. Vaidya, “Sublinear-time parallel algorithms for matching and related problems,”Journal of Algorithms 14 (1993) 180–213.

    Google Scholar 

  16. A.V. Goldberg and R.E. Tarjan, “A new approach to the maximum flow problem,”Journal of the Association for Computing Machinery 35 (1988) 921–940.

    Google Scholar 

  17. A.V. Goldberg and R.E. Tarjan, “Finding minimum-cost circulations by successive approximation,”Mathematics of Operations Research 15 (1990) 430–466.

    Google Scholar 

  18. D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993).

    Google Scholar 

  19. R. Jonker and A. Volgenant, “A shortest augmenting path algorithm for dense and sparse linear assignment problems,”Computing 38 (1987) 325–340.

    Google Scholar 

  20. D. Knuth, Personal communication (1993).

  21. H.W. Kuhn, “The Hungarian method for the assignment problem,”Naval Research Logistics Quarterly 2 (1955) 83–97.

    Google Scholar 

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

    Google Scholar 

  23. Q.C. Nguyen and V. Venkateswaran, “Implementations of Goldberg—Tarjan maximum flow algorithm,” in: D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993) pp. 19–42.

    Google Scholar 

  24. J.B. Orlin and R.K. Ahuja, “New scaling algorithms for the assignment and minimum cycle mean problems,” Sloan Working Paper 2019-88, Sloan School of Management, Massachusetts Institute of Technology, Cambridge, MA (1988).

    Google Scholar 

  25. K.G. Ramakrishnan, N.K. Karmarkar and A.P. Kamath, “An approximate dual projective algorithm for solving assignment problems,” in: D.S. Johnson and C.C. McGeoch, eds.,Network Flows and Matching: First DIMACS Implementation Challenge (American Mathematical Society, Providence, RI, 1993) pp. 431–452.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Additional information

This author's research was supported in part by ONR Young Investigator Award N00014-91-J-1855, NSF Presidential Young Investigator Grant CCR-8858097 with matching funds from AT&T, DEC and 3M, and a grant from the Powell Foundation.

This author's research was supported by the above-mentioned ONR and NSF grants.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Goldberg, A.V., Kennedy, R. An efficient cost scaling algorithm for the assignment problem. Mathematical Programming 71, 153–177 (1995). https://doi.org/10.1007/BF01585996

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01585996

Keywords

Navigation