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New algorithms improve approximation for capacitated cluster editing

By PulseAugur Editorial · [1 sources] ·

Researchers have developed improved approximation algorithms for the capacitated cluster editing problem, which aims to construct disjoint cliques of bounded size from a signed graph to minimize disagreements. The new algorithms build upon existing methods for the uncapacitated version, achieving better approximation ratios. Specifically, one approach offers a downsizing procedure with a ratio of $2\alpha + 1$, while another hybrid method using LP sampling and pivoting shows expected costs of $\frac 95 L + \frac 1{10} \OPT_k$ or $\frac {306}{203} L + \frac {50}{203} \OPT_k$. These advancements lead to randomized approximation algorithms with ratios of $(1.9 + \epsilon)$ and $(1.7537 + \epsilon)$. AI

RANK_REASON Academic paper detailing new approximation algorithms for a graph theory problem. [lever_c_demoted from research: ic=1 ai=0.1]

Read on arXiv cs.LG →

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New algorithms improve approximation for capacitated cluster editing

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Academic paper detailing new approximation algorithms for a graph theory problem. [lever_c_demoted from research: ic=1 ai=0.1]
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COVERAGE [1]

  1. arXiv cs.LG TIER_1 English(EN) · Cong Ma, Guangting Chen, Yong Chen, Guohui Lin, Jiaxuan Ma, Lusheng Wang ·

    Improved approximation algorithms for capacitated cluster editing

    arXiv:2611.05916v1 Announce Type: cross Abstract: We study the minimum-disagreement cluster editing problem that, from a complete edge-signed graph, where a positive edge represents two vertices agreeing to each other while a negative represents a disagreement, constructs disjoin…

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