pp 1–37 | Cite as

Quasi-Polynomial Time Approximation Schemes for Packing and Covering Problems in Planar Graphs

  • Michał Pilipczuk
  • Erik Jan van Leeuwen
  • Andreas WieseEmail author


We consider two optimization problems in planar graphs. In Maximum Weight Independent Set of Objects we are given a graph G and a family \({\mathcal {D}}\) of objects, each being a connected subgraph of G with a prescribed weight, and the task is to find a maximum-weight subfamily of \({\mathcal {D}}\) consisting of pairwise disjoint objects. In Minimum Weight Distance Set Cover we are given a graph G in which the edges might have different lengths, two sets \({\mathcal {D}},{\mathcal {C}}\) of vertices of G, where vertices of \({\mathcal {D}}\) have prescribed weights, and a nonnegative radius r. The task is to find a minimum-weight subset of \({\mathcal {D}}\) such that every vertex of \({\mathcal {C}}\) is at distance at most r from some selected vertex. Via simple reductions, these two problems generalize a number of geometric optimization tasks, notably Maximum Weight Independent Set for polygons in the plane and Weighted Geometric Set Cover for unit disks and unit squares. We present quasi-polynomial time approximation schemes (QPTASs) for both of the above problems in planar graphs: given an accuracy parameter \(\epsilon >0\) we can compute a solution whose weight is within multiplicative factor of \((1+\epsilon )\) from the optimum in time \(2^{{\mathrm {poly}}(1/\epsilon ,\log |{\mathcal {D}}|)}\cdot n^{{\mathcal {O}}(1)}\), where n is the number of vertices of the input graph. We note that a QPTAS for Maximum Weight Independent Set of Objects would follow from existing work. However, our main contribution is to provide a unified framework that works for both problems in both a planar and geometric setting and to transfer the techniques used for recursive approximation schemes for geometric problems due to Adamaszek and Wiese (in Proceedings of the FOCS 2013, IEEE, 2013; in Proceedings of the SODA 2014, SIAM, 2014) and Har-Peled and Sariel (in Proceedings of the SOCG 2014, SIAM, 2014) to the setting of planar graphs. In particular, this yields a purely combinatorial viewpoint on these methods as a phenomenon in planar graphs.


Approximation schemes Planar graphs Independent set of objects Geometric set cover 



We thank Dániel Marx for insightful discussions on the approach to optimization problems in geometric and planar settings via Voronoi diagrams.


  1. 1.
    Adamaszek, A., Wiese, A.: Approximation schemes for maximum weight independent set of rectangles. In: Proceedings of the FOCS 2013, pp. 400–409. IEEE (2013)Google Scholar
  2. 2.
    Adamaszek, A., Wiese, A.: A QPTAS for maximum weight independent set of polygons with polylogarithmically many vertices. In: Proceedings of the SODA 2014, pp. 645–656. SIAM (2014)Google Scholar
  3. 3.
    Gu, Q.-P., Tamaki, H.: Improved bounds on the planar branchwidth with respect to the largest grid minor size. Algorithmica 64(3), 416–453 (2012)MathSciNetCrossRefGoogle Scholar
  4. 4.
    Har-Peled, S.: Quasi-polynomial time approximation scheme for sparse subsets of polygons. In: Proceedings of the SOCG 2014, pp. 120–129. SIAM (2014)Google Scholar
  5. 5.
    Marx, D., Pilipczuk, M.: Optimal parameterized algorithms for planar facility location problems using Voronoi diagrams. In: Proceedings of the ESA 2015, Volume 9294 of LNCS, pp. 865–877. Springer (2015)Google Scholar
  6. 6.
    Miller, G.L.: Finding small simple cycle separators for 2-connected planar graphs. J. Comput. Syst. Sci. 32(3), 265–279 (1986)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Mustafa, N.H., Raman, R., Ray, S.: Quasi-polynomial time approximation scheme for weighted geometric set cover on pseudodisks and halfspaces. SIAM J. Comput. 44(6), 1650–1669 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Seymour, P.D., Thomas, R.: Call routing and the ratcatcher. Combinatorica 14(2), 217–241 (1994)MathSciNetCrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Institute of InformaticsUniversity of WarsawWarsawPoland
  2. 2.Department of Information and Computing SciencesUtrecht UniversityUtrechtThe Netherlands
  3. 3.Department of Industrial Engineering and Center for Mathematical ModelingUniversidad de ChileSantiago de ChileChile

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