Skip to main content

Lagrangean relaxation for integer programming

  • Chapter
  • First Online:
Approaches to Integer Programming

Part of the book series: Mathematical Programming Studies ((MATHPROGRAMM,volume 2))

Abstract

Taking a set of “complicating” constraints of a general mixed integer program up into the objective function in a Lagrangean fashion (with fixed multipliers) yields a “Lagrangean relaxation” of the original program. This paper gives a systematic development of this simple bounding construct as a means of exploiting special problem structure. A general theory is developed and special emphasis is given to the application of Lagrangean relaxation in the context of LP-based branch-and-bound.

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

Access this chapter

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. R.D. Armstrong and P. Sinha, “Improved penalty calculations for a mixed integer branch-and-bound algorithm”, Mathematical Programming 6 (1974) 212–223.

    Article  MATH  MathSciNet  Google Scholar 

  2. R. Brooks and A. Geoffrion, “Finding Everett’s Lagrange multipliers by linear programming”, Operations Research 14 (1966) 1149–1153.

    Article  MathSciNet  Google Scholar 

  3. Dakin, R.J., “A free search algorithm for mixed integer programming problems”, Computer Journal, 8 (1965) 250–255.

    Article  MATH  MathSciNet  Google Scholar 

  4. N.J. Driebeek, “An algorithm for the solution of mixed integer programming problems”, Management Science 12 (1966) 576–587.

    Article  Google Scholar 

  5. Everett, H.M., “Generalized Lagrange multiplier method for solving problems of optimum allocation of resources”, Operations Research 11 (1966) 399–417.

    Article  MathSciNet  Google Scholar 

  6. M.L. Fisher, “Optimal solution of scheduling problems using Lagrange multipliers: Part I”, Operations Research 21 (1973) 1114–1127.

    Article  MATH  MathSciNet  Google Scholar 

  7. M.L. Fisher, “A dual algorithm for the one-machine scheduling problem”, Graduate School of Business Rept., University of Chicago, Chicago, Ill. (1974).

    Google Scholar 

  8. M.L. Fisher, W.D. Northup and J.F. Shapiro, “Using duality to solve discrete optimization problems: Theory and computational experience”, Working Paper OR 030-74, Operations Research Center, M.I.T. (1974).

    Google Scholar 

  9. M.L. Fisher and L. Schrage, “Using Lagrange multipliers to schedule elective hospital admissions”, Working Paper, University of Chicago, Chicago, Ill. (1972).

    Google Scholar 

  10. M.L. Fisher and J.F. Shapiro, “Constructive duality in integer programming”, SIAM Journal on Applied Mathematics, to appear.

    Google Scholar 

  11. J.J.H. Forrest, J.P.H. Hirst and J.A. Tomlin, “Practical solution of large mixed integer programming problems with UMPIRE”, Management Science 20 (1974) 733–773.

    Article  MathSciNet  Google Scholar 

  12. A.M. Geoffrion, “An improved implicit enumeration approach for integer programming”, Operations Research 17 (1969) 437–454.

    Article  MATH  Google Scholar 

  13. A.M. Geoffrion, “Duality in nonlinear programming”, SIAM Review 13 (1971) 1–37.

    Article  MATH  MathSciNet  Google Scholar 

  14. A.M. Geoffrion and R.E. Marsten, “Integer programming algorithms: A framework and state-of-the-art survey”, Management Science 18 (1972) 465–491.

    Article  MATH  MathSciNet  Google Scholar 

  15. A.M. Geoffrion and R.D. McBride, “The capacitated facility location problem with additional constraints”, paper presented to the Joint National Meeting of AIIE, ORSA, and TIMS, Atlantic City, November 8–10, 1972.

    Google Scholar 

  16. F. Glover, “A multiphase-dual algorithm for the zero-one integer programming problem”, Operations Research 13 (1965) 879–919.

    Article  MATH  MathSciNet  Google Scholar 

  17. F. Glover, “Surrogate constraints”, Operations Research 16 (1968) 741–749.

    Article  MATH  MathSciNet  Google Scholar 

  18. H.J. Greenberg and T.C. Robbins, “Finding Everett’s Lagrange multipliers by Generalized Linear Programming, Parts I, II, and III”, Tech. Rept. CP-70008, Computer Science/Operations Research Center, Southern Methodist University, Dallas, Tex. revised (June 1972).

    Google Scholar 

  19. W.C. Healy, Jr., “Multiple choice programming”, Operations Research 12 (1964) 122–138.

    Article  MATH  MathSciNet  Google Scholar 

  20. M. Held and R.M. Karp, “The traveling salesman problem and minimum spanning trees”, Operations Research 18 (1970) 1138–1162.

    Article  MATH  MathSciNet  Google Scholar 

  21. M. Held and R.M. Karp, “The traveling salesman problem and minimum spanning trees: Part II”, Mathematical Programming 1 (1971) 6–25.

    Article  MATH  MathSciNet  Google Scholar 

  22. M. Held, P. Wolfe and H.P. Crowder, “Validation of subgradient optimization”, Mathematical Sciences Department, IBM Watson Research Center, Yorktown Heights, N.Y. (August 1973).

    Google Scholar 

  23. W.W. Hogan, R.E. Marsten and J.W. Blankenship, “The BOXSTEP method for large scale optimization”, Working Paper 660-73, Sloan School of Management, M.I.T. (December 1973).

    Google Scholar 

  24. R.E. Marsten, private communication (August 22, 1973).

    Google Scholar 

  25. G.L. Nemhauser and Z. Ullman, “A note on the generalized Lagrange multiplier solution to an integer programming problem”, Operations Research 16 (1968) 450–452.

    Article  MATH  Google Scholar 

  26. G.T. Ross and R.M. Soland, “A branch and bound algorithm for the generalized assignment problem”, Mathematical Programming, to appear.

    Google Scholar 

  27. J.F. Shapiro, “Generalized Lagrange multipliers in integer programming”, Operations Research 19 (1971) 68–76.

    Article  MATH  MathSciNet  Google Scholar 

  28. J.A. Tomlin, “An improved branch and bound method for integer programming”, Operations Research 19 (1971) 1070–1075.

    Article  MATH  MathSciNet  Google Scholar 

  29. J.A. Tomlin, “Branch and bound methods for integer and non-convex programming”, in: J. Abadie, ed., Integer and nonlinear programming (North-Holland, Amsterdam, 1970).

    Google Scholar 

  30. A.F. Veinott and G.B. Dantzig, “Integral extreme points”, SIAM Review 10 (1968) 371–372.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

M. L. Balinski

Rights and permissions

Reprints and permissions

Copyright information

© 1974 The Mathematical Programming Society

About this chapter

Cite this chapter

Geoffrion, A.M. (1974). Lagrangean relaxation for integer programming. In: Balinski, M.L. (eds) Approaches to Integer Programming. Mathematical Programming Studies, vol 2. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0120690

Download citation

  • DOI: https://doi.org/10.1007/BFb0120690

  • Received:

  • Revised:

  • Published:

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00739-2

  • Online ISBN: 978-3-642-00740-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics