Fractional Linear Matroid Matching is in quasi-NC

Rohit Gurjar, Taihei Oki, Roshan Raj
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Abstract

The matching and linear matroid intersection problems are solvable in quasi-NC, meaning that there exist deterministic algorithms that run in polylogarithmic time and use quasi-polynomially many parallel processors. However, such a parallel algorithm is unknown for linear matroid matching, which generalizes both of these problems. In this work, we propose a quasi-NC algorithm for fractional linear matroid matching, which is a relaxation of linear matroid matching and commonly generalizes fractional matching and linear matroid intersection. Our algorithm builds upon the connection of fractional matroid matching to non-commutative Edmonds' problem recently revealed by Oki and Soma~(2023). As a corollary, we also solve black-box non-commutative Edmonds' problem with rank-two skew-symmetric coefficients.
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分数线性 Matroid 匹配处于准 NC 状态
匹配问题和线性矩阵相交问题在准数控中是可以求解的,这意味着存在运行时间为多对数的确定性算法,并且可以使用准对数数量的并行处理器。然而,这种并行算法对于线性矩阵匹配问题还不得而知,而线性矩阵匹配问题是这两个问题的一般化。在这项工作中,我们提出了分数线性矩阵匹配的准数控算法,这是线性矩阵匹配的一种松弛,通常是对分数匹配和线性矩阵相交的概括。我们的算法建立在 Oki 和 Soma~(2023) 最近揭示的分数矩阵匹配与非交换 Edmonds 问题的联系之上。作为推论,我们还解决了具有秩二倾斜对称系数的黑箱非交换埃德蒙兹问题。
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