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On the 100% rule of sensitivity analysis in linear programming

  • Session 14: Mathematical Programming and Genetic Algorithms
  • Conference paper
  • First Online:
Computing and Combinatorics (COCOON 1997)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 1276))

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Abstract

The 100% Rule was introduced by Bradley, Hax and Magnanti [1] in Sensitivity Analysis of linear programming theory. It is concerned with the qualitative behavior of an optimal solution as it changes according to the right hand side vector. We extend this 100% Rule to the most generalized form, to deal with changes in all the parameters of any given linear program, including the coefficients of the matrix, the objective function as well as the right hand side vector.

Research supported in part by NSF grants CCR-9057486 and CCR-9319093, and by an Alfred P. Sloan Fellowship.

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References

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Authors and Affiliations

  1. Department of Computer Science, SUNY at Buffalo, 14260, Buffalo, NY, USA

    Pu Cai & Jin-Yi Cai

Authors
  1. Pu Cai
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  2. Jin-Yi Cai
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Editor information

Tao Jiang D. T. Lee

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© 1997 Springer-Verlag Berlin Heidelberg

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Cai, P., Cai, JY. (1997). On the 100% rule of sensitivity analysis in linear programming. In: Jiang, T., Lee, D.T. (eds) Computing and Combinatorics. COCOON 1997. Lecture Notes in Computer Science, vol 1276. Springer, Berlin, Heidelberg. https://doi.org/10.1007/BFb0045113

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  • DOI: https://doi.org/10.1007/BFb0045113

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  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-63357-0

  • Online ISBN: 978-3-540-69522-6

  • eBook Packages: Springer Book Archive

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