Randomized Greedy Algorithms for the Maximum Matching Problem with New Analysis

Matthias Poloczek, Mario Szegedy

Abstract: It is a long-standing problem to lower bound the performance of randomized greedy algorithms for maximum matching. Aronson, Dyer, Frieze and Suen [1]studied the modified randomized greedy (MRG) algorithm and proved that it approximates the maximum matching within a factor of at least 1/2 + 1/400,000. They use heavy combinatorial methods in their analysis. We introduce a new technique we call Contrast Analysis, and show a 1/2 + 1/256 performance lower bound for the MRG algorithm. The technique seems to be useful not only for the MRG, but also for other related algorithms.