Partial Implementation of Max Flow and Min Cost Flow in Almost-Linear Time

Nithin Kavi
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Abstract

In 2022, Chen et al. proposed an algorithm in \cite{main} that solves the min cost flow problem in $m^{1 + o(1)} \log U \log C$ time, where $m$ is the number of edges in the graph, $U$ is an upper bound on capacities and $C$ is an upper bound on costs. However, as far as the authors of \cite{main} know, no one has implemented their algorithm to date. In this paper, we discuss implementations of several key portions of the algorithm given in \cite{main}, including the justifications for specific implementation choices. For the portions of the algorithm that we do not implement, we provide stubs. We then go through the entire algorithm and calculate the $m^{o(1)}$ term more precisely. Finally, we conclude with potential directions for future work in this area.
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在近线性时间内部分实现最大流量和最小成本流量
2022 年,Chen 等人在\cite{main}中提出了一种算法,可以在 $m^{1 + o(1)} \log U \log C$ 时间内解决最小成本流问题,其中 $m$ 是图中的边数,$U$ 是容量上限,$C$ 是成本上限。然而,据 \cite{main} 的作者所知,迄今为止还没有人实现过他们的算法。在本文中,我们将讨论 \cite{main}中给出的算法的几个关键部分的实现,包括具体实现选择的理由。对于我们没有实现的算法部分,我们将提供存根。然后,我们将回顾整个算法,并更精确地计算 $m^{o(1)}$ 项。最后,我们总结了这一领域未来工作的潜在方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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