Andreas Emil Feldmann , Anish Mukherjee , Erik Jan van Leeuwen
{"title":"The parameterized complexity of the survivable network design problem","authors":"Andreas Emil Feldmann , Anish Mukherjee , Erik Jan van Leeuwen","doi":"10.1016/j.jcss.2024.103604","DOIUrl":null,"url":null,"abstract":"<div><div>In the well-known <span>Survivable Network Design Problem (SNDP)</span>, we are given an undirected graph <em>G</em> with edge costs, a set <em>R</em> of terminal vertices, and an integer demand <span><math><msub><mrow><mi>d</mi></mrow><mrow><mi>s</mi><mo>,</mo><mi>t</mi></mrow></msub></math></span> for every terminal pair <span><math><mi>s</mi><mo>,</mo><mi>t</mi><mo>∈</mo><mi>R</mi></math></span>. The task is to compute a subgraph <em>H</em> of <em>G</em> of minimum cost, such that for every terminal pair <span><math><mi>s</mi><mo>,</mo><mi>t</mi><mo>∈</mo><mi>R</mi></math></span> there are at least <span><math><msub><mrow><mi>d</mi></mrow><mrow><mi>s</mi><mo>,</mo><mi>t</mi></mrow></msub></math></span> disjoint paths between <em>s</em> and <em>t</em> in <em>H</em>. 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 <span>EC-SNDP</span>, while if they must be internally vertex-disjoint we obtain <span>VC-SNDP</span>. Another important case is the element-connectivity variant (<span>LC-SNDP</span>), 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 <em>ℓ</em>, the sum of demands <em>D</em>, the number of terminals <em>k</em>, and the maximum demand <span><math><msub><mrow><mi>d</mi></mrow><mrow><mi>max</mi></mrow></msub></math></span>.</div></div>","PeriodicalId":50224,"journal":{"name":"Journal of Computer and System Sciences","volume":"148 ","pages":"Article 103604"},"PeriodicalIF":1.1000,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Computer and System Sciences","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0022000024000990","RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"BUSINESS, FINANCE","Score":null,"Total":0}
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 for every terminal pair . The task is to compute a subgraph H of G of minimum cost, such that for every terminal pair there are at least 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 ℓ, the sum of demands D, the number of terminals k, and the maximum demand .
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.