arXivDaily arXiv每日学术速递 周一至周五更新
arXiv 1908.11491cs.DS
收藏

最小标签s-t切割具有较大的完整性间隙

Minimum Label s-t Cut has Large Integrality Gaps

Peng Zhang, Linqing Tang

摘要:

给定图G=(V,E),其标号集L = {l_1,l_2,.,l_q},其中每个边具有来自L的标签、V中的源s和V中的宿t,最小标签s-t切割问题要求挑选具有最小基数的标签的集合L'子集L,使得具有来自G的L'中的标签的所有边的移除断开s和t。这个问题来自于现实世界中的许多应用,例如信息安全和计算机网络。本文研究了最小标号s-t割的两个线性规划,证明了这两个线性规划都有大的积分间隙,即Omega(m)和Omega(m^{1/3-n}),其中m是图的边数,n> 0是任意小的常数.由于最小标号s-t割是NP-难问题,而线性规划技术是设计近似算法的主要途径,因此我们的结果对单纯依靠线性规划设计更好的最小标号s-t割近似算法的希望给出了否定的回答.

英文摘要:

Given a graph G=(V,E) with a label set L = {l_1, l_2, ..., l_q}, in which each edge has a label from L, a source s in V, and a sink t in V, the Min Label s-t Cut problem asks to pick a set L' subseteq L of labels with minimized cardinality, such that the removal of all edges with labels in L' from G disconnects s and t. This problem comes from many applications in real world, for example, information security and computer networks. In this paper, we study two linear programs for Min Label s-t Cut, proving that both of them have large integrality gaps, namely, Omega(m) and Omega(m^{1/3-epsilon}) for the respective linear programs, where m is the number of edges in the graph and epsilon > 0 is any arbitrarily small constant. As Min Label s-t Cut is NP-hard and the linear programming technique is a main approach to design approximation algorithms, our results give negative answer to the hope that designs better approximation algorithms for Min Label s-t Cut that purely rely on linear programming.

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。

↑