Skip to main content

Pathways to the Optimal Set in Linear Programming

  • Chapter

Abstract

This chapter presents continuous paths leading to the set of optimal solutions of a linear programming problem. These paths are derived from the weighted logarithmic barrier function. The defining equations are bilinear and have some nice primal-dual symmetry properties. Extensions to the general linear complementarity problem are indicated.

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

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. E. R. Barnes, A variation on Karmarkar’s algorithm for solving linear programming problems, Research Report No. RC 11136, IBM T. J. Watson Research Center, Yorktown Heights, MY. (May 1985).

    Google Scholar 

  2. D. A. Bayer and J. C. Lagarias, The nonlinear geometry of linear programming I: Affine and projective rescaling trajectories, AT&T Preprint.

    Google Scholar 

  3. R. W. Cottle and G. B. Dantzig, Complementary pivot theory of mathematical programming, in Mathematics of Decision Sciences, G. B. Dantzig and A. F. Veinott, Jr. (eds.), American Mathematical Society, Providence, R.I., 1968, pp. 115–136.

    Google Scholar 

  4. G. B. Dantzig, Linear Programming and Extensions, Princeton University Press, Princeton, N.J., 1963.

    MATH  Google Scholar 

  5. B. C. Eaves and H. Scarf, The solution of systems of piecewise linear equations, Math. Operations Res. 1 (1976), 1–27.

    Article  MATH  MathSciNet  Google Scholar 

  6. A. V. Fiacco and G. P. McCormick, Nonlinear Programming: Sequential Unconstrained Minimization Techniques, Wiley, New York, 1968.

    MATH  Google Scholar 

  7. K. R. Frisch, The logarithmic potential method of convex programming, unpublished manuscript, University Institute of Economics, Oslo, Norway (1955).

    Google Scholar 

  8. C. B. Garcia and W. I. Zangwill, Pathways to Solutions, Fixed Points and Equilibria, Prentice-Hall, Englewood Cliffs, N.J., 1981.

    MATH  Google Scholar 

  9. 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, Technical Report SOL 85–11, Systems Optimization Laboratory, Department of Operations Research, Stanford University, Stanford, Calif. (July 1985).

    Google Scholar 

  10. H. M. Greenberg, and J. E. Kalan, Methods of feasible paths in nonlinear programming, Technical Report CP 72004, Computer Science/Operations Research Center, Southern Methodist University (February 1972).

    Google Scholar 

  11. P. Huard, Resolution of mathematical programming with nonlinear constraints by the method of centers, in Nonlinear Programming, J. Abadie (ed.), North-Holland, Amsterdam, 1967, pp. 207–219.

    Google Scholar 

  12. N. Karmarkar, A new polynomial-time algorithm for linear programming, Combinatorica 2 (1984), 373–395.

    Article  MathSciNet  Google Scholar 

  13. C. E. Lemke, Bimatrix equilibrium points and mathematical programming, Management Sci. 11 (1965), 681–689.

    Article  MathSciNet  Google Scholar 

  14. C. E. Lemke, On complementary pivot theory, in Mathematics of Decision Sciences, G. B. Dantzig and A. F. Veinott, Jr. (eds.), American Mathematical Society, Providence, R.I., 1968, pp. 95–114.

    Google Scholar 

  15. O. Mangasarian, Normal solutions of linear programs, Math. Programming Study 22 (1984), 206–216.

    MATH  MathSciNet  Google Scholar 

  16. N. Megiddo and M. Shub, Boundary behavior of interior point algorithms for linear programming, Math. Operations Res. 13 (1988), to appear.

    Google Scholar 

  17. K. G. Murty, A new interior variant of the gradient projection method for linear programming, Technical Paper 85–18, Department of Industrial and Operations Engineering, University of Michigan, Ann Arbor (May 1985).

    Google Scholar 

  18. J. L. Nazareth, Homotopies in linear programming, Algorithmica 1 (1986), 529–536.

    Article  MATH  MathSciNet  Google Scholar 

  19. H. E. Scarf, The approximation of fixed points of continuous mappings, SIAM. J. Appl. Math. 15 (1967), 1328–1343.

    Article  MATH  MathSciNet  Google Scholar 

  20. S. Smale, Talk at Mathematical Sciences Research Institute (MSRI), Berkeley, California (January 1986).

    Google Scholar 

  21. R. J. Vanderbei, M. J. Meketon, and B. A. Freedman, A modification of Karmarkar’s linear programming algorithm, Algorithmica 1 (1986), 395–409.

    Article  MATH  MathSciNet  Google Scholar 

Download references

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 1989 Springer-Verlag New York Inc.

About this chapter

Cite this chapter

Megiddo, N. (1989). Pathways to the Optimal Set in Linear Programming. In: Megiddo, N. (eds) Progress in Mathematical Programming. Springer, New York, NY. https://doi.org/10.1007/978-1-4613-9617-8_8

Download citation

  • DOI: https://doi.org/10.1007/978-1-4613-9617-8_8

  • Publisher Name: Springer, New York, NY

  • Print ISBN: 978-1-4613-9619-2

  • Online ISBN: 978-1-4613-9617-8

  • eBook Packages: Springer Book Archive

Publish with us

Policies and ethics