arXivDaily arXiv每日学术速递 周一至周五更新
arXiv周末暂无论文更新,休息一下吧,周末愉快~~
arXiv 1205.0144cs.DS
收藏

基于稠密子图的无处稀疏生成子

Everywhere-Sparse Spanners via Dense Subgraphs

Eden Chlamtac, Michael Dinitz, Robert Krauthgamer

摘要:

在构造稀疏(少量边)或轻(低总权重)的图空间方面的重大进展已经跳过了到处稀疏(最大度小)的空间。这种差异与其他网络设计问题是一致的,其中最大程度的目标一直是一个臭名昭著的技术挑战。我们的主要结果是最低度2-生成器(LD 2S)问题,其中的目标是计算输入图的2-向量,以最小化最大度。我们设计了一个多项式时间的算法,实现近似因子$\tilde O(Δ^{3-2\sqrt{2}}) \approx \tilde O(Δ^{0.172})$,其中$Δ$是输入图的最大程度。之前的$\tilde O(Δ^{1/4})$ -近似在近20年前由Kortsarz和Peleg证明[SODA 1994,SICOMP 1998]。我们的主要概念贡献是在LD 2S和最密k子图(DkS)问题的变体之间建立正式联系。具体而言,我们设计这两个问题的强松弛的基础上的Sherali-Adams线性规划(LP)的层次结构,并表明,“忠实”的DKS-变量的随机舍入可以用来圆LD 2S的解决方案。我们的忠实性概念直观地意味着所有的顶点和边都是以与它们的LP值成比例的概率被选择的,但精确的公式更微妙。不幸的是,DkS已知的最好算法以不忠实的方式使用Lovász-Schrijver LP层次结构[Bhaskara,Charikar,Chlamtac,Feige和Vijayaraghavan,STOC 2010]。我们的主要技术贡献是克服这个缺点,同时仍然匹配的差距,出现在随机图中种植一个子图具有相同的对数密度。

英文摘要:

The significant progress in constructing graph spanners that are sparse (small number of edges) or light (low total weight) has skipped spanners that are everywhere-sparse (small maximum degree). This disparity is in line with other network design problems, where the maximum-degree objective has been a notorious technical challenge. Our main result is for the Lowest Degree 2-Spanner (LD2S) problem, where the goal is to compute a 2-spanner of an input graph so as to minimize the maximum degree. We design a polynomial-time algorithm achieving approximation factor $\tilde O(Δ^{3-2\sqrt{2}}) \approx \tilde O(Δ^{0.172})$, where $Δ$ is the maximum degree of the input graph. The previous $\tilde O(Δ^{1/4})$ -approximation was proved nearly two decades ago by Kortsarz and Peleg [SODA 1994, SICOMP 1998]. Our main conceptual contribution is to establish a formal connection between LD2S and a variant of the Densest k-Subgraph (DkS) problem. Specifically, we design for both problems strong relaxations based on the Sherali-Adams linear programming (LP) hierarchy, and show that "faithful" randomized rounding of the DkS-variant can be used to round LD2S solutions. Our notion of faithfulness intuitively means that all vertices and edges are chosen with probability proportional to their LP value, but the precise formulation is more subtle. Unfortunately, the best algorithms known for DkS use the Lovász-Schrijver LP hierarchy in a non-faithful way [Bhaskara, Charikar, Chlamtac, Feige, and Vijayaraghavan, STOC 2010]. Our main technical contribution is to overcome this shortcoming, while still matching the gap that arises in random graphs by planting a subgraph with same log-density.

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。

↑