Geometric applications of posets

We show the power of posets in computational geometry by solving several problems posed on a set S of n points in the plane: (1) find the n − k − 1 rectilinear farthest neighbors (or, equivalently, k nearest neighbors) to every point of S (extendable to higher dimensions), (2) enumerate the k largest (smallest) rectilinear distances in decreasing (increasing) order among the points of S, (3) given a distance δ > 0, report all the pairs of points that belong to S and are of rectilinear distance δ or more (less), covering k ≥ n/2 points of S by rectilinear (4) and circular (5) concentric rings, and (6) given a number k ≥ n/2 decide whether a query rectangle contains k points or less.     

3-D vertical ray shooting and 2-D point enclosure, range searching, and arc shooting amidst convex fat objects

We present a new data structure for a set of n convex simply-shaped fat objects in the plane, and use it to obtain efficient and rather simple solutions to several problems including (i) vertical ray shooting—preprocess a set of n non-intersecting convex simply-shaped flat objects in 3-space, whose xy-projections are fat, for efficient vertical ray shooting queries, (ii) point enclosure—preprocess a set C of n convex simply-shaped fat objects in the plane, so that the k objects containing a query point p can be reported efficiently, (iii) bounded-size range searching— preprocess a set C of n convex fat polygons, so that the k objects intersecting a “not-too-large” query polygon can be reported efficiently, and (iv) bounded-size segment shooting—preprocess a set C as in (iii), so that the first object (if exists) hit by a “not-too-long” oriented query segment can be found efficiently. For the first three problems we construct data structures of size O(λ_s(n)log³n), where s is the maximum number of intersections between the boundaries of the (xy-projections) of any pair of objects, and λ_s(n) is the maximum length of (n, s) Davenport-Schinzel sequences. The data structure for the fourth problem is of size O(λ_s(n)log²n). The query time in the first problem is O(log⁴n), the query time in the second and third problems is O(log³n + klog²n), and the query time in the fourth problem is O(log³n).

We also present a simple algorithm for computing a depth order for a set as in (i), that is based on the solution to the vertical ray shooting problem. (A depth order for , if exists, is a linear order of , such that, if K₁, K₂ and K₁ lies vertically above K₂, then K₁ precedes K₂.) Unlike the algorithm of Agarwal et al. (1995) that might output a false order when a depth order does not exist, the new algorithm is able to determine whether such an order exists, and it is often more efficient in practical situations than the former algorithm.     

Efficient visibility queries in simple polygons

We present a method of decomposing a simple polygon that allows the preprocessing of the polygon to efficiently answer visibility queries of various forms in an output sensitive manner. Using O(n³logn) preprocessing time and O(n³) space, we can, given a query point q inside or outside an n vertex polygon, recover the visibility polygon of q in O(logn+k) time, where k is the size of the visibility polygon, and recover the number of vertices visible from q in O(logn) time.

The key notion behind the decomposition is the succinct representation of visibility regions, and tight bounds on the number of such regions. These techniques are extended to handle other types of queries, such as visibility of fixed points other than the polygon vertices, and for visibility from a line segment rather than a point. Some of these results have been obtained independently by Guibas, Motwani and Raghavan [18] .     

Chromatic nearest neighbor searching: A query sensitive approach

The nearest neighbor problem is that of preprocessing a set P of n data points in so that, given any query point q, the closest point in P to q can be determined efficiently. In the chromatic nearest neighbor problem, each point of P is assigned a color, and the problem is to determine the color of the nearest point to the query point. More generally, given k1, the problem is to determine the color occurring most frequently among the k nearest neighbors. The chromatic version of the nearest neighbor problem is used in many applications in pattern recognition and learning. In this paper we present a simple algorithm for solving the chromatic k nearest neighbor problem. We provide a query sensitive analysis, which shows that if the color classes form spatially well separated clusters (as often happens in practice), then queries can be answered quite efficiently. We also allow the user to specify an error bound 0, and consider the same problem in the context of approximate nearest neighbor searching. We present empirical evidence that for well clustered data sets, this approach leads to significant improvements in efficiency.     

