On the Number of Iterations for Dantzig-Wolfe Optimization and Packing-Covering Approximation Algorithms

Abstract

We give a lower bound on the iteration complexity of a natural class of Lagrangean-relaxation algorithms for approximately solving packing/covering linear programs. We show that, given an input with $m$ random 0/1-constraints on $n$ variables, with high probability, any such algorithm requires $Ω(ρ\log(m)/ε^2)$ iterations to compute a $(1+ε)$-approximate solution, where $ρ$ is the width of the input. The bound is tight for a range of the parameters $(m,n,ρ,ε)$. The algorithms in the class include Dantzig-Wolfe decomposition, Benders' decomposition, Lagrangean relaxation as developed by Held and Karp [1971] for lower-bounding TSP, and many others (e.g. by Plotkin, Shmoys, and Tardos [1988] and Grigoriadis and Khachiyan [1996]). To prove the bound, we use a discrepancy argument to show an analogous lower bound on the support size of $(1+ε)$-approximate mixed strategies for random two-player zero-sum 0/1-matrix games.