Further Results on Generalized Intersection Searching Problems: Counting,Reporting, and Dynamization

In a generalized intersection searching problem, a set, S, of colored geometric objects is to be preprocessed so that given some query object, q, the distinct colors of the objects intersected by q can be reported efficiently or the number of such colors can be counted efficiently. In the dynamic setting, colored objects can be inserted into or deleted from S. These problems generalize the well-studied standard intersection searching problems and are rich in applications. Unfortunately, the techniques known for the standard problems do not yield efficient solutions for the generalized problems. Moreover, previous work (R. Janardan and M. Lopez, Generalized intersection searching problems, Internat. J. Comput. Geom. Appl.3 (1993), 39-69) on generalized problems applies only to the static reporting problems. In this paper, a uniform framework is presented to solve efficiently the counting/reporting versions of a variety of generalized intersection searching problems in static/dynamic settings. These problems include 1-, 2-, and 3-dimensional range searching, quadrant searching, interval intersection searching, 1- and 2-dimensional point enclosure searching, and orthogonal segment intersection searching.     

Matching subsequences in trees

Given two rooted, labeled trees P and T the tree path subsequence problem is to determine which paths in P are subsequences of which paths in T. Here a path begins at the root and ends at a leaf. In this paper we propose this problem as a useful query primitive for XML data, and provide new algorithms improving the previously best known time and space bounds.     

Viscosity methods for common solutions for equilibrium and hierarchical fixed point problems

Implicit and explicit viscosity methods for finding common solutions of equilibrium and hierarchical fixed points are presented. These methods are used to solve systems of equilibrium problems and variational inequalities where the involving operators are complements of nonexpansive mappings. The results here are situated on the lines of the research of the corresponding results of Moudafi [Krasnoselski-Mann iteration for hierarchical fixed-point problems, Inverse Probl. 23 (2007), pp. 1635–1640; Weak convergence theorems for nonexpansive mappings and equilibrium problems, to appear in JNCA], Moudafi and Maingé [Towards viscosity approximations of hierarchical fixed-points problems, Fixed Point Theory Appl. Art ID 95453 (2006), 10 pp.; Strong convergence of an iterative method for hierarchical fixed point problems, Pac. J. Optim. 3 (2007), pp. 529–538; Coupling viscosity methods with the extragradient algorithm for solving equilibrium problems, to appear in JNCA], Yao and Liou [Weak and strong convergence of Krasnosel'ski?–Mann iteration for hierarchical fixed point problems, Inverse Probl. 24 (2008), 015015 8 pp.], S. Takahashi and W. Takahashi [Viscosity approximation methods for equilibrium problems and fixed point problems in Hilbert spaces, J. Math. Anal. Appl. 331 (2006), pp. 506–515], Xu [Viscosity method for hierarchical fixed point approach to variational inequalities, preprint.], Combettes and Hirstoaga [Equilibrium programming in Hilbert spaces, J. Nonlinear Convex Anal. 6 (2005), pp. 117–136] and Plubtieng and Pumbaeang [A general iterative method for equilibrium problems and fixed point problems in Hilbert spaces, J. Math. Anal. Appl. 336 (2007), pp. 455–469.].     

Linear space data structures for two types of range search

This paper investigates the existence of linear space data structures for range searching. We examine thehomothetic range search problem, where a setS ofn points in the plane is to be preprocessed so that for any triangleT with sides parallel to three fixed directions the points ofS that lie inT can be computed efficiently. We also look atdomination searching in three dimensions. In this problem,S is a set ofn points inE ³ and the question is to retrieve all points ofS that are dominated by some query point. We describe linear space data structures for both problems. The query time is optimal in the first case and nearly optimal in the second.This research was conducted while the first author was with Brown University and the second author was with the Technical University of Graz, Austria. The first author was supported in part by NSF Grant MCS 83-03925.     

