A randomized polynomial-time simplex algorithm for linear programming

We present the first randomized polynomial-time simplex algorithm for linear programming. Like the other known polynomial-time algorithms for linear programming, its running time depends polynomially on the number of bits used to represent its input. We begin by reducing the input linear program to a special form in which we merely need to certify boundedness. As boundedness does not depend upon the right-hand-side vector, we run the shadow-vertex simplex method with a random right-hand-side vector. Thus, we do not need to bound the diameter of the original polytope. Our analysis rests on a geometric statement of independent interest: given a polytope Ax b in isotropic position, if one makes a polynomially small perturbation to b then the number of edges of the projection of the perturbed polytope onto a random 2-dimensional subspace is expected to be polynomial.

• Type: Conference
• Year: 2006
• Where: STOC
• Authors: Jonathan A. Kelner, Daniel A. Spielman
• Keywords: Algorithms, Known Polynomial-time Algorithms, Random 2-dimensional Subspace, Shadow-vertex Simplex Method, STOC 2006