Label placement by maximum independent set in rectangles

Motivated by the problem of labeling maps, we investigate the problem of computing a large non-intersecting subset in a set of n rectangles in the plane. Our results are as follows. In O(n log n) time, we can find an O(log n)-factor approximation of the maximum subset in a set of n arbitrary axis-parallel rectangles in the plane. If all rectangles have unit height, we can find a 2-approximation in O(n log n) time. Extending this result, we obtain a (1 + 1/k)-approximation in time O(n log n + n^2k−1) time, for any integer k ≥ 1.     

Finding all essential terms of a characteristic maxpolynomial

Let us denote ab=max(a,b) and ab=a+b for and extend this pair of operations to matrices and vectors in the same way as in linear algebra. We present an O(n²(m+n log n)) algorithm for finding all essential terms of the max-algebraic characteristic polynomial of an n×n matrix over with m finite elements. In the cases when all terms are essential, this algorithm also solves the following problem: Given an n×n matrix A and k{1,…,n}, find a k×k principal submatrix of A whose assignment problem value is maximum.     

On parallel rectilinear obstacle- avoiding paths

We give improved space and processor complexities for the problem of computing, in parallel, a data structure that supports queries about shortest rectilinear obstacle-avoiding paths in the plane, where the obstacles are disjoint rectangles. That is, a query specifies any source and destination in the plane, and the data structure enables efficient processing of the query. We now can build the data structure with O(n²/log n) CREW PRAM processors, as opposed to the previous O(n²), and with O(n²) space, as opposed to the previous O(n²(log n)²). The time complexity remains unchanged, at O((log n)²). As before, the data structure we compute enables a query to be processed in O(log n) time, by one processor for obtaining a path length, or by O(k/log n) processors for retrieving a shortest path itself, where k is the number of segments on that path. The new ideas that made our improvement possible include a new partitioning scheme of the recursion tree, which is used to schedule the computations performed on that tree. Since a number of other related shortest paths problems are solved using this technique as a subroutine our improvement translates into a similar improvement in the complexities of these problems as well.     

Computing simple paths among obstacles

Given a set X of points in the plane, two distinguished points s,tX, and a set Φ of obstacles represented by line segments, we wish to compute a simple polygonal path from s to t that uses only points in X as vertices and avoids the obstacles in Φ. We present two results: (1) we show that finding such simple paths among arbitrary obstacles is NP-complete, and (2) we give a polynomial-time algorithm that computes simple paths when the obstacles form a simple polygon P and X is inside P. Our algorithm runs in time O(m²n²), where m is the number of vertices of P and n is the number of points in X.     

On embedding an outer-planar graph in a point set

Given an n-vertex outer-planar graph G and a set P of n points in the plane, we present an O(nlog³n) time and O(n) space algorithm to compute a straight-line embedding of G in P, improving upon the algorithm in [8,12] that requires O(n²) time. Our algorithm is near-optimal as there is an Ω(nlogn) lower bound for the problem [4]. We present a simpler O(nd) time and O(n) space algorithm to compute a straight-line embedding of G in P where lognd2n is the length of the longest vertex disjoint path in the dual of G. Therefore, the time complexity of the simpler algorithm varies between O(nlogn) and O(n²) depending on the value of d. More efficient algorithms are presented for certain restricted cases. If the dual of G is a path, then an optimal Θ(nlogn) time algorithm is presented. If the given point set is in convex position then we show that O(n) time suffices.     

Resolutions of Stanley-Reisner rings and Alexander duality

Associated to any simplicial complex Δ on n vertices is a square-free monomial ideal I_Δ in the polynomial ring A = k[x₁, …, x_n], and its quotient k[Δ] = A/I_Δ known as the Stanley-Reisner ring. This note considers a simplicial complex Δ^* which is in a sense a canonical Alexander dual to Δ, previously considered in [1, 5]. Using Alexander duality and a result of Hochster computing the Betti numbers dim_k Tor_i^A (k[Δ],k), it is shown (Proposition 1) that these Betti numbers are computable from the homology of links of faces in Δ^*. As corollaries, we prove that I_Δ has a linear resolution as A-module if and only if Δ^* is Cohen-Macaulay over k, and show how to compute the Betti numbers dim_k Tor_i^A (k[Δ],k) in some cases where Δ^* is wellbehaved (shellable, Cohen-Macaulay, or Buchsbaum). Some other applications of the notion of shellability are also discussed.     

