We present a weighted approach to compute a maximum cardinality matching in an arbitrary bipartite graph. Our main result is a new algorithm that takes as input a weighted bipartite graph with edge weights of or . Let be an upper bound on the weight of any matching in . Consider the subgraph induced by all the edges of with a weight . Suppose every connected component in this subgraph has vertices and edges. We present an algorithm to compute a maximum cardinality matching in in $\tilde{}( m(\sqrt{w}+ \sqrt{r}+\frac{wr}{n}))$ time. When all the edge weights are (symmetrically when all weights are ), our algorithm will be identical to the well-known Hopcroft-Karp (HK) algorithm, which runs in time. However, if we can carefully assign weights of and on its edges such that both and are sub-linear in and for , then we can compute maximum cardinality matching in in time. Using our algorithm, we obtain a new time algorithm to compute an -approximate bottleneck matching of and an $\frac{1}{\varepsilon^{(d)}}n^{1+\frac{d-1}{2d-1}}\log n\varepsilon$-approximate bottleneck matching in -dimensions. All previous algorithms take time. Given any graph that has an easily computable balanced vertex separator for every subgraph of size , for , we can apply our algorithm to compute a maximum matching in time improving upon the time taken by the HK-Algorithm.

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