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

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arxiv 2407.10034 v1 pith:ILXDLBMD submitted 2024-07-14 cs.DS cs.DM

classification cs.DScs.DM
keywords algorithmciteflowmainboundcostimplementationportions
verification ladder T0 review T1 audit T2 compute T3 formal
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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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  1. VLAff: Vision-Language-Affordance Model for Unified Actionable Affordances

    cs.RO 2026-08 conditional novelty 6.0 of 10

    From 204K egocentric human videos, the authors automatically extract visual, grasp, and trajectory affordances and train one vision-language model, VLAff, that predicts all three for robot manipulation.

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