The simplex algorithm in dimension three

We investigate the worst-case behavior of the simplex algorithm on linear programs with three variables, that is, on 3-dimensional simple polytopes. Among the pivot rules that we consider, the "random edge" rule yields the best asymptotic behavior as well as the most complicated analysis. All other rules turn out to be much easier to study, but also produce worse results: Most of them show essentially worst-possible behavior; this includes both Kalai's "random-facet" rule, which without dimension restriction is known to be subexponential, and Zadeh's deterministic history-dependent rule, for which no nonpolynomial instances in general dimensions have been found so far.