Quasi-optimal range searching in spaces of finite VC-dimension

The range-searching problems that allow efficient partition trees are characterized as those defined by range spaces of finite Vapnik-Chervonenkis dimension. More generally, these problems are shown to be the only ones that admit linear-size solutions with sublinear query time in the arithmetic model. The proof rests on a characterization of spanning trees with a low stabbing number. We use probabilistic arguments to treat the general case, but we are able to use geometric techniques to handle the most common range-searching problems, such as simplex and spherical range search. We prove that any set ofn points inE ^d admits a spanning tree which cannot be cut by any hyperplane (or hypersphere) through more than roughlyn ^1–1/d edges. This result yields quasi-optimal solutions to simplex range searching in the arithmetic model of computation. We also look at polygon, disk, and tetrahedron range searching on a random access machine. Givenn points inE ², we derive a data structure of sizeO(n logn) for counting how many points fall inside a query convexk-gon (for arbitrary values ofk). The query time isO(kn logn). Ifk is fixed once and for all (as in triangular range searching), then the storage requirement drops toO(n). We also describe anO(n logn)-size data structure for counting how many points fall inside a query circle inO(n log² n) query time. Finally, we present anO(n logn)-size data structure for counting how many points fall inside a query tetrahedron in 3-space inO(n ^2/3 log² n) query time. All the algorithms are optimal within polylogarithmic factors. In all cases, the preprocessing can be done in polynomial time. Furthermore, the algorithms can also handle reporting within the same complexity (adding the size of the output as a linear term to the query time).Portions of this work have appeared in preliminary form in Partition trees for triangle counting and other range searching problems (E. Welzl),Proc. 4th Ann. ACM Symp. Comput. Geom. (1988), 23–33, and Tight Bounds on the Stabbing Number of Spanning Trees in Euclidean Space (B. Chazelle), Comput. Sci. Techn. Rep. No. CS-TR-155-88, Princeton University, 1988. Bernard Chazelle acknowledges the National Science Foundation for supporting this research in part under Grant CCR-8700917. Emo Welzl acknowledges the Deutsche Forschungsgemeinschaft for supporting this research in part under Grant We 1265/1-1.     

Removing Degeneracy in LP-Type Problems Revisited

LP-type problems is a successful axiomatic framework for optimization problems capturing, e.g., linear programming and the smallest enclosing ball of a point set. In Matoušek and Škovroň (Theory Comput. 3:159–177, 2007), it is proved that in order to remove degeneracies of an LP-type problem, we sometimes have to increase its combinatorial dimension by a multiplicative factor of at least 1+ε with a certain small positive constant ε. The proof goes by checking the unsolvability of a system of linear inequalities, with several pages of calculations. Here by a short topological argument we prove that the dimension sometimes has to increase at least twice. We also construct 2-dimensional LP-type problems with −∞ for which removing degeneracies forces arbitrarily large dimension increase.     

Violator spaces: Structure and algorithms

Sharir and Welzl introduced an abstract framework for optimization problems, called LP-type problems or also generalized linear programming problems, which proved useful in algorithm design. We define a new, and as we believe, simpler and more natural framework: violator spaces, which constitute a proper generalization of LP-type problems. We show that Clarkson's randomized algorithms for low-dimensional linear programming work in the context of violator spaces. For example, in this way we obtain the fastest known algorithm for the P-matrix generalized linear complementarity problem with a constant number of blocks. We also give two new characterizations of LP-type problems: they are equivalent to acyclic violator spaces, as well as to concrete LP-type problems (informally, the constraints in a concrete LP-type problem are subsets of a linearly ordered ground set, and the value of a set of constraints is the minimum of its intersection).     

