Please wait a minute...
Frontiers of Computer Science

ISSN 2095-2228

ISSN 2095-2236(Online)

CN 10-1014/TP

Postal Subscription Code 80-970

2018 Impact Factor: 1.129

Front. Comput. Sci.    2023, Vol. 17 Issue (3) : 173404    https://doi.org/10.1007/s11704-022-1665-9
RESEARCH ARTICLE
A primal-dual approximation algorithm for the k-prize-collecting minimum vertex cover problem with submodular penalties
Xiaofei LIU1, Weidong LI2(), Jinhua YANG3
1. School of Information Science and Engineering, Yunnan University, Kunming 650500, China
2. School of Mathematics and Statistics, Yunnan University, Kunming 650500, China
3. Dianchi College of Yunnan University, Kunming 650228, China
 Download: PDF(2172 KB)   HTML
 Export: BibTeX | EndNote | Reference Manager | ProCite | RefWorks
Abstract

In this paper, we consider the k-prize-collecting minimum vertex cover problem with submodular penalties, which generalizes the well-known minimum vertex cover problem, minimum partial vertex cover problem and minimum vertex cover problem with submodular penalties. We are given a cost graph G=(V,E;c) and an integer k. This problem determines a vertex set SV such that S covers at least k edges. The objective is to minimize the total cost of the vertices in S plus the penalty of the uncovered edge set, where the penalty is determined by a submodular function. We design a two-phase combinatorial algorithm based on the guessing technique and the primal-dual framework to address the problem. When the submodular penalty cost function is normalized and nondecreasing, the proposed algorithm has an approximation factor of 3. When the submodular penalty cost function is linear, the approximation factor of the proposed algorithm is reduced to 2, which is the best factor if the unique game conjecture holds.

Keywords vertex cover      k-prize-collecting      primal-dual      approximation algorithm     
Corresponding Author(s): Weidong LI   
About author:

Tongcan Cui and Yizhe Hou contributed equally to this work.

Just Accepted Date: 22 April 2022   Issue Date: 20 October 2022
 Cite this article:   
Xiaofei LIU,Weidong LI,Jinhua YANG. A primal-dual approximation algorithm for the k-prize-collecting minimum vertex cover problem with submodular penalties[J]. Front. Comput. Sci., 2023, 17(3): 173404.
 URL:  
https://academic.hep.com.cn/fcs/EN/10.1007/s11704-022-1665-9
https://academic.hep.com.cn/fcs/EN/Y2023/V17/I3/173404
  
Fig.1  For instance I=(G;c,π;k), we illustrate Phase 1 in a, b and c; and illustrate Phase 2 in d and e
Fig.2  The relationship of E, E1 and E2
  
  
  
