The Complexity of Distributed Minimum Weight Cycle Approximation
Yi-Jun Chang, Yanyu Chen, Dipan Dey, Yonggang Jiang, Gopinath Mishra, Hung Thuan Nguyen, Mingyang Yang

TL;DR
This paper presents new distributed algorithms and lower bounds for approximating the minimum weight cycle problem, establishing nearly tight bounds for small-diameter graphs in the CONGEST model.
Contribution
It introduces a randomized $(k+1)$-approximation algorithm with a trade-off between approximation ratio and round complexity, and proves matching lower bounds under the Erdős girth conjecture.
Findings
Achieves a $(k+1)$-approximation with sublinear round complexity.
Proves lower bounds matching the upper bounds up to polylogarithmic factors.
Improves upon previous algorithms with better approximation and complexity trade-offs.
Abstract
We investigate the \emph{minimum weight cycle (MWC)} problem in the model of distributed computing. For undirected weighted graphs, we design a randomized algorithm that achieves a -approximation, for any \emph{real} number . The round complexity of algorithm is \[ \tilde{O}\!\Big( n^{\frac{k+1}{2k+1}} + n^{\frac{1}{k}} + D\, n^{\frac{1}{2(2k+1)}} + D^{\frac{2}{5}} n^{\frac{2}{5}+\frac{1}{2(2k+1)}} \Big). \] where denotes the number of nodes and is the unweighted diameter of the graph. This result yields a smooth trade-off between approximation ratio and round complexity. In particular, when and , the bound simplifies to \[ \tilde{O}\!\left( n^{\frac{k+1}{2k+1}} \right) \] On the lower bound side, assuming the Erd\H{o}s girth conjecture, we prove that for every \emph{integer} , any randomized…
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