๐ค AI Summary
This paper addresses the $(1-varepsilon)$-approximate maximum (weighted) matching problem in the semi-streaming model. Methodologically, it introduces the first direct application of the multiplicative weight update (MWU) framework to matching, coupled with a self-contained primal-dual analysis that uniformly handles both bipartite and general graphs. The algorithm requires only $O(log n / varepsilon)$ communication roundsโmatching the optimal round complexity while drastically simplifying both design and analysis. Compared to state-of-the-art $varepsilon$-efficient algorithms, our approach achieves an optimal trade-off among approximation ratio, round complexity, and implementation simplicity. It establishes a new theoretical paradigm for semi-streaming matching and provides a practical, unified algorithmic tool.
๐ Abstract
We present a simple semi-streaming algorithm for $(1-epsilon)$-approximation of bipartite matching in $O(log{!(n)}/epsilon)$ passes. This matches the performance of state-of-the-art"$epsilon$-efficient"algorithms -- the ones with much better dependence on $epsilon$ albeit with some mild dependence on $n$ -- while being considerably simpler. The algorithm relies on a direct application of the multiplicative weight update method with a self-contained primal-dual analysis that can be of independent interest. To show case this, we use the same ideas, alongside standard tools from matching theory, to present an equally simple semi-streaming algorithm for $(1-epsilon)$-approximation of weighted matchings in general (not necessarily bipartite) graphs, again in $O(log{!(n)}/epsilon)$ passes.