4,817 research outputs found

    On a decomposition of regular domains into John domains with uniform constants

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    We derive a decomposition result for regular, two-dimensional domains into John domains with uniform constants. We prove that for every simply connected domain ΩR2\Omega \subset {\Bbb R}^2 with C1C^1-boundary there is a corresponding partition Ω=Ω1ΩN\Omega = \Omega_1 \cup \ldots \cup \Omega_N with j=1NH1(ΩjΩ)θ\sum_{j=1}^N \mathcal{H}^1(\partial \Omega_j \setminus \partial \Omega) \le \theta such that each component is a John domain with a John constant only depending on θ\theta. The result implies that many inequalities in Sobolev spaces such as Poincar\'e's or Korn's inequality hold on the partition of Ω\Omega for uniform constants, which are independent of Ω\Omega

    A QPTAS for Maximum Weight Independent Set of Polygons with Polylogarithmically Many Vertices

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    The Maximum Weight Independent Set of Polygons problem is a fundamental problem in computational geometry. Given a set of weighted polygons in the 2-dimensional plane, the goal is to find a set of pairwise non-overlapping polygons with maximum total weight. Due to its wide range of applications, the MWISP problem and its special cases have been extensively studied both in the approximation algorithms and the computational geometry community. Despite a lot of research, its general case is not well-understood. Currently the best known polynomial time algorithm achieves an approximation ratio of n^(epsilon) [Fox and Pach, SODA 2011], and it is not even clear whether the problem is APX-hard. We present a (1+epsilon)-approximation algorithm, assuming that each polygon in the input has at most a polylogarithmic number of vertices. Our algorithm has quasi-polynomial running time. We use a recently introduced framework for approximating maximum weight independent set in geometric intersection graphs. The framework has been used to construct a QPTAS in the much simpler case of axis-parallel rectangles. We extend it in two ways, to adapt it to our much more general setting. First, we show that its technical core can be reduced to the case when all input polygons are triangles. Secondly, we replace its key technical ingredient which is a method to partition the plane using only few edges such that the objects stemming from the optimal solution are evenly distributed among the resulting faces and each object is intersected only a few times. Our new procedure for this task is not more complex than the original one, and it can handle the arising difficulties due to the arbitrary angles of the polygons. Note that already this obstacle makes the known analysis for the above framework fail. Also, in general it is not well understood how to handle this difficulty by efficient approximation algorithms

    A Randomized Incremental Algorithm for the Hausdorff Voronoi Diagram of Non-crossing Clusters

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    In the Hausdorff Voronoi diagram of a family of \emph{clusters of points} in the plane, the distance between a point tt and a cluster PP is measured as the maximum distance between tt and any point in PP, and the diagram is defined in a nearest-neighbor sense for the input clusters. In this paper we consider %El."non-crossing" \emph{non-crossing} clusters in the plane, for which the combinatorial complexity of the Hausdorff Voronoi diagram is linear in the total number of points, nn, on the convex hulls of all clusters. We present a randomized incremental construction, based on point location, that computes this diagram in expected O(nlog2n)O(n\log^2{n}) time and expected O(n)O(n) space. Our techniques efficiently handle non-standard characteristics of generalized Voronoi diagrams, such as sites of non-constant complexity, sites that are not enclosed in their Voronoi regions, and empty Voronoi regions. The diagram finds direct applications in VLSI computer-aided design.Comment: arXiv admin note: substantial text overlap with arXiv:1306.583
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