The parameterized complexity of the survivable network design problem

IF 1.1 3区 计算机科学 Q1 BUSINESS, FINANCE
Andreas Emil Feldmann , Anish Mukherjee , Erik Jan van Leeuwen
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引用次数: 0

Abstract

In the well-known Survivable Network Design Problem (SNDP), we are given an undirected graph G with edge costs, a set R of terminal vertices, and an integer demand ds,t for every terminal pair s,tR. The task is to compute a subgraph H of G of minimum cost, such that for every terminal pair s,tR there are at least ds,t disjoint paths between s and t in H. Depending on the type of disjointness, we obtain several variants of SNDP that have been widely studied in the literature: if the paths are required to be edge-disjoint we obtain EC-SNDP, while if they must be internally vertex-disjoint we obtain VC-SNDP. Another important case is the element-connectivity variant (LC-SNDP), where the paths must be disjoint on edges and non-terminals, i.e., they may only share terminals. In this work we shed light on the parameterized complexity of the above problems. We consider several natural parameters, which include the solution size , the sum of demands D, the number of terminals k, and the maximum demand dmax.
可生存网络设计问题的参数化复杂性
在著名的 "可存活网络设计问题"(SNDP)中,我们给定了一个带边成本的无向图 G、一组终端顶点 R 以及每个终端对 s,t∈R 的整数需求 ds,t。我们的任务是计算代价最小的 G 子图 H,使得对于每个终端对 s,t∈R,H 中的 s 和 t 之间至少有 ds,t 互不相交的路径。根据互不相交的类型,我们会得到 SNDP 的几种变体,这些变体已在文献中得到广泛研究:如果路径必须是边互不相交,我们会得到 EC-SNDP;如果路径必须是内部顶点互不相交,我们会得到 VC-SNDP。另一种重要情况是元素连通性变体(LC-SNDP),即路径必须在边和非终端上不相交,也就是说,它们只能共享终端。在这项工作中,我们将阐明上述问题的参数化复杂性。我们考虑了几个自然参数,包括解大小 ℓ、需求总和 D、终端数 k 和最大需求量 dmax。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of Computer and System Sciences
Journal of Computer and System Sciences 工程技术-计算机:理论方法
CiteScore
3.70
自引率
0.00%
发文量
58
审稿时长
68 days
期刊介绍: The Journal of Computer and System Sciences publishes original research papers in computer science and related subjects in system science, with attention to the relevant mathematical theory. Applications-oriented papers may also be accepted and they are expected to contain deep analytic evaluation of the proposed solutions. Research areas include traditional subjects such as: • Theory of algorithms and computability • Formal languages • Automata theory Contemporary subjects such as: • Complexity theory • Algorithmic Complexity • Parallel & distributed computing • Computer networks • Neural networks • Computational learning theory • Database theory & practice • Computer modeling of complex systems • Security and Privacy.
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