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arXiv 2407.04447cs.DScs.DM
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增量式带奖励收集斯坦纳树问题的双准则近似

Bicriterial Approximation for the Incremental Prize-Collecting Steiner-Tree Problem

Yann Disser, Svenja M. Griesbach, Max Klimm, Annette Lutz

AI总结:

本文研究预算增长的增量式带奖励收集斯坦纳树问题,提出双准则近似框架,并针对树图与一般图分别给出密度贪心算法和容量缩放算法,证明其近似比与根离心率或路径长度相关。

摘要:

我们考虑带根奖励收集斯坦纳树问题的一个增量变体,其中预算约束不断增长。虽然不存在能够同时对所有预算近似最优解的增量解,但我们证明双准则 $(α,μ)$-近似是可能的,即对于所有 $B \in \mathbb{R}_{\geq 0}$,一个以预算 $B+α$ 得到的解与预算为 $B$ 的最优解相比,是一个乘性 $μ$-近似。对于底层图为树的情形,我们提出一个多项式时间的密度贪心算法,它计算出一个 $(χ,1)$-近似,其中 $χ$ 表示底层图中根顶点的离心率,并且证明这是最优可能的。将密度贪心算法适配到一般图上时,它是 $(γ,2)$-竞争的,其中 $γ$ 是从根出发的顶点不相交路径的最大长度。虽然该算法不是多项式时间运行的,但可以将其适配为一个在多项式时间内运行的 $(γ,3)$-竞争算法。我们进一步设计了一个容量缩放算法,它保证 $(3χ,8)$-近似,并且更一般地,对于每个固定的 $\ell \in \mathbb{N}$,保证 $\smash{\bigl((4\ell - 1)χ, \frac{2^{\ell + 2}}{2^{\ell}-1}\bigr)}$-近似。

英文摘要:

We consider an incremental variant of the rooted prize-collecting Steiner-tree problem with a growing budget constraint. While no incremental solution exists that simultaneously approximates the optimum for all budgets, we show that a bicriterial $(α,μ)$-approximation is possible, i.e., a solution that with budget $B+α$ for all $B \in \mathbb{R}_{\geq 0}$ is a multiplicative $μ$-approximation compared to the optimum solution with budget $B$. For the case that the underlying graph is a tree, we present a polynomial-time density-greedy algorithm that computes a $(χ,1)$-approximation, where $χ$ denotes the eccentricity of the root vertex in the underlying graph, and show that this is best possible. An adaptation of the density-greedy algorithm for general graphs is $(γ,2)$-competitive where $γ$ is the maximal length of a vertex-disjoint path starting in the root. While this algorithm does not run in polynomial time, it can be adapted to a $(γ,3)$-competitive algorithm that runs in polynomial time. We further devise a capacity-scaling algorithm that guarantees a $(3χ,8)$-approximation and, more generally, a $\smash{\bigl((4\ell - 1)χ, \frac{2^{\ell + 2}}{2^{\ell}-1}\bigr)}$-approximation for every fixed $\ell \in \mathbb{N}$.

机构、总结、中文摘要均由AI分析而来,可能存在错误,请以论文正文为准。

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