Algorithms for generalized halfspace range searching and other intersection searching problems

In a generalized intersection searching problem, a set S of colored geometric objects is to be preprocessed so that, given a query object q, the distinct colors of the objects of S that are intersected by q can be reported or counted efficiently. These problems generalize the well-studied standard intersection searching problems and have many applications. Unfortunately, the solutions known for the standard problems do not yield efficient solutions to the generalized problems. Recently, efficient solutions have been given for generalized problems where the input and query objects are iso-oriented (i.e., axes-parallel) or where the color classes satisfy additional properties (e.g., connectedness). In this paper, efficient algorithms are given for several generalized problems involving objects that are not necessarily iso-oriented. These problems include: generalized halfspace range searching in , for any fixed d ≥ 2, and segment intersection searching, triangle stabbing, and triangle range searching in for certain classes of line segments and triangles. The techniques used include: computing suitable sparse representations of the input, persistent data structures, and filtering search.     

Existence results for hemivariational inequalities with measures

We prove existence results for multivalued quasilinear elliptic problems of hemivariational inequality type with measure data right-hand sides. In case of L ¹-data, we study existence and enclosure behaviors of solutions by an appropriate sub-supersolution approach. The proofs of our results are based on general existence theory for multivalued pseudomonotone operators, and approximation-, truncation-, and special test function techniques.     

Cache-Oblivious Range Reporting with Optimal Queries Requires Superlinear Space

We consider a number of range reporting problems in two and three dimensions and prove lower bounds on the amount of space used by any cache-oblivious data structure for these problems that achieves the optimal query bound of O(log  _B N+K/B) block transfers, where K is the size of the query output.     

Effective Search Problems

The task of computing a function F with the help of an oracle X can be viewed as a search problem where the cost measure is the number of queries to X. We ask for the minimal number that can be achieved by a suitable choice of X and call this quantity the query complexity of F. This concept is suggested by earlier work of Beigel, Gasarch, Gill, and Owings on “Bounded query classes”. We introduce a fault tolerant version and relate it with Ulam's game. For many natural classes of functions F we obtain tight upper and lower bounds on the query complexity of F. Previous results like the Nonspeedup Theorem and the Cardinality Theorem appear in a wider perspective. Mathematics Subject Classification: 03D20, 68Q15, 68R05.     

Compressing spatio-temporal trajectories

A trajectory is a sequence of locations, each associated with a timestamp, describing the movement of a point. Trajectory data is becoming increasingly available and the size of recorded trajectories is getting larger. In this paper we study the problem of compressing planar trajectories such that the most common spatio-temporal queries can still be answered approximately after the compression has taken place. In the process, we develop an implementation of the Douglas–Peucker path-simplification algorithm which works efficiently even in the case where the polygonal path given as input is allowed to self-intersect. For a polygonal path of size n, the processing time is O(nlog^kn) for k=2 or k=3 depending on the type of simplification.     

Farthest-point queries with geometric and combinatorial constraints

In this paper we discuss farthest-point problems in which a set or sequence S of n points in the plane is given in advance and can be preprocessed to answer various queries efficiently. First, we give a data structure that can be used to compute the point farthest from a query line segment in O(log²n) time. Our data structure needs O(nlogn) space and preprocessing time. To the best of our knowledge no solution to this problem has been suggested yet. Second, we show how to use this data structure to obtain an output-sensitive query-based algorithm for polygonal path simplification. Both results are based on a series of data structures for fundamental farthest-point queries that can be reduced to each other.     

Cache Oblivious Algorithms for the RMQ and the RMSQ Problems

In the Range Minimum/Maximum Query (RMQ) and Range Maximum-Sum Segment Query (RMSQ) problems, we are given an array which we can preprocess in order to answer subsequent queries. In the RMQ query, we are given a range on the array and we need to find the maximum/minimum element within that range. On the other hand, in RMSQ query, we need to return the segment within the given query range that gives the maximum sum. In this paper, we present cache oblivious optimal algorithms for both of the above problems. In particular, for both the problems, we have presented linear time data structures having optimal cache miss. The data structures can answer the corresponding queries in constant time with constant cache miss.     

