Improved asymptotic analysis of the average number of steps performed by the self-dual simplex algorithm

Abstract

In this paper we analyze the average number of steps performed by the self-dual simplex algorithm for linear programming, under the probabilistic model of spherical symmetry. The model was proposed by Smale. Consider a problem of n variables with m constraints. Smale established that for every number of constraints m, there is a constant c(m) such that the number of pivot steps of the self-dual algorithm, ρ(m, n), is less than c(m)(ln n)m(m+1). We improve upon this estimate by showing that ρ(m, n) is bounded by a function of m only. The symmetry of the function in m and n implies that ρ(m, n) is in fact bounded by a function of the smaller of m and n. © 1986 The Mathematical Programming Society, Inc.