Assigning Weights to Minimize the Covering Radius in the Plane

Given a set P of n points in the plane and a multiset W of k weights with k leq n, we assign a weight in W to a point in P to minimize the maximum weighted distance from the weighted center of P to any point in P. In this paper, we give two algorithms which take O(k^2 n^2 log^4 n) time and O(k^5 n log^4 k + kn log^3 n) time, respectively. For a constant k, the second algorithm takes only O(n log^3 n) time, which is near-linear

A Near-Optimal Algorithm for Finding an Optimal Shortcut of a Tree

We consider the problem of finding a shortcut connecting two vertices of a graph that minimizes the diameter of the resulting graph. We present an O(n^2 log^3 n)-time algorithm using linear space for the case that the input graph is a tree consisting of n vertices. Additionally, we present an O(n^2 log^3 n)-time algorithm using linear space for a continuous version of this problem

Dynamic Geodesic Convex Hulls in Dynamic Simple Polygons

We consider the geodesic convex hulls of points in a simple polygonal region in the presence of non-crossing line segments (barriers) that subdivide the region into simply connected faces. We present an algorithm together with data structures for maintaining the geodesic convex hull of points in each face in a sublinear update time under the fully-dynamic setting where both input points and barriers change by insertions and deletions. The algorithm processes a mixed update sequence of insertions and deletions of points and barriers. Each update takes O(n^2/3 log^2 n) time with high probability, where n is the total number of the points and barriers at the moment. Our data structures support basic queries on the geodesic convex hull, each of which takes O(polylog n) time. In addition, we present an algorithm together with data structures for geodesic triangle counting queries under the fully-dynamic setting. With high probability, each update takes O(n^2/3 log n) time, and each query takes O(n^2/3 log n) time

Approximate Range Queries for Clustering

We study the approximate range searching for three variants of the clustering problem with a set P of n points in d-dimensional Euclidean space and axis-parallel rectangular range queries: the k-median, k-means, and k-center range-clustering query problems. We present data structures and query algorithms that compute (1+epsilon)-approximations to the optimal clusterings of P cap Q efficiently for a query consisting of an orthogonal range Q, an integer k, and a value epsilon>0

A New Balanced Subdivision of a Simple Polygon for Time-Space Trade-off Algorithms

We are given a read-only memory for input and a write-only stream for output. For a positive integer parameter s, an s-workspace algorithm is an algorithm using only O(s) words of workspace in addition to the memory for input. In this paper, we present an O(n^2/s)-time s-workspace algorithm for subdividing a simple polygon into O(min{n/s,s}) subpolygons of complexity O(max{n/s,s}). As applications of the subdivision, the previously best known time-space trade-offs for the following three geometric problems are improved immediately: (1) computing the shortest path between two points inside a simple n-gon, (2) computing the shortest path tree from a point inside a simple n-gon, (3) computing a triangulation of a simple n-gon. In addition, we improve the algorithm for the second problem even further

Finding Pairwise Intersections of Rectangles in a Query Rectangle

We consider the following problem: Preprocess a set S of n axis-parallel boxes in mathbb{R}^d so that given a query of an axis-parallel box Q in mathbb{R}^d, the pairs of boxes of S whose intersection intersects the query box can be reported efficiently. For the case that d=2, we present a data structure of size O(nlog n) supporting O(log n+k) query time, where k is the size of the output. This improves the previously best known result by de Berg et al. which requires O(log nlog^* n+ klog n) query time using O(nlog n) space.There has been no known result for this problem for higher dimensions, except that for d=3, the best known data structure supports O(sqrt{n}+klog^2log^* n) query time using O(nsqrt {n}log n) space. For a constant d>2, we present a data structure supporting O(n^{1-delta}log^{d-1} n + k polylog n) query time for any constant 1/dleqdelta<1.The size of the data structure is O(n^{delta d}log n) if 1/dleqdelta<1/2, or O(n^{delta d-2delta+1}) if 1/2leq delta<1

Point Location in Dynamic Planar Subdivisions

We study the point location problem on dynamic planar subdivisions that allows insertions and deletions of edges. In our problem, the underlying graph of a subdivision is not necessarily connected. We present a data structure of linear size for such a dynamic planar subdivision that supports sublinear-time update and polylogarithmic-time query. Precisely, the amortized update time is O(sqrt{n}log n(log log n)^{3/2}) and the query time is O(log n(log log n)^2), where n is the number of edges in the subdivision. This answers a question posed by Snoeyink in the Handbook of Computational Geometry. When only deletions of edges are allowed, the update time and query time are just O(alpha(n)) and O(log n), respectively