Analysis of approximate nearest neighbor searching with clustered point sets

We present an empirical analysis of data structures for approximate nearest neighbor searching. We compare the well-known optimized kd-tree splitting method against two alternative splitting methods. The first, called the sliding-midpoint method, which attempts to balance the goals of producing subdivision cells of bounded aspect ratio, while not producing any empty cells. The second, called the minimum-ambiguity method is a query-based approach. In addition to the data points, it is also given a training set of query points for preprocessing. It employs a simple greedy algorithm to select the splitting plane that minimizes the average amount of ambiguity in the choice of the nearest neighbor for the training points. We provide an empirical analysis comparing these two methods against the optimized kd-tree construction for a number of synthetically generated data and query sets. We demonstrate that for clustered data and query sets, these algorithms can provide significant improvements over the standard kd-tree construction for approximate nearest neighbor searching.Comment: 20 pages, 8 figures. Presented at ALENEX '99, Baltimore, MD, Jan 15-16, 199

On the Combinatorial Complexity of Approximating Polytopes

Approximating convex bodies succinctly by convex polytopes is a fundamental problem in discrete geometry. A convex body $K$ of diameter $\mathrm{diam}(K)$ is given in Euclidean $d$-dimensional space, where $d$ is a constant. Given an error parameter $\varepsilon > 0$, the objective is to determine a polytope of minimum combinatorial complexity whose Hausdorff distance from $K$ is at most $\varepsilon \cdot \mathrm{diam}(K)$. By combinatorial complexity we mean the total number of faces of all dimensions of the polytope. A well-known result by Dudley implies that $O(1/\varepsilon^{(d-1)/2})$ facets suffice, and a dual result by Bronshteyn and Ivanov similarly bounds the number of vertices, but neither result bounds the total combinatorial complexity. We show that there exists an approximating polytope whose total combinatorial complexity is $\tilde{O}(1/\varepsilon^{(d-1)/2})$, where $\tilde{O}$ conceals a polylogarithmic factor in $1/\varepsilon$. This is a significant improvement upon the best known bound, which is roughly $O(1/\varepsilon^{d-2})$. Our result is based on a novel combination of both old and new ideas. First, we employ Macbeath regions, a classical structure from the theory of convexity. The construction of our approximating polytope employs a new stratified placement of these regions. Second, in order to analyze the combinatorial complexity of the approximating polytope, we present a tight analysis of a width-based variant of B\'{a}r\'{a}ny and Larman's economical cap covering. Finally, we use a deterministic adaptation of the witness-collector technique (developed recently by Devillers et al.) in the context of our stratified construction.Comment: In Proceedings of the 32nd International Symposium Computational Geometry (SoCG 2016) and accepted to SoCG 2016 special issue of Discrete and Computational Geometr

On orbital allotments for geostationary satellites

The following satellite synthesis problem is addressed: communication satellites are to be allotted positions on the geostationary arc so that interference does not exceed a given acceptable level by enforcing conservative pairwise satellite separation. A desired location is specified for each satellite, and the objective is to minimize the sum of the deviations between the satellites' prescribed and desired locations. Two mixed integer programming models for the satellite synthesis problem are presented. Four solution strategies, branch-and-bound, Benders' decomposition, linear programming with restricted basis entry, and a switching heuristic, are used to find solutions to example synthesis problems. Computational results indicate the switching algorithm yields solutions of good quality in reasonable execution times when compared to the other solution methods. It is demonstrated that the switching algorithm can be applied to synthesis problems with the objective of minimizing the largest deviation between a prescribed location and the corresponding desired location. Furthermore, it is shown that the switching heuristic can use no conservative, location-dependent satellite separations in order to satisfy interference criteria

Delaunay triangulation and computational fluid dynamics meshes

In aerospace computational fluid dynamics (CFD) calculations, the Delaunay triangulation of suitable quadrilateral meshes can lead to unsuitable triangulated meshes. Here, we present case studies which illustrate the limitations of using structured grid generation methods which produce points in a curvilinear coordinate system for subsequent triangulations for CFD applications. We discuss conditions under which meshes of quadrilateral elements may not produce a Delaunay triangulation suitable for CFD calculations, particularly with regard to high aspect ratio, skewed quadrilateral elements

Implementation of linear minimum area enclosing traingle algorithm

This article has been made available through the Brunel Open Access Publishing Fund.An algorithm which computes the minimum area triangle enclosing a convex polygon in linear time already exists in the literature. The paper describing the algorithm also proves that the provided solution is optimal and a lower complexity sequential algorithm cannot exist. However, only a high-level description of the algorithm was provided, making the implementation difficult to reproduce. The present note aims to contribute to the field by providing a detailed description of the algorithm which is easy to implement and reproduce, and a benchmark comprising 10,000 variable sized, randomly generated convex polygons for illustrating the linearity of the algorithm

