In this paper, we study network flow algorithms for bipartite networks. A network G = (V; E) is called bipartite if its vertex set V can be partitioned into two subsets V 1 and V 2 such that all edges have one endpoint in V 1 and the other in V 2 . Let n = jV j, n 1 = jV 1 j, n 2 = jV 2 j, m = jEj and assume without loss of generality that n 1 n 2 . We call a bipartite network unbalanced if n 1 ø n 2 and balanced otherwise. (This notion is necessarily imprecise.) We show that several maximum flow algorithms can be substantially sped up when applied to unbalanced networks. The basic idea in these improvements is a two-edge push rule that allows us to "charge" most computation to vertices in V 1 , and hence develop algorithms whose running times depend on n 1 rather than n. For example, we show that the two-edge push version of Goldberg and Tarjan's FIFO preflow push algorithm runs in O(n 1 m + n 3 1 ) time and that the analogous version of Ahuja and Orlin's excess scaling algori...

Identifying the teams that are already eliminated from contention for first place of a sports league, is a classic problem that has been widely used to illustrate the application of linear programming and network flow. In the classic setting each game is played between two teams and the first place goes to the team with the greatest total wins. Recently, two papers [Way] and [AEHO] detailed a surprising structural fact in the classic setting: At any point in the season, there is a computable threshold W such that a team is eliminated (has no chance to win or tie for first place) if and only if it cannot win W or more games. They used this threshold to speed up the identification of eliminated teams. In both papers, the proofs of the existence the threshold are tied to the computational methods used to find it. In this paper we show that thresholds exist for a wide range of elimination problems (greatly generalizing the classical setting), via a simpler proof not connected to any partic...