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Computational Practice in Mathematical Programming pp 12–25Cite as

Partially normalized pivot selection in linear programming

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Part of the book series: Mathematical Programming Studies ((MATHPROGRAMM,volume 4))

Abstract

An algorithm is presented for pivot selection in the simplex method using the partial norm of the updated column to dynamically scale the reduced costs. The geometry of this partial norm method is compared with several known methods including the usual simplex pivot selection rule and Harris’ Devex procedure. Computational comparisons between the partial norm method and other methods show our algorithm to be a surprisingly effective procedure. We show the partial norm procedure to be amenable to simplex routines using multiple pricing.

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References

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Author information

Authors and Affiliations

  1. IBM Thomas J. Watson Research Center, Yorktown Heights, New York, U.S.A

    Harlan Crowder

  2. Potchefstroom University, Transvaal, Republic of South Africa

    J.M. Hattingh

Authors
  1. Harlan Crowder
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  2. J.M. Hattingh
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Editor information

M. L. Balinski Eli Hellerman

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© 1975 The Mathematical Programming Society

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Crowder, H., Hattingh, J. (1975). Partially normalized pivot selection in linear programming. In: Balinski, M.L., Hellerman, E. (eds) Computational Practice in Mathematical Programming. Mathematical Programming Studies, vol 4. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0120708

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  • DOI: https://doi.org/10.1007/BFb0120708

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-00765-1

  • Online ISBN: 978-3-642-00766-8

  • eBook Packages: Springer Book Archive

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