A faster divide-and-conquer algorithm for constructing delaunay triangulations

An easily implemented modification to the divide-and-conquer algorithm for computing the Delaunay triangulation ofn sites in the plane is presented. The change reduces its (n logn) expected running time toO(n log logn) for a large class of distributions that includes the uniform distribution in the unit square. Experimental evidence presented demonstrates that the modified algorithm performs very well forn2¹⁶, the range of the experiments. It is conjectured that the average number of edges it creates—a good measure of its efficiency—is no more than twice optimal forn less than seven trillion. The improvement is shown to extend to the computation of the Delaunay triangulation in theL _p metric for 1<p.This research was supported by National Science Foundation Grants DCR-8352081 and DCR-8416190.     

A bidirectional shortest-path algorithm with good average-case behavior

The two-terminal shortest-path problem asks for the shortest directed path from a specified nodes to a specified noded in a complete directed graphG onn nodes, where each edge has a nonnegative length. We show that if the length of each edge is chosen independently from the exponential distribution, and adjacency lists at each node are sorted by length, then a priority-queue implementation of Dijkstra's unidirectional search algorithm has the expected running time (n logn). We present a bidirectional search algorithm that has expected running time (n logn). These results are generalized to apply to a wide class of edge-length distributions, and to sparse graphs. If adjacency lists are not sorted, bidirectional search has the expected running time (an) on graphs of average degreea, as compared with (an) for unidirectional search.     

Classical and Quantum Complexity of the Sturm–Liouville Eigenvalue Problem

We study the approximation of the smallest eigenvalue of a Sturm–Liouville problem in the classical and quantum settings. We consider a univariate Sturm–Liouville eigenvalue problem with a nonnegative function q from the class C² ([0,1]) and study the minimal number n() of function evaluations or queries that are necessary to compute an -approximation of the smallest eigenvalue. We prove that n()=(^–1/2) in the (deterministic) worst case setting, and n()=(^–2/5) in the randomized setting. The quantum setting offers a polynomial speedup with bit queries and an exponential speedup with power queries. Bit queries are similar to the oracle calls used in Grovers algorithm appropriately extended to real valued functions. Power queries are used for a number of problems including phase estimation. They are obtained by considering the propagator of the discretized system at a number of different time moments. They allow us to use powers of the unitary matrix exp((1/2) iM), where M is an n× n matrix obtained from the standard discretization of the Sturm–Liouville differential operator. The quantum implementation of power queries by a number of elementary quantum gates that is polylog in n is an open issue. In particular, we show how to compute an -approximation with probability (3/4) using n()=(^–1/3) bit queries. For power queries, we use the phase estimation algorithm as a basic tool and present the algorithm that solves the problem using n()=(log ^–1) power queries, log ^2–1 quantum operations, and (3/2) log ^–1 quantum bits. We also prove that the minimal number of qubits needed for this problem (regardless of the kind of queries used) is at least roughly (1/2) log ^–1. The lower bound on the number of quantum queries is proven in Bessen (in preparation). We derive a formula that relates the Sturm–Liouville eigenvalue problem to a weighted integration problem. Many computational problems may be recast as this weighted integration problem, which allows us to solve them with a polylog number of power queries. Examples include Grovers search, the approximation of the Boolean mean, NP-complete problems, and many multivariate integration problems. In this paper we only provide the relationship formula. The implications are covered in a forthcoming paper (in preparation).PACS: 03.67.Lx, 02.60.-x.     

A priority queue in which initialization and queue operations takeO(loglogD) time

Many computer algorithms have embedded in them a subalgorithm called a priority queue which produces on demand an element of extreme priority among elements in the queue. Queues on unrestricted priority domains have a running time of (nlogn) for sequences ofn queue operations. We describe a simple priority queue over the priority domain {1,,N} in which initialization, insertion, and deletion takeO(loglogD) time, whereD is the difference between the next lowest and next highest priority elements in the queue. In the case of initialization,D=(N). Finding a least element, greatest element, and the neighbor in priority order of some specified element take constant time. We also consider dynamic space allocation for the data structures used. Space can be allocated in blocks of size (N ^1/p ), for small integerp. This research was supported by the National Science Foundation under grants MCS 77-21092 and MCS 80-002684.     

Finding a closest visible vertex pair between two polygons

Given nonintersecting simple polygonsP andQ, two verticespP andq Q are said to be visible if does not properly intersectP orQ. We present a parallel algorithm for finding a closest pair among all visible pairs (p,q),pP andqQ. The algorithm runs in time O(logn) using O(n) processors on a CREW PRAM, wheren=¦P¦+¦Q¦. This algorithm can be implemented serially in (n) time, which gives a new optimal sequential solution for this problem.This paper appeared in preliminary form as [1]. This work was supported in part by an AT&T BellLaboratories Graduate Fellowship, the Joint Services Electronics Program (U.S. Army, U.S. Navy, U.S. Air Force) under Contract N00014-90-J-1270, and NSF Grant CCR-89-22008. This work was done while the author was with the Department of Computer Science at the University of Illinois at Urbana-Champaign.     

A 2n−2 step algorithm for routing in ann ×n array with constant-size queues

In this paper we describe a deterministic algorithm for solving any 1–1 packet-routing problem on ann ×n mesh in 2n–2 steps using constant-size queues. The time bound is optimal in the worst case. The best previous deterministic algorithm for this problem required time 2n+(n/q) using queues of size (q) for any 1qn, and the best previous randomized algorithm required time 2n+(logn) using constant-size queues.This research was supported by the Clear Center at UTD, DARPA Contracts N00014-91-J-1698 and N00014-92-J-1799, Air Force Contract F49620-92-J-0125, Army Contract DAAL-03-86-K-0171, an NSF Presidential Young Investigator Award with matching funds from AT&T and IBM, and by the Texas Advanced Research Program under Grant No. 3972. A preliminary version of this paper appeared in [5].     

