[go: up one dir, main page]
More Web Proxy on the site http://driver.im/ Skip to main content
Log in

A technique for speeding up the solution of the Lagrangean dual

  • Published:
Mathematical Programming Submit manuscript

Abstract

We propose techniques for the solution of the LP relaxation and the Lagrangean dual in combinatorial optimization and nonlinear programming problems. Our techniques find the optimal solution value and the optimal dual multipliers of the LP relaxation and the Lagrangean dual in polynomial time using as a subroutine either the Ellipsoid algorithm or the recent algorithm of Vaidya. Moreover, in problems of a certain structure our techniques find not only the optimal solution value, but the solution as well. Our techniques lead to significant improvements in the theoretical running time compared with previously known methods (interior point methods, Ellipsoid algorithm, Vaidya's algorithm). We use our method to the solution of the LP relaxation and the Langrangean dual of several classical combinatorial problems, like the traveling salesman problem, the vehicle routing problem, the Steiner tree problem, thek-connected problem, multicommodity flows, network design problems, network flow problems with side constraints, facility location problems,K-polymatroid intersection, multiple item capacitated lot sizing problem, and stochastic programming. In all these problems our techniques significantly improve the theoretical running time and yield the fastest way to solve them.

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

Access this article

Subscribe and save

Springer+ Basic
£29.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Price includes VAT (United Kingdom)

Instant access to the full article PDF.

Similar content being viewed by others

References

  1. R. Ahuja, A. Goldberg, J. Orlin and R. Tarjan, “Finding minimum-cost flows by double scaling,” to appear in:SIAM Journal of Computing (1991).

  2. R. Ahuja, J. Orlin, C. Stein and R. Tarjan, “Improved algorithms for bipartite network flow”, “Improved algorithms for bipartite network flows,” Sloan working paper 3218-90-MS, MIT (Cambridge, MA, 1990).

    Google Scholar 

  3. A. Balakrishnan, T.L. Magnanti and R.T. Wong, “A dual-ascent procedure for large-scale uncapacitated network design,”Operations Research 5 (1989) 716–740.

    Google Scholar 

  4. F. Barahona, M. Grötschel, M. Jünger and G. Reinelt, “An application of combinatorial optimization to statistical physics and circuit layout design,”Operations Research 36 (1988) 493–513.

    Google Scholar 

  5. I. Barany, T.J. Van Roy and L.A. Wolsey, “Uncapacitated lot sizing: the convex hull of solutions,”Mathematical Programming Study 22 (1984) 32–43.

    Google Scholar 

  6. V. Chandru and M. Trick, “On the complexity of Lagrange multiplier search,” unpublished manuscript.

  7. D. Coppersmith and S. Winograd, “Matrix multiplication via arithmetic progressions,”Proceedings of the 19th Annual ACM Symposium on Theory of Computing (1987) 1–6.

  8. H. Crowder, E.L. Johnson and M.W. Padberg, “Solving large scale zero–one linear programming problems,”Operations Research 31 (1983) 803–834.

    Google Scholar 

  9. H. Crowder and M.W. Padberg, “Solving large scale symmetric traveling salesman problems to optimality,”Management Science 26 (1980) 495–509.

    Google Scholar 

  10. W. Cunningham, “Testing membership in matroid polyhedra,”Journal of Combinatorial Theory Series B 36 (1984) 161–188.

    Google Scholar 

  11. G.D. Eppen and R.K. Martin, “Solving multi-item capacitated lot sizing problems using variable redefinition,”Operations Research 35 (1987) 832–848.

    Google Scholar 

  12. M. Fisher, “The Lagrangean relaxation method for solving integer programming problems,”Management Science 27 (1981) 1–18.

    Google Scholar 

  13. A. Geoffrion, “Lagrangean relaxation and its uses in integer programming,”Mathematical Programming Study 2 (1974) 82–114.

    Google Scholar 

  14. M. Goemans and D. Bertsimas, “Survivable networks, linear programming relaxations and the parsimonious property,”Mathematical Programming 60 (1993) 145–166.

    Google Scholar 

  15. M. Grötschel, M. Jünger and G. Reinelt, “A cutting plane algorithm for the linear ordering problem,”Operations Research 32 (1984) 1195–1220.

    Google Scholar 

  16. M. Grötschel, L. Loväsz and A. Schrijver,Geometric Algorithms and Combinatorial Optimization (Springer, Berlin, 1988).

    Google Scholar 

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

    Google Scholar 

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

    Google Scholar 

  19. M. Held, P. Wolfe and H. Crowder, “Validation of subgradient optimization,”Mathematical Programming 6 (1974) 62–88.

    Google Scholar 

  20. E.L. Johnson, M.M. Kostreva and H.H. Suhl, “Solving 0–1 integer programming problems arising from large scale planning models,”Operations Research 33 (1985) 802–820.

    Google Scholar 

  21. K. Jornsten and M. Nasberg, “A new Lagrangean relaxation approach to the generalized assignment problem,”European Journal of Operations Research 27 (1986) 313–323.

    Google Scholar 

  22. L.G. Khachian, “A polynomial algorithm for linear programming,”Soviet Mathematics Doklady 20 (1979) 191–194.

    Google Scholar 

  23. J. Leung, T.L Magnanti and R. Vachani, “Facets and algorithms for capacitated lot sizing,”Mathematical Programming 45 (1989) 331–359.

    Google Scholar 

  24. T.L. Magnanti, P. Mireault and R.T. Wong, “Tailoring Benders decomposition for uncapacitated network design,”Mathematical Programming Study 26 (1986) 112–154.

    Google Scholar 

  25. T.L. Magnanti and R. Vachani, “A strong cutting plane algorithm for production scheduling with changeover costs,” working paper OR173-87, Operations Research Center (1989).

  26. T.L. Magnanti and R.T. Wong, “Network design and transportation planning: Models and algorithms,”Transportation Science 18 (1984) 1–56.

    Google Scholar 

  27. G.L. Nemhauser and L.A. Wolsey,Integer and Combinatorial Optimization (Wiley, New York, 1985).

    Google Scholar 

  28. M.W. Padberg and S. Hong, “On the symmetric traveling salesman problem: a computational study,”Mathematical Programming Study 12 (1980) 78–107.

    Google Scholar 

  29. M.W. Padberg and G. Rinaldi, “Optimization of a 532-city symmetric traveling salesman problem by branch and cut,”Operations Research Letters 6 (1987) 1–7.

    Google Scholar 

  30. S. Plotkin, D. Shmoys and E. Tardos, “Fast approximation algorithms for fractional packing and covering problems,” extended abstract.

  31. J. Shapiro,Mathematical Programming, Structures and Algorithms (Wiley, New York, 1979).

    Google Scholar 

  32. P. Vaidya, “Speeding-up linear programming using fast matrix multiplication,”Proceedings of the 30th Symposium on the Foundations of Computer Science (1989) 332–337.

  33. P. Vaidya, “A new algorithm for minimizing convex functions over convex sets,” AT&T Bell Laboratories (Murray Hill, NJ, 1990).

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Rights and permissions

Reprints and permissions

About this article

Cite this article

Bertsimas, D., Orlin, J.B. A technique for speeding up the solution of the Lagrangean dual. Mathematical Programming 63, 23–45 (1994). https://doi.org/10.1007/BF01582057

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01582057

Key words

Navigation