Extremal point queries with lines and line segments and related problems

We address a number of extremal point query problems when P is a set of n points in , d3 a constant, including the computation of the farthest point from a query line and the computation of the farthest point from each of the lines spanned by the points in P. In , we give a data structure of size O(n¹⁺), that can be constructed in O(n¹⁺) time and can report the farthest point of P from a query line segment in O(n^2/3+) time, where >0 is an arbitrarily small constant. Applications of our results also include: (1) Sub-cubic time algorithms for fitting a polygonal chain through an indexed set of points in , d3 a constant, and (2) A sub-quadratic time and space algorithm that, given P and an anchor point q, computes the minimum (maximum) area triangle defined by q with P{q}.     

On the dynamization of data structures

In this paper we present a simple dynamization method that preserves the query and storage costs of a static data structure and ensures reasonable update costs. In this method, the majority of data elements are maintained in a single data structure, and the updates are handled using smaller auxiliary data structures. We analyze the query, storage, and amortized update costs for the dynamic version of a static data structure in terms of a functionf, such thatf(n)<n, that bounds the sizes of the auxiliary data structures (wheren is the number of elements in the data structure). The conditions onf for minimal (with respect to asymptotic upper bounds) amortized update costs are then obtained. The proposed method is shown to be particularly suited for the cases where the merging of two data structures is more efficient than building the resultant data structure from scratch. Its effectiveness is illustrated by applying it to a class of data structures that have linear merging cost; this class consists of data structures such as Voronoi diagrams, K-d trees, quadtrees, multiple attribute trees, etc.     

Design,planning, scheduling,and control problems of flexible manufacturing systems

The design and use of flexible manufacturing systems (FMSs) involve some intricate operations research problems.FMS design problems include, for example, determining the appropriate number of machine tools of each type, the capacity of the material handling system, and the size of buffers.FMS planning problems include the determination of which parts should be simultaneously machined, the optimal partition of machine tools into groups, allocations of pallets and fixtures to part types, and the assignment of operations and associated cutting tools among the limited-capacity tool magazines of the machine tools.FMS scheduling problems include determining the optimal input sequence of parts and an optimal sequence at each machine tool given the current part mix.FMS control problems are those concerned with, for example, monitoring the system to be sure that requirements and due dates are being met and that unreliability problems are taken care of. This paper defines and describes these FMS problems in detail for OR/MS researchers to work on.     

Range searching with efficient hierarchical cuttings

We present an improved space/query-time tradeoff for the general simplex range searching problem, matching known lower bounds up to small polylogarithmic factors. In particular, we construct a linear-space simplex range searching data structure withO(n ^1−1/d ) query time, which is optimal ford=2 and probably also ford>2. Further, we show that multilevel range searching data structures can be built with only a polylogarithmic overhead in space and query time per level (the previous solutions require at least a small fixed power ofn). We show that Hopcroft's problem (detecting an incidence amongn lines andn points) can be solved in time . In all these algorithms we apply Chazelle's results on computing optimal cuttings. Part of this research was done during the First Utrecht Computational Geometry Workshop, supported by the Dutch Organization for Scientific Research (NWO).     

Off-Line Maintenance of Planar Configurations

We achieve anO(log n) amortized time bound per operation for the off-line version of the dynamic convex hull problem in the plane. In this problem, a sequence ofninsert,delete, andqueryinstructions are to be processed, where each insert instruction adds a new point to the set, each delete instruction removes an existing point, and each query requests a standard convex hull search. We process the entire sequence in totalO(n log n) time andO(n) space. Alternatively, we can preprocess a sequence ofninsertions and deletions inO(n log n) time and space, then answer queries in history inO(log n) time apiece (a query in history means a query comes with a time parametert, and it must be answered with respect to the convex hull present at timet). The same bounds also hold for the off-line maintenance of several related structures, such as the maximal vectors, the intersection of half-planes, and the kernel of a polygon. Achieving anO(log n) per-operation time bound for theon-lineversions of these problems is a longstanding open problem in computational geometry.