ContributionsAn algorithm for the Tutte polynomials of graphs of bounded treewidth

Let k be a fixed, positive integer. We give an algorithm which computes the Tutte polynomial of any graph G of treewidth at most k in time O(n^{2+7 log₂ c}), where c is twice the number of partitions of a set with 3k + 3 elements and n the number of vertices of G.     

Optimal fault-tolerant routings with small routing tables for k-connected graphs

We study the problem of designing fault-tolerant routings with small routing tables for a k-connected network of n processors in the surviving route graph model. The surviving route graph R(G,ρ)/F for a graph G, a routing ρ and a set of faults F is a directed graph consisting of nonfaulty nodes of G with a directed edge from a node x to a node y iff there are no faults on the route from x to y. The diameter of the surviving route graph could be one of the fault-tolerance measures for the graph G and the routing ρ and it is denoted by D(R(G,ρ)/F). We want to reduce the total number of routes defined in the routing, and the maximum of the number of routes defined for a node (called route degree) as least as possible. In this paper, we show that we can construct a routing λ for every n-node k-connected graph such that n2k², in which the route degree is , the total number of routes is O(k²n) and D(R(G,λ)/F)3 for any fault set F (|F|<k). In particular, in the case that k=2 we can construct a routing λ′ for every biconnected graph in which the route degree is , the total number of routes is O(n) and D(R(G,λ′)/{f})3 for any fault f. We also show that we can construct a routing ρ₁ for every n-node biconnected graph, in which the total number of routes is O(n) and D(R(G,ρ₁)/{f})2 for any fault f, and a routing ρ₂ (using ρ₁) for every n-node biconnected graph, in which the route degree is , the total number of routes is and D(R(G,ρ₂)/{f})2 for any fault f.     

Reporting curve segment intersections using restricted predicates

We investigate how to report all k intersecting pairs among a collection of n x-monotone curve segments in the plane, using only predicates of the following forms: is an endpoint to the left of another? is an endpoint above a segment? do two segments intersect? By studying the intersection problem in an abstract setting that assumes the availability of certain “detection oracles”, we obtain a near-optimal randomized algorithm that runs in expected time. In the bichromatic case (where segments are colored red or blue with no red/red or blue/blue intersections), we find a better algorithm that runs in O((n+k)log_2+k/nn) worst-case time, by modifying a known segment-tree method. Two questions of Boissonnat and Snoeyink are thus answered to within logarithmic factors.     

Efficient text fingerprinting via Parikh mapping

We consider the problem of fingerprinting text by sets of symbols. Specifically, if S is a string, of length n, over a finite, ordered alphabet Σ, and S′ is a substring of S, then the fingerprint of S′ is the subset φ of Σ of precisely the symbols appearing in S′. In this paper we show efficient methods of answering various queries on fingerprint statistics. Our preprocessing is done in time O(n|Σ|lognlog|Σ|) and enables answering the following queries:

(1)Given an integer k, compute the number of distinct fingerprints of size k in time O(1).

(2)Given a set φΣ, compute the total number of distinct occurrences in S of substrings with fingerprint φ in time O(|Σ|logn).

     

Fast approximations for sums of distances, clustering and the Fermat–Weber problem

We describe two data structures that preprocess a set S of n points in (d constant) so that the sum of Euclidean distances of points in S to a query point q can be quickly approximated to within a factor of . This preprocessing technique has several applications in clustering and facility location. Using it, we derive an O(nlogn) time deterministic and O(n) time randomized -approximation algorithm for the so called Fermat–Weber problem in any fixed dimension.     

