The bipartite matching problem in the online and streaming settings has received a lot of attention recently. The classical vertex arrival setting, for which the celebrated Karp, Vazirani and Vazirani (KVV) algorithm achieves a $1-1/e$ approximation, is rather well understood: the $1-1/e$ approximation is optimal in both the online and semi-streaming setting, where the algorithm is constrained to use $n\cdot \log^{O(1)} n$ space. The more challenging the edge arrival model has seen significant progress recently in the online algorithms literature. For the strictly online model (no preemption) approximations better than trivial factor $1/2$ have been ruled out [Gamlath et al'FOCS'19]. For the less restrictive online preemptive model a better than $\frac1{1+\ln 2}$-approximation [Epstein et al'STACS'12] and even a better than $(2-\sqrt{2})$-approximation[Huang et al'SODA'19] have been ruled out. The recent hardness results for online preemptive matching in the edge arrival model are based on the idea of stringing together multiple copies of a KVV hard instance using edge arrivals. In this paper, we show how to implement such constructions using ideas developed in the literature on Ruzsa-Szemer\'edi graphs. As a result, we show that any single pass streaming algorithm that approximates the maximum matching in a bipartite graph with $n$ vertices to a factor better than $\frac1{1+\ln 2}\approx 0.59$ requires $n^{1+\Omega(1/\log\log n)}\gg n \log^{O(1)} n$ space. This gives the first separation between the classical one sided vertex arrival setting and the edge arrival setting in the semi-streaming model.
翻译:在线和流中设置的双面匹配问题最近受到了很多关注。 典型的顶端抵达设置, 值得庆祝的Karp、 Vazirani 和 Vazirani (KVVV) 算法实现了1-1/ 美元近似值, 人们相当理解: 1-1/ 美元近似值在在线和半流设置中是最佳的, 该算法限制使用 $n\ cdot 和 al- STACS'12] 空间。 更具有挑战性的顶端抵达模式在在线算法文献中取得了显著的进展。 对于严格的在线模型( no prevention) 近似于小因数 1/2美元。 对于不那么限制性的在线先发制模型来说, ngroc1 美元是最佳的, 这个算法是先行和 irecialalal 运算中最高级的硬性结果 。