{"title":"Approximating the Held-Karp Bound for Metric TSP in Nearly-Linear Time","authors":"C. Chekuri, Kent Quanrud","doi":"10.1109/FOCS.2017.78","DOIUrl":null,"url":null,"abstract":"We give a nearly linear-time randomized approximation scheme for the Held-Karp bound [22] for Metric-TSP. Formally, given an undirected edge-weighted graph G = (V,E) on m edges and ε 0, the algorithm outputs in O(m log^4 n/ε^2) time, with high probability, a (1 + ε)-approximation to the Held-Karp bound on the Metric-TSP instance induced by the shortest path metric on G. The algorithm can also be used to output a corresponding solution to the Subtour Elimination LP. We substantially improve upon the O(m^2 log^2(m)/ε^2) running time achieved previously by Garg and Khandekar.","PeriodicalId":311592,"journal":{"name":"2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)","volume":"53 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-02-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"20","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 IEEE 58th Annual Symposium on Foundations of Computer Science (FOCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/FOCS.2017.78","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 20
Abstract
We give a nearly linear-time randomized approximation scheme for the Held-Karp bound [22] for Metric-TSP. Formally, given an undirected edge-weighted graph G = (V,E) on m edges and ε 0, the algorithm outputs in O(m log^4 n/ε^2) time, with high probability, a (1 + ε)-approximation to the Held-Karp bound on the Metric-TSP instance induced by the shortest path metric on G. The algorithm can also be used to output a corresponding solution to the Subtour Elimination LP. We substantially improve upon the O(m^2 log^2(m)/ε^2) running time achieved previously by Garg and Khandekar.