An approximation algorithm for maximum weight budgeted connected set cover
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  • 作者:Yingli Ran ; Zhao Zhang ; Ker-I Ko ; Jun Liang
  • 关键词:Budgeted set cover ; Connected set cover ; Approximation algorithm
  • 刊名:Journal of Combinatorial Optimization
  • 出版年:2016
  • 出版时间:May 2016
  • 年:2016
  • 卷:31
  • 期:4
  • 页码:1505-1517
  • 全文大小:482 KB
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  • 作者单位:Yingli Ran (1)
    Zhao Zhang (2)
    Ker-I Ko (3)
    Jun Liang (4)

    1. College of Mathematics and System Sciences, Xinjiang University, Urumqi, 830046, Xinjiang, China
    2. College of Mathematics Physics and Information Engineering, Zhejiang Normal University, Jinhua, 321004, Zhejiang, China
    3. Department of Computer Science, National Chiao Tung University, Hsinchu, 30050, Taiwan
    4. Department of Computer Science, University of Texas at Dallas, Richardson, TX, 75080, USA
  • 刊物类别:Mathematics and Statistics
  • 刊物主题:Mathematics
    Combinatorics
    Convex and Discrete Geometry
    Mathematical Modeling and IndustrialMathematics
    Theory of Computation
    Optimization
    Operation Research and Decision Theory
  • 出版者:Springer Netherlands
  • ISSN:1573-2886
文摘
This paper studies approximation algorithm for the maximum weight budgeted connected set cover (MWBCSC) problem. Given an element set \(X\), a collection of sets \({\mathcal {S}}\subseteq 2^X\), a weight function \(w\) on \(X\), a cost function \(c\) on \({\mathcal {S}}\), a connected graph \(G_{\mathcal {S}}\) (called communication graph) on vertex set \({\mathcal {S}}\), and a budget \(L\), the MWBCSC problem is to select a subcollection \({\mathcal {S'}}\subseteq {\mathcal {S}}\) such that the cost \(c({\mathcal {S'}})=\sum _{S\in {\mathcal {S'}}}c(S)\le L\), the subgraph of \(G_{\mathcal {S}}\) induced by \({\mathcal {S'}}\) is connected, and the total weight of elements covered by \({\mathcal {S'}}\) (that is \(\sum _{x\in \bigcup _{S\in {\mathcal {S'}}}S}w(x)\)) is maximized. We present a polynomial time algorithm for this problem with a natural communication graph that has performance ratio \(O((\delta +1)\log n)\), where \(\delta \) is the maximum degree of graph \(G_{\mathcal {S}}\) and \(n\) is the number of sets in \({\mathcal {S}}\). In particular, if every set has cost at most \(L/2\), the performance ratio can be improved to \(O(\log n)\).

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