8 research outputs found
Separating a Voronoi Diagram via Local Search
Given a set P of n points in R^dwe show how to insert a set Z of O(n^(1-1/d)) additional points, such that P can be broken into two sets P1 and P2of roughly equal size, such that in the Voronoi diagram V(P u Z), the cells of P1 do not touch the cells of P2; that is, Z separates P1 from P2 in the Voronoi diagram (and also in the dual Delaunay triangulation). In addition, given such a partition (P1,P2) of Pwe present an approximation algorithm to compute a minimum size separator realizing this partition. We also present a simple local search algorithm that is a PTAS for approximating the optimal Voronoi partition
On Variants of k-means Clustering
\textit{Clustering problems} often arise in the fields like data mining,
machine learning etc. to group a collection of objects into similar groups with
respect to a similarity (or dissimilarity) measure. Among the clustering
problems, specifically \textit{-means} clustering has got much attention
from the researchers. Despite the fact that -means is a very well studied
problem its status in the plane is still an open problem. In particular, it is
unknown whether it admits a PTAS in the plane. The best known approximation
bound in polynomial time is 9+\eps.
In this paper, we consider the following variant of -means. Given a set
of points in and a real , find a finite set of
points in that minimizes the quantity . For any fixed dimension , we design a local
search PTAS for this problem. We also give a "bi-criterion" local search
algorithm for -means which uses (1+\eps)k centers and yields a solution
whose cost is at most (1+\eps) times the cost of an optimal -means
solution. The algorithm runs in polynomial time for any fixed dimension.
The contribution of this paper is two fold. On the one hand, we are being
able to handle the square of distances in an elegant manner, which yields near
optimal approximation bound. This leads us towards a better understanding of
the -means problem. On the other hand, our analysis of local search might
also be useful for other geometric problems. This is important considering that
very little is known about the local search method for geometric approximation.Comment: 15 page
Submodular Clustering in Low Dimensions
We study a clustering problem where the goal is to maximize the coverage of the input points by k chosen centers. Specifically, given a set of n points P ? ?^d, the goal is to pick k centers C ? ?^d that maximize the service ?_{p?P}?(?(p,C)) to the points P, where ?(p,C) is the distance of p to its nearest center in C, and ? is a non-increasing service function ?: ?+ ? ?+. This includes problems of placing k base stations as to maximize the total bandwidth to the clients - indeed, the closer the client is to its nearest base station, the more data it can send/receive, and the target is to place k base stations so that the total bandwidth is maximized. We provide an n^{?^-O(d)} time algorithm for this problem that achieves a (1-?)-approximation. Notably, the runtime does not depend on the parameter k and it works for an arbitrary non-increasing service function ?: ?+ ? ?+
An approximation algorithm for the art gallery problem
Given a simple polygon P on n vertices, two points x, y in P are said to be visible to each other if the line segment between x and y is contained in P. The Point Guard Art Gallery problem asks for a minimum-size set S such that every point in P is visible from a point in S. The set S is referred to as guards. Assuming integer coordinates and a specific general position on the vertices of P, we present the first O(log OPT)-approximation algorithm for the point guard problem. This algorithm combines ideas in papers of Efrat and Har-Peled and Deshpande et al. We also point out a mistake in the latter
Packing and Covering with Non-Piercing Regions
In this paper, we design the first polynomial time approximation schemes for the Set Cover and Dominating Set problems when the underlying sets are non-piercing regions (which include pseudodisks). We show that the local
search algorithm that yields PTASs when the regions are disks [Aschner/Katz/Morgenstern/Yuditsky, WALCOM 2013; Gibson/Pirwani, 2005; Mustafa/Raman/Ray, 2015] can be extended to work for non-piercing regions. While such an extension is intuitive and natural, attempts to settle this question have failed even for pseudodisks. The techniques used for analysis when the regions are disks rely heavily on the underlying geometry, and do not extend to topologically defined settings such as pseudodisks. In order to prove our results, we introduce novel techniques that we believe will find applications in other problems.
We then consider the Capacitated Region Packing problem. Here, the input consists of a set of points with capacities, and a set of regions. The objective is to pick a maximum cardinality subset of regions so that no point is covered by more regions than its capacity. We show that this problem admits a PTAS when the regions are k-admissible regions (pseudodisks are 2-admissible), and the capacities are bounded. Our result settles a conjecture of Har-Peled (see Conclusion of [Har-Peled, SoCG 2014]) in the affirmative. The conjecture was for a weaker version of the problem, namely when the regions are pseudodisks, the capacities are uniform, and the point set consists of all points in the plane.
Finally, we consider the Capacitated Point Packing problem. In this setting, the regions have capacities, and our
objective is to find a maximum cardinality subset of points such that no region has more points than its capacity. We show that this problem admits a PTAS when the capacity is unity, extending one of the results of Ene et al. [Ene/Har-Peled/Raichel, SoCG 2012]