An Improved Approximation Algorithm for Metric Triangle Packing
Jingyang Zhao, Mingyu Xiao
TL;DR
The paper tackles the Maximum Weight Metric Triangle Packing problem on edge-weighted metric complete graphs and achieves a deterministic $\,0.66835-\varepsilon$-approximation by combining three independently constructed triangle packings ($T_1$, $T_2$, $T_3$) and derandomizing the framework via conditional expectations. The core methods include a short cycle packing to guide the first packing, a maximum-weight matching-based second packing, and a novel randomized third packing that can be derandomized; a linear-programming trade-off solidifies the final approximation ratio. This work improves the previous best deterministic and randomized results, providing a practical polynomial-time algorithm with a tight analysis that surpasses the natural $2/3$ barrier. The approach has potential implications for related packing problems and motivates further exploration of deterministic techniques in cycle- and path-based packings.
Abstract
Given an edge-weighted metric complete graph with $n$ vertices, the maximum weight metric triangle packing problem is to find a set of $n/3$ vertex-disjoint triangles with the total weight of all triangles in the packing maximized. Several simple methods can lead to a 2/3-approximation ratio. However, this barrier is not easy to break. Chen et al. proposed a randomized approximation algorithm with an expected ratio of $(0.66768-\varepsilon)$ for any constant $\varepsilon>0$. In this paper, we improve the approximation ratio to $(0.66835-\varepsilon)$. Furthermore, we can derandomize our algorithm.