Circular permutation graph family with applications

In a circular permutation diagram, there are two sets of terminals on two concentric circles: C_in and C_out. Given a permutation Π = [π₁, π₂, …, π_n], terminal i on C_in and terminal π_i on C_out are connected by a wire. The intersection graph G_c of a circular permutation diagram D_c is called a circular permutation graph of a permutation Π corresponding to the diagram D_c. The set of all circular permutation graphs of a permutation Π is called the circular permutation graph family of permutation Π. In this paper, we propose the following: (1) an O(V + E) time algorithm to check if a labeled graph G = (V, E) is a labeled circular permutation graph. (2) An O(n log n + nt) time algorithm to find a maximum independent set of a family, where n = Π and t is the cardinality of the output. (Number t in the worst case is O(n). However, if Π is uniformly distributed (and independent from i), its expected value is O(√n).) (3) An O(min(δV_clog logV_c,V_clogV_c) + E_c) time algorithm for finding a maximum independent set of a circular permutation diagram D_c, where δ is the minimum degree of vertices in the intersection graph G_c = (V_c,E_c) of D_c. (4) An O(n log log n) time algorithm for finding a maximum clique and the chromatic number of a circular permutation diagram, where n is the number of wires in the diagram.     

Approximate range searching

The range searching problem is a fundamental problem in computational geometry, with numerous important applications. Most research has focused on solving this problem exactly, but lower bounds show that if linear space is assumed, the problem cannot be solved in polylogarithmic time, except for the case of orthogonal ranges. In this paper we show that if one is willing to allow approximate ranges, then it is possible to do much better. In particular, given a bounded range Q of diameter w and >0, an approximate range query treats the range as a fuzzy object, meaning that points lying within distance w of the boundary of Q either may or may not be counted. We show that in any fixed dimension d, a set of n points in can be preprocessed in O(n+logn) time and O(n) space, such that approximate queries can be answered in O(logn(1/)^d) time. The only assumption we make about ranges is that the intersection of a range and a d-dimensional cube can be answered in constant time (depending on dimension). For convex ranges, we tighten this to O(logn+(1/)^d−1) time. We also present a lower bound for approximate range searching based on partition trees of Ω(logn+(1/)^d−1), which implies optimality for convex ranges (assuming fixed dimensions). Finally, we give empirical evidence showing that allowing small relative errors can significantly improve query execution times.     

A local search approximation algorithm for k-means clustering

In k-means clustering we are given a set of n data points in d-dimensional space and an integer k, and the problem is to determine a set of k points in  , called centers, to minimize the mean squared distance from each data point to its nearest center. No exact polynomial-time algorithms are known for this problem. Although asymptotically efficient approximation algorithms exist, these algorithms are not practical due to the very high constant factors involved. There are many heuristics that are used in practice, but we know of no bounds on their performance.

We consider the question of whether there exists a simple and practical approximation algorithm for k-means clustering. We present a local improvement heuristic based on swapping centers in and out. We prove that this yields a (9+)-approximation algorithm. We present an example showing that any approach based on performing a fixed number of swaps achieves an approximation factor of at least (9−) in all sufficiently high dimensions. Thus, our approximation factor is almost tight for algorithms based on performing a fixed number of swaps. To establish the practical value of the heuristic, we present an empirical study that shows that, when combined with Lloyd's algorithm, this heuristic performs quite well in practice.     

Minimal enclosing parallelepiped in 3D

We investigate the problem of finding a minimal volume parallelepiped enclosing a given set of n three-dimensional points. We give two mathematical properties of these parallelepipeds, from which we derive two algorithms of theoretical complexity O(n⁶). Experiments show that in practice our quickest algorithm runs in O(n²) (at least for n10⁵). We also present our application in structural biology.     

Equality of higher numerical ranges of matrices and a conjecture of Kippenhahn on Hermitian pencils

Let M_n be the algebra of all n × n complex matrices. For 1 k n, the kth numerical range of A M_n is defined by W_k(A) = (1/k)∑_j^k=1x_j^*Ax_j : x₁, …, x_k is an orthonormal set in ⁿ]. It is known that tr A/n = W_n(A) W_n−1(A) W₁(A). We study the condition on A under which W_m(A) = W_k(A) for some given 1 m < k n. It turns out that this study is closely related to a conjecture of Kippenhahn on Hermitian pencils. A new class of counterexamples to the conjecture is constructed, based on the theory of the numerical range.