We present a deterministic $(1+\varepsilon)$-approximate maximum matching algorithm in $\mathsf{poly}(1/\varepsilon)$ passes in the semi-streaming model, solving the long-standing open problem of breaking the exponential barrier in the dependence on $1/\varepsilon$. Our algorithm exponentially improves on the well-known randomized $(1/\varepsilon)^{O(1/\varepsilon)}$-pass algorithm from the seminal work by McGregor [APPROX05], the recent deterministic algorithm by Tirodkar with the same pass complexity [FSTTCS18], as well as the deterministic $\log n \cdot \mathsf{poly}(1/\varepsilon)$-pass algorithm by Ahn and Guha [ICALP11].
翻译:我们在半流模式中以$\mathsf{poly}(1/\\varepsilon) 提供一种确定性$(1 ⁇ varepsilon) $- 近似最大匹配算法, 解决长期存在的在依赖$/\varepsilon美元时打破指数障碍的开放问题。 我们的算法在McGregor[APPROX05] 的原始工程中以美元( 1/\ varepsilon) =( 1/\ varepsilon) $( $- passal) 的随机算法上取得了指数性进步。 Tirodkar 最近的确定性算法具有同样的通过复杂性 [FSTTCS18] 以及Ahn 和 Guha [CricP11] 的确定性 。