Skip to main content
Log in

Exploiting special structure in Karmarkar's linear programming algorithm

  • Published:
Mathematical Programming Submit manuscript

Abstract

We propose methods to take advantage of specially-structured constraints in a variant of Karmarkar's projective algorithm for standard form linear programming problems. We can use these constraints to generate improved bounds on the optimal value of the problem and also to compute the necessary projections more efficiently, while maintaining the theoretical bound on the algorithm's performance. It is shown how various upper-bounding constraints can be handled implicitly in this way. Unfortunately, the situation for network constraints appears less favorable.

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. K.M. Anstreicher, “A monotonic projective algorithm for fractional linear programming,”Algorithmica 1 (1986) 483–498.

    Google Scholar 

  2. E.R. Barnes, “A variation on Karmarkar's algorithm for solving linear programming problems,”Mathematical Programming 36 (1986) 174–182.

    Google Scholar 

  3. V. Chandru and B. Kochar, “A class of algorithms for linear programming,” manuscript, Department of Industrial Engineering, Purdue University, West Lafayette, IN (1985).

    Google Scholar 

  4. G.B. Dantzig,Linear programming and extensions (Princeton University Press, Princeton, NJ, 1963).

    Google Scholar 

  5. G.B. Dantzig, M.A.H. Dempster, and M.J. Kallio, eds.,Large scale linear programming (IIASA, Laxenburg, Austria, 1981).

    Google Scholar 

  6. G.B. Dantzig and R.M. Van Slyke, “Generalized upper bounding techniques,”Journal of Computer System Sciences 1(1967) 213–226.

    Google Scholar 

  7. G.B. Dantzig and P. Wolfe, “The decomposition algorithm for linear programming,”Econometrica 29 (1961) 767–778.

    Google Scholar 

  8. G. de Ghellinck and J.-Ph. Vial, “A polynomial Newton method for linear programming,”Algorithmica 1 (1986) 425–453.

    Google Scholar 

  9. D. Gay, “A variant of Karmarkar's linear programming algorithm for problems in standard form“,Mathematical Programming 37 (1987) 81–90.

    Google Scholar 

  10. P.E. Gill, W. Murray, M.A. Saunders, J.A. Tomlin and M.H. Wright, “On projected Newton barrier methods for linear programming and an equivalence to Karmarkar's projective method,”Mathematical Programming 36 (1986) 183–209.

    Google Scholar 

  11. C. Gonzaga, “A conical projection algorithm for linear programming,” manuscript, Department of Electrical Engineering and Computer Science, University of California, Berkeley, CA (1985).

    Google Scholar 

  12. G.W. Graves and R.D. McBride, “The factorization approach to large-scale linear programming,”Mathematical Programming 20 (1976) 91–110.

    Google Scholar 

  13. D. Jensen and A. Steger, private communication, Department of Applied Mathematics and Statistics, State University of New York at Stonybrook, Stonybrook, New York (1985).

  14. S. Kapoor and P.M. Vaidya, “Fast algorithms for convex quadratic programming and multicommodity flows,” Proceedings of the 18th ACM Symposium on Theory of Computing (1986) 147–159.

  15. N. Karmarkar, “A new polynomial time algorithm for linear programming,”Combinatorica 4 (1984) 373–395.

    Google Scholar 

  16. N. Karmarkar and L.P. Sinha, “Application of Karmarkar's algorithm to overseas telecommunications facilities planning,” paper presented at XII International Symposium on Mathematical Programming, Boston (1985).

  17. L.S. Lasdon,Optimization theory for large systems (Macmillan, New York, 1970).

    Google Scholar 

  18. B.A. Murtagh,Advanced linear programming: computation and practice (McGraw-Hill, New York, 1981).

    Google Scholar 

  19. G. Rinaldi, “A projective method for linear programming with box-type constraints,”Algorithmica 1 (1986) 517–527.

    Google Scholar 

  20. L. Schrage, “Implicit representation of variable upper bounds in linear programming,”Mathematical Programming Study 14 (1975) 118–132.

    Google Scholar 

  21. L. Schrage, “Implicit representation of generalized variable upper bounds in linear programming,”Mathematical Programming 14 (1978) 11–20.

    Google Scholar 

  22. A. Steger, “An extension of Karmarkar's algorithm for bounded linear programming problems,” M.S. Thesis, SUNY at Stonybrook, New York (1985).

    Google Scholar 

  23. M.J. Todd, “An implementation of the simplex method for linear programming problems with variable upper bounds,”Mathematical Programming 23 (1982) 34–49.

    Google Scholar 

  24. M.J. Todd, “Improved bounds and containing ellipsoids in Karmarkar's linear programming algorithm,” to appear inMathematics of Operations Research.

  25. M.J. Todd and B.P. Burrell, “An extension of Karmarkar's algorithm for linear programming using dual variables,”Algorithmica 1 (1986) 409–424.

    Google Scholar 

  26. R.J. Vanderbei, M.S. Meketon and B.A. Freedman, “A modification of Karmarkar's linear programming algorithm,”Algorithmica 1 (1986) 395–407.

    Google Scholar 

  27. Y. Ye and M. Kojima, “Recovering optimal dual solutions in Karmarkar's polynomial algorithm for linear programming,” to appear inMathematical Programming.

Download references

Author information

Authors and Affiliations

Authors

Additional information

Research supported in part by National Science Foundation Grant ECS-8602534, ONR Contract N00014-87-K-0212 and the US Army Research Office through the Mathematical Sciences Institute of Cornell University.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Todd, M.J. Exploiting special structure in Karmarkar's linear programming algorithm. Mathematical Programming 41, 97–113 (1988). https://doi.org/10.1007/BF01580755

Download citation

  • Received:

  • Revised:

  • Issue Date:

  • DOI: https://doi.org/10.1007/BF01580755

Key words

Navigation