Abstract

The Vertex Cover problem plays an essential role in the study of polynomial kernelization in parameterized complexity, i.e., the study of provable and efficient preprocessing for ${\mathsf{NP}}$-hard problems. Motivated by the great variety of positive and negative results for kernelization for Vertex Cover subject to different parameters and graph classes, we seek to unify and generalize them using so-called blocking sets. A blocking set is a set of vertices such that no optimal vertex cover contains all vertices in the blocking set, and the study of minimal blocking sets played implicit and explicit roles in many existing results. We show that in the most-studied setting, parameterized by the size of a deletion set to a specified graph class ${\mathcal{C}}$, bounded minimal blocking set size is necessary but not sufficient to get a polynomial kernelization. Under mild technical assumptions, bounded minimal blocking set size is shown to allow an essentially tight polynomial-time reduction in the number of connected components. We then determine the exact maximum size of minimal blocking sets for graphs of bounded elimination distance to any hereditary class $\mathcal{C}$, including the case of graphs of bounded treedepth. We get similar but not tight bounds for certain nonhereditary classes $\mathcal{C}$, including the class ${\mathcal{C}}_{{\mathrm{LP}}}$ of graphs where integral and fractional vertex cover size coincide. These bounds allow us to derive polynomial kernels for Vertex Cover parameterized by the size of a deletion set to graphs of bounded elimination distance to, e.g., forest, bipartite, or ${\mathcal{C}}_{\mathrm{LP}}$ graphs.

