arXiv subjects

James King

Publications and source records attributed to James King.

24 records · Page 2Linked to original sources

arXiv2011

Random hyperplane search trees in high dimensions

Given a set S of n \geq d points in general position in R^d, a random hyperplane split is obtained by sampling d points uniformly at random without replacement from S and splitting based on their affine hull. A random hyperplane search tree is a binary space partition tree obtained by recursive application of random hyperplane splits. We investigate the structural distributions of such random trees with a particular focus on the growth with d. A blessing of dimensionality arises--as d increases, random hyperplane splits more closely resemble perfectly balanced splits; in turn, random hyperplane search trees more closely resemble perfectly balanced binary search trees. We prove that, for any fixed dimension d, a random hyperplane search tree storing n points has height at most (1 + O(1/sqrt(d))) log_2 n and average element depth at most (1 + O(1/d)) log_2 n with high probability as n \rightarrow \infty. Further, we show that these bounds are asymptotically optimal with respect to d.

cs.CG↗

arXiv2011

Fast Vertex Guarding for Polygons

For a polygon P with n vertices, the vertex guarding problem asks for the minimum subset G of P's vertices such that every point in P is seen by at least one point in G. This problem is NP-complete and APX-hard. The first approximation algorithm (Ghosh, 1987) involves decomposing P into O(n^4) cells that are equivalence classes for visibility from the vertices of P. This discretized problem can then be treated as an instance of set cover and solved in O(n^5) time with a greedy O(log n)-approximation algorithm. Ghosh (2010) recently revisited the algorithm, noting that minimum visibility decompositions for simple polygons (Bose et al., 2000) have only O(n^3) cells, improving the running time of the algorithm to O(n^4) for simple polygons. In this paper we show that, since minimum visibility decompositions for simple polygons have only O(n^2) cells of minimal visibility (Bose et al., 2000), the running time of the algorithm can be further improved to O(n^3). This result was obtained independently by Jang and Kwon (2011). We extend the result of Bose et al. to polygons with holes, showing that a minimum visibility decomposition of a polygon with h holes has only O((h+1)n^3) cells and only O((h+1)^2 n^2) cells of minimal visibility. We exploit this result to obtain a faster algorithm for vertex guarding polygons with holes. We then show that, in the same time complexity, we can attain approximation factors of O(log log(opt)) for simple polygons and O((1+\log((h+1))) log(opt)) for polygons with holes.

cs.CG↗

arXiv2010

Odds-On Trees

Let R^d -> A be a query problem over R^d for which there exists a data structure S that can compute P(q) in O(log n) time for any query point q in R^d. Let D be a probability measure over R^d representing a distribution of queries. We describe a data structure called the odds-on tree, of size O(n^ε) that can be used as a filter that quickly computes P(q) for some query values q in R^d and relies on S for the remaining queries. With an odds-on tree, the expected query time for a point drawn according to D is O(H*+1), where H* is a lower-bound on the expected cost of any linear decision tree that solves P. Odds-on trees have a number of applications, including distribution-sensitive data structures for point location in 2-d, point-in-polytope testing in d dimensions, ray shooting in simple polygons, ray shooting in polytopes, nearest-neighbour queries in R^d, point-location in arrangements of hyperplanes in R^d, and many other geometric searching problems that can be solved in the linear-decision tree model. A standard lifting technique extends these results to algebraic decision trees of constant degree. A slightly different version of odds-on trees yields similar results for orthogonal searching problems that can be solved in the comparison tree model.

cs.CG↗

arXiv2010

Improved Approximation for Guarding Simple Galleries from the Perimeter

We provide an O(log log OPT)-approximation algorithm for the problem of guarding a simple polygon with guards on the perimeter. We first design a polynomial-time algorithm for building epsilon-nets of size O(1/epsilon log log 1/epsilon) for the instances of Hitting Set associated with our guarding problem. We then apply the technique of Bronnimann and Goodrich to build an approximation algorithm from this epsilon-net finder. Along with a simple polygon P, our algorithm takes as input a finite set of potential guard locations that must include the polygon's vertices. If a finite set of potential guard locations is not specified, e.g. when guards may be placed anywhere on the perimeter, we use a known discretization technique at the cost of making the algorithm's running time potentially linear in the ratio between the longest and shortest distances between vertices. Our algorithm is the first to improve upon O(log OPT)-approximation algorithms that use generic net finders for set systems of finite VC-dimension.

cs.CG↗

arXiv2010

Point Location in Disconnected Planar Subdivisions

Let $G$ be a (possibly disconnected) planar subdivision and let $D$ be a probability measure over $\R^2$. The current paper shows how to preprocess $(G,D)$ into an O(n) size data structure that can answer planar point location queries over $G$. The expected query time of this data structure, for a query point drawn according to $D$, is $O(H+1)$, where $H$ is a lower bound on the expected query time of any linear decision tree for point location in $G$. This extends the results of Collette et al (2008, 2009) from connected planar subdivisions to disconnected planar subdivisions. A version of this structure, when combined with existing results on succinct point location, provides a succinct distribution-sensitive point location structure.

cs.CG↗

arXiv2009

The Complexity of Guarding Terrains

A set $G$ of points on a 1.5-dimensional terrain, also known as an $x$-monotone polygonal chain, is said to guard the terrain if any point on the terrain is 'seen' by a point in $G$. Two points on the terrain see each other if and only if the line segment between them is never strictly below the terrain. The minimum terrain guarding problem asks for a minimum guarding set for the given input terrain. We prove that the decision version of this problem is NP-hard. This solves a significant open problem and complements recent positive approximability results for the optimization problem. Our proof uses a reduction from PLANAR 3-SAT. We build gadgets capable of 'mirroring' a consistent variable assignment back and forth across a main valley. The structural simplicity of 1.5-dimensional terrains makes it difficult to build general clause gadgets that do not destroy this assignment when they are evaluated. However, we exploit the structure in instances of PLANAR 3-SAT to find very specific operations involving only 'adjacent' variables. For these restricted operations we can construct gadgets that allow a full reduction to work.

cs.CG↗