Combinatorial Linear Programming: Geometry Can Help

  • Authors:
  • Bernd Gärtner

  • Affiliations:
  • -

  • Venue:
  • RANDOM '98 Proceedings of the Second International Workshop on Randomization and Approximation Techniques in Computer Science
  • Year:
  • 1998

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Abstract

We consider a class A of generalized linear programs on the d-cube (due to Matoušek) and prove that Kalai's subexponential simplex algorithm Random-Facet is polynomial on all actual linear programs in the class. In contrast, the subexponential analysis is known to be best possible for general instances in A. Thus, we identify a "geometric" property of linear programming that goes beyond all abstract notions previously employed in generalized linear programming frameworks, and that can be exploited by the simplex method in a nontrivial setting.