Keywords

  1. Vertex Cover
  2. kernelization
  3. blocking sets
  4. elimination distance
  5. structural parameters

MSC codes

  1. 68Q25
  2. 05C85

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References

1.
L. M. Adleman, Two theorems on random polynomial time, in Proceedings of the 19th Annual Symposium on Foundations of Computer Science, Ann Arbor, Michigan, IEEE Computer Society, 1978, pp. 75--83, https://doi.org/10.1109/SFCS.1978.37.
2.
H. L. Bodlaender, B. M. P. Jansen, and S. Kratsch, Kernelization lower bounds by cross-composition, SIAM J. Discrete Math., 28 (2014), pp. 277--305, https://doi.org/10.1137/120880240.
3.
H. L. Bodlaender, S. Thomassé, and A. Yeo, Kernel bounds for disjoint cycles and disjoint paths, Theoret. Comput. Sci., 412 (2011), pp. 4570--4578, https://doi.org/10.1016/j.tcs.2011.04.039.
4.
M. Bougeret and I. Sau, How much does a treedepth modulator help to obtain polynomial kernels beyond sparse graphs?, Algorithmica, 81 (2019), pp. 4043--4068, https://doi.org/10.1007/s00453-018-0468-8.
5.
J. Bulian and A. Dawar, Graph isomorphism parameterized by elimination distance to bounded degree, Algorithmica, 75 (2016), pp. 363--382, https://doi.org/10.1007/s00453-015-0045-3.
6.
M. Cygan, F. V. Fomin, L. Kowalik, D. Lokshtanov, D. Marx, M. Pilipczuk, M. Pilipczuk, and S. Saurabh, Parameterized Algorithms, Springer, New York, 2015, https://doi.org/10.1007/978-3-319-21275-3.
7.
H. Dell and D. van Melkebeek, Satisfiability allows no nontrivial sparsification unless the polynomial-time hierarchy collapses, J. ACM, 61 (2014), pp. 23:1--23:27, https://doi.org/10.1145/2629620.
8.
F. V. Fomin, D. Lokshtanov, S. Saurabh, and M. Zehavi, Kernelization: Theory of Parameterized Preprocessing, Cambridge University Press, Cambridge, 2018, https://doi.org/10.1017/9781107415157.
9.
F. V. Fomin and T. J. F. Strømme, Vertex cover structural parameterization revisited, in Proceedings of the 42nd International Workshop Graph-Theoretic Concepts in Computer Science, WG 2016, Istanbul, Turkey, Lecture Notes Comput. Sci. 9941, P. Heggernes, ed., 2016, pp. 171--182, https://doi.org/10.1007/978-3-662-53536-3_15.
10.
S. Garg and G. Philip, Raising the bar for vertex cover: Fixed-parameter tractability above higher guarantee, in Proceedings of the 27th Annual ACM-SIAM Symposium on Discrete Algorithms, SODA 2016, Arlington, VA, January 10-12, 2016, R. Krauthgamer, ed., SIAM, Philadelphia, 2016, pp. 1152--1166, https://doi.org/10.1137/1.9781611974331.ch80.
11.
E. C. Hols and S. Kratsch, Smaller parameters for vertex cover kernelization, in Proceedings of the 12th International Symposium on Parameterized and Exact Computation, IPEC 2017, Vienna, Austria, LIPIcs, 89, D. Lokshtanov and N. Nishimura, eds., Schloss Dagstuhl---Leibniz-Zentrum fuer Informatik, 2017, pp. 20:1--20:12, https://doi.org/10.4230/LIPIcs.IPEC.2017.20.
12.
J. E. Hopcroft and R. M. Karp, An $n^{5/2}$ algorithm for maximum matchings in bipartite graphs, SIAM J. Comput., 2 (1973), pp. 225--231, https://doi.org/10.1137/0202019.
13.
B. M. P. Jansen, The Power of Data Reduction: Kernels for Fundamental Graph Problems, PhD thesis, Utrecht University, 2013.
14.
B. M. P. Jansen and H. L. Bodlaender, Vertex cover kernelization revisited---upper and lower bounds for a refined parameter, Theory Comput. Syst., 53 (2013), pp. 263--299, https://doi.org/10.1007/s00224-012-9393-4.
15.
B. M. P. Jansen and A. Pieterse, Polynomial kernels for hitting forbidden minors under structural parameterizations, in Proceedings of the 26th Annual European Symposium on Algorithms, ESA 2018, Helsinki, Finland, LIPIcs 112, Y. Azar, H. Bast, and G. Herman, eds., Schloss Dagstuhl---Leibniz-Zentrum fuer Informatik, 2018, pp. 48:1--48:15, https://doi.org/10.4230/LIPIcs.ESA.2018.48.
16.
K.-I. Ko, Some observations on the probabilistic algorithms and NP-hard problems, Inform. Process. Lett., 14 (1982), pp. 39--43, https://doi.org/10.1016/0020-0190(82)90139-9.
17.
D. C. Kozen, Design and Analysis of Algorithms, Texts Monogr. Comput. Sci., Springer, Springer, 1992, https://doi.org/10.1007/978-1-4612-4400-4.
18.
S. Kratsch, A randomized polynomial kernelization for vertex cover with a smaller parameter, SIAM J. Discrete Math., 32 (2018), pp. 1806--1839, https://doi.org/10.1137/16M1104585.
19.
S. Kratsch and M. Wahlström, Representative sets and irrelevant vertices: New tools for kernelization, J. ACM, 67 (2020), pp. 16:1--16:50, https://doi.org/10.1145/3390887.
20.
D. Lokshtanov, N. S. Narayanaswamy, V. Raman, M. S. Ramanujan, and S. Saurabh, Faster parameterized algorithms using linear programming, ACM Trans. Algorithms, 11 (2014), pp. 15:1--15:31, https://doi.org/10.1145/2566616.
21.
D. Majumdar, R. Neogi, V. Raman, and S. Vaishali, Tractability of König edge deletion problems, Theoret. Comput. Sci., 796 (2019), pp. 207--215, https://doi.org/10.1016/j.tcs.2019.09.011.
22.
D. Majumdar, V. Raman, and S. Saurabh, Polynomial kernels for vertex cover parameterized by small degree modulators, Theory Comput. Syst., 62 (2018), pp. 1910--1951, https://doi.org/10.1007/s00224-018-9858-1.
23.
G. L. Nemhauser and L. E. T., Jr., Properties of vertex packing and independence system polyhedra, Math. Program., 6 (1974), pp. 48--61, https://doi.org/10.1007/BF01580222.
24.
G. L. Nemhauser and L. E. Trotter, Jr., Vertex packings: Structural properties and algorithms, Math. Program., 8 (1975), pp. 232--248, https://doi.org/10.1007/BF01580444.
25.
L. G. Valiant and V. V. Vazirani, NP is as easy as detecting unique solutions, Theoret. Comput. Sci., 47 (1986), pp. 85--93, https://doi.org/10.1016/0304-3975(86)90135-0.

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Published In

cover image SIAM Journal on Discrete Mathematics
SIAM Journal on Discrete Mathematics
Pages: 1955 - 1990
ISSN (online): 1095-7146

History

Submitted: 30 April 2020
Accepted: 2 March 2022
Published online: 24 August 2022

Keywords

  1. Vertex Cover
  2. kernelization
  3. blocking sets
  4. elimination distance
  5. structural parameters

MSC codes

  1. 68Q25
  2. 05C85

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