Tighter Bounds for the Randomized Polynomial-Time Simplex Algorithm for Linear Programming
By: Daniel Gibor
Potential Business Impact:
Makes computers solve hard math problems faster.
In this paper, we present a randomized polynomial-time simplex algorithm with higher probability and tighter bounds for linear programming by applying improved quasi-convex properties, a logarithmic rounding on a given polytope and its logarithmic perturbation. We base our work on the first randomized polynomial-time simplex method by Jonathan A. Kelner and Daniel A. Spielman [KS06].
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