Space Exploration via Proximity Search

We investigate what computational tasks can be performed on a point set in $\Re^d$, if we are only given black-box access to it via nearest-neighbor search. This is a reasonable assumption if the underlying point set is either provided implicitly, or it is stored in a data structure that can answer such queries. In particular, we show the following: (A) One can compute an approximate bi-criteria $k$-center clustering of the point set, and more generally compute a greedy permutation of the point set. (B) One can decide if a query point is (approximately) inside the convex-hull of the point set. We also investigate the problem of clustering the given point set, such that meaningful proximity queries can be carried out on the centers of the clusters, instead of the whole point set

Online Algorithms for Warehouse Management

As the prevalence of E-commerce continues to grow, the efficient operation of warehouses and fulfillment centers is becoming increasingly important. To this end, many such warehouses are adding automation in order to help streamline operations, drive down costs, and increase overall efficiency. The introduction of automation comes with the opportunity for new theoretical models and computational problems with which to better understand and optimize such systems. These systems often maintain a warehouse of standardized portable storage units, which are stored and retrieved by robotic workers. In general, there are two principal issues in optimizing such a system: where in the warehouse each storage unit should be located and how best to retrieve them. These two concerns naturally go hand-in-hand, but are further complicated by the unknown request frequencies of stored products. Analogous to virtual-memory systems, the more popular and oft-requested an item is, the more efficient its retrieval should be. In this paper, we propose a theoretical model for organizing portable storage units in a warehouse subject to an online sequence of access requests. We consider two formulations, depending on whether there is a single access point or multiple access points. We present algorithms that are O(1)-competitive with respect to an optimal algorithm. In the case of a single access point, our solution is also asymptotically optimal with respect to density

Approximate Geometric MST Range Queries

Range searching is a widely-used method in computational geometry for efficiently accessing local regions of a large data set. Typically, range searching involves either counting or reporting the points lying within a given query region, but it is often desirable to compute statistics that better describe the structure of the point set lying within the region, not just the count. In this paper we consider the geometric minimum spanning tree (MST) problem in the context of range searching where approximation is allowed. We are given a set P of n points in R^d. The objective is to preprocess P so that given an admissible query region Q, it is possible to efficiently approximate the weight of the minimum spanning tree of the subset of P lying within Q. There are two natural sources of approximation error, first by treating Q as a fuzzy object and second by approximating the MST weight itself. To model this, we assume that we are given two positive real approximation parameters eps_q and eps_w. Following the typical practice in approximate range searching, the range is expressed as two shapes Q^- and Q^+, where Q^- is contained in Q which is contained in Q^+, and their boundaries are separated by a distance of at least eps_q diam(Q). Points within Q^- must be included and points external to Q^+ cannot be included. A weight W is a valid answer to the query if there exist subsets P\u27 and P\u27\u27 of P, such that Q^- is contained in P\u27 which is contained in P\u27\u27 which is contained in Q^+ and wt(MST(P\u27)) <= W <= (1+eps_w) wt(MST(P\u27\u27)). In this paper, we present an efficient data structure for answering such queries. Our approach uses simple data structures based on quadtrees, and it can be applied whenever Q^- and Q^+ are compact sets of constant combinatorial complexity. It uses space O(n), and it answers queries in time O(log n + 1/(eps_q eps_w)^{d + O(1)}). The O(1) term is a small constant independent of dimension, and the hidden constant factor in the overall running time depends on d, but not on eps_q or eps_w. Preprocessing requires knowledge of eps_w, but not eps_q

An investigation of screening for medical referral by Physical Therapists

Screening for medical referral is essential to physical therapist practice; however, no studies have examined medical screening across physical therapy practice settings. The purpose of this study was to determine if physical therapists in a variety of practice settings, given brief clinical vignettes, would screen for medical referral in a similar manner. The Delphi Technique was implemented to establish content validity, although only 2 vignettes reached 100% consensus by the expert panel. After reviewing the vignettes, physical therapist subjects determined if they would provide intervention, provide intervention and refer, or refer before intervention. To determine which variables were associated with identified appropriate decisions in each category, 4 sets of logistic regressions were performed. A random sample of APTA members in targeted sections (n=214) completed the survey. Physical therapists with more than 23 years experience were 7 times more likely to make similar management decisions in noncritical vignettes then those with 11 years or less. Half of the participants practiced in outpatient practice settings. In critical medical vignettes, 86% of physical therapists made similar decisions. Physical therapists in this study made identified appropriate management decisions for 90% of the vignettes. Board certification, practice setting and highest earned degree were not found to be statistically significant. It is imperative not only to ensure that the client is appropriate for physical therapy intervention, but it is also vital to recognize and identify signs and symptoms that warrant evaluation by other health care providers