  
1 R M Karp . Reducibility among combinatorial problems. In: Miller R E, Thatcher J W, Bohlinger J D, eds. Complexity of Computer Computations. Boston: Springer, 1972, 85–103
2 V V Vazirani . Approximation Algorithms. Berlin, Heidelberg: Springer, 2001
3 S, Khot O Regev . Vertex cover might be hard to approximate to within 2 -ε. Journal of Computer and System Sciences, 2008, 74( 3): 335–349
4 D S Hochbaum . Approximation algorithms for the set covering and vertex cover problems. SIAM Journal on Computing, 1982, 11( 3): 555–556
5 R, Bar-Yehuda S Even . A linear-time approximation algorithm for the weighted vertex cover problem. Journal of Algorithms, 1981, 2( 2): 198–203
6 N H, Bshouty L Burroughs . Massaging a linear programming solution to give a 2-approximation for a generalization of the vertex cover problem. In: Proceedings of the 15th Annual Symposium on Theoretical Aspects of Computer Science. 1998, 298–308
7 D S Hochbaum . The t-vertex cover problem: extending the half integrality framework with budget constraints. In: Proceedings of International Workshop on Approximation Algorithms for Combinatorial Optimization. 1998, 111–122
8 R Bar-Yehuda . Using homogeneous weights for approximating the partial cover problem. Journal of Algorithms, 2001, 39( 2): 137–144
9 R, Gandhi S, Khuller A Srinivasan . Approximation algorithms for partial covering problems. Journal of Algorithms, 2004, 53( 1): 55–84
10 J Mestre . A primal-dual approximation algorithm for partial vertex cover: making educated guesses. Algorithmica, 2009, 55( 1): 227–239
11 D S Hochbaum . Solving integer programs over monotone inequalities in three variables: a framework for half integrality and good approximations. European Journal of Operational Research, 2002, 140( 2): 291–321
12 R, Bar-Yehuda D Rawitz . On the equivalence between the primal-dual schema and the local ratio technique. SIAM Journal on Discrete Mathematics, 2005, 19( 3): 762–797
13 Y, Li D, Du N, Xiu D Xu . Improved approximation algorithms for the facility location problems with linear/submodular penalties. Algorithmica, 2015, 73( 2): 460–482
14 D, Du R, Lu D Xu . A primal-dual approximation algorithm for the facility location problem with submodular penalties. Algorithmica, 2012, 63(1–2): 1–2
15 X, Liu W Li . Approximation algorithms for the multiprocessor scheduling with submodular penalties. Optimization Letters, 2021, 15( 6): 2165–2180
16 X, Liu W, Li R Xie . A primal-dual approximation algorithm for the k-prize-collecting minimum power cover problem. Optimization Letters, 2021, DOI:
https://doi.org/10.1007/s11590-021-01831-z
17 S, Iwata K Nagano . Submodular function minimization under covering constraints. In: Proceedings of the 50th Annual IEEE Symposium on Foundations of Computer Science. 2009, 671–680
18 D, Xu F, Wang D, Du C Wu . Approximation algorithms for submodular vertex cover problems with linear/submodular penalties using primal-dual technique. Theoretical Computer Science, 2016, 630: 117–125
19 N Kamiyama . A note on the submodular vertex cover problem with submodular penalties. Theoretical Computer Science, 2017, 659: 95–97
20 J S, Guo W, Liu B Hou . An approximation algorithm for p-prize-collecting set cover problem. Journal of the Operations Research Society of China, 2021, DOI:
https://doi.org/10.1007/s40305-021-00364-7
21 L, Fleischer S Iwata . A push-relabel framework for submodular function minimization and applications to parametric optimization. Discrete Applied Mathematics, 2003, 131( 2): 311–322
22 M J, Kao J Y, Shiau C C, Lin D T Lee . Tight approximation for partial vertex cover with hard capacities. Theoretical Computer Science, 2019, 778: 61–72
23 W C, Cheung M X, Goemans S C W Wong . Improved algorithms for vertex cover with hard capacities on multigraphs and hypergraphs. In: Proceedings of the 25th Annual ACM-SIAM Symposium on Discrete Algorithms. 2014, 1714–1726
24 S C W Wong . Tight algorithms for vertex cover with hard capacities on multigraphs and hypergraphs. In: Proceedings of the 28th Annual ACM-SIAM Symposium on Discrete Algorithms. 2017, 2626–2637
[1] FCS-21665-OF-XL_suppl_1 Download
[1] Peng ZHANG. The LP-rounding plus greed approach for partial optimization revisited[J]. Front. Comput. Sci., 2022, 16(1): 161402-.
[2] Yun PENG, Xin LIN, Byron CHOI, Bingsheng HE. VColor*: a practical approach for coloring large graphs[J]. Front. Comput. Sci., 2021, 15(4): 154610-.
[3] Yudong QIN, Deke GUO, Zhiyao HU, Bangbang REN. Uncertain multicast under dynamic behaviors[J]. Front. Comput. Sci., 2020, 14(1): 130-145.
[4] Xingshen SONG, Yuexiang YANG, Yu JIANG, Kun JIANG. Optimizing partitioning strategies for faster inverted index compression[J]. Front. Comput. Sci., 2019, 13(2): 343-356.
[5] Peng ZHANG. Unbalanced graph cuts with minimum capacity[J]. Front. Comput. Sci., 2014, 8(4): 676-683.
[6] Deying LI, Qinghua ZHU, Jiannong CAO, . Approximation algorithm for constructing data aggregation trees for wireless sensor networks[J]. Front. Comput. Sci., 2009, 3(4): 524-534.
Viewed
Full text


Abstract

Cited

  Shared   
  Discussed