An optimal time bound for oblivious routing

The problem of routing data packets in a constant-degree network is considered. A routing scheme is calledoblivious if the route taken by each packet is uniquely determined by its source and destination. The time required for the oblivious routing ofn packets onn processors is known to be (n). It is demonstrated that the presence of extra processors can expedite oblivious routing. More specifically, the time required for the oblivious routing ofn packets onp processors is (n/p + logn).     

A minimum-area circuit forl-selection

We prove tight upper and lower bounds on the area of semelective, when-oblivious VLSI circuits for the problem ofl-selection. The area required to select thelth smallest ofn k-bit integers is found to be heavily dependent on the relative sizes ofl,k, andn. Whenl<2 ^k , the minimal area isA = (minn,l(k-logl)). Whenl2 ^k ,A = (2 ^k (logl-k + 1)).This work was supported in part by National Science Foundation Grant DMC 84-06408 and by the Slovak Academy of Sciences.     

Geometric applications of a matrix-searching algorithm

LetA be a matrix with real entries and letj(i) be the index of the leftmost column containing the maximum value in rowi ofA.A is said to bemonotone ifi ₁ >i ₂ implies thatj(i ₁) J(i ₂).A istotally monotone if all of its submatrices are monotone. We show that finding the maximum entry in each row of an arbitraryn xm monotone matrix requires (m logn) time, whereas if the matrix is totally monotone the time is (m) whenmn and is (m(1 + log(n/m))) whenm<n. The problem of finding the maximum value within each row of a totally monotone matrix arises in several geometric algorithms such as the all-farthest-neighbors problem for the vertices of a convex polygon. Previously only the property of monotonicity, not total monotonicity, had been used within these algorithms. We use the (m) bound on finding the maxima of wide totally monotone matrices to speed up these algorithms by a factor of logn.On leave from the Technion, Haifa, Israel.     

A lower bound for sorting networks based on the shuffle permutation

We prove an (lg² n/lg lgn) lower bound for the depth ofn-input sorting networks based on the shuffle permutation. The best previously known lower bound was the trivial (lgn) bound, while the best upper bound is given by Batcher's (lg² n)-depth bitonic sorting network. The proof technique employed in the lower bound argument may be of independent interest.C. G. Plaxton was supported by NSF Research Initiation Award CCR-9111591, and the Texas Advanced Research Program under Grant No. 003658-480. T. Suel was supported by the Texas Advanced Research Program under Grant No. 003658-480, and by an MCD Fellowship of the University of Texas at Austin.     

Rational index of Vector Addition Systems languages

Summary The rational index _L of a non-empty language L is a function of into , whose asymptotic behavior can be used to classify languages. We prove that the languages associated to Vector Addition System or Petri nets have rational indexes bounded by polynomials. This situation should be contrasted with the case of context-free languages. Indeed some context-free languages like the Greibach's languages have rational indexes bounded by polynomials. But some other context-free languages have rational indexes in exp n and the generators of the rational cone of context-free languages have rational indexes in exp n ²/ln n. We give an upper bound and a lower bound on the rational index of each term of an infinite sequence of V.A.S. languages, such that any V.A.S. language can be obtained as the image by a rational transduction of one of these languages.     

Parallel integer sorting using small operations

We consider the problem of sortingn integers in the range [0,n ^c -1], wherec is a constant. It has been shown by Rajasekaran and Sen [14] that this problem can be solved optimally inO(logn) steps on an EREW PRAM withO(n) n -bit operations, for any constant >O. Though the number of operations is optimal, each operation is very large. In this paper, we show thatn integers in the range [0,n ^c -1] can be sorted inO(logn) time withO(nlogn)O(1)-bit operations andO(n) O(logn)-bit operations. The model used is a non-standard variant of an EREW PRAMtthat permits processors to have word-sizes ofO(1)-bits and (logn)-bits. Clearly, the speed of the proposed algorithm is optimal. Considering that the input to the problem consists ofO (n logn) bits, the proposed algorithm performs an optimal amount of work, measured at the bit level.This work was partially supported by The Northeast Parallel Architectures Center (NPAC) at Syracuse University, Syracuse, NY 13244 and The Rome Air Development Center, under contract F30602-88-D-0027.     

Parallel construction of a suffix tree with applications

Many string manipulations can be performed efficiently on suffix trees. In this paper a CRCW parallel RAM algorithm is presented that constructs the suffix tree associated with a string ofn symbols inO(logn) time withn processors. The algorithm requires (n ²) space. However, the space needed can be reduced toO(n ¹⁺) for any 0< 1, with a corresponding slow-down proportional to 1/. Efficient parallel procedures are also given for some string problems that can be solved with suffix trees.The results of this paper have been achieved independently and simultaneously in [AI-86] and [LSV-86]. The research of U. Vishkin was supported by NSF Grant NSF-CCR-8615337, ONR Grant N00014-85-K-0046, and Foundation for Research in Electronics, Computers, and Communication, administered by the Israeli Academy of Sciences and Humanities. The research of A. Apostolico was carried out in part while visiting at the Istituto di Analisi dei Sistemi e Informatica, Rome, with support from the Italian National Research Council. The research of G. M. Landau, B. Schieber, and U. Vishkin was supported by the Applied Mathematical Sciences subprogram of the Office of Energy Research, U.S. Department of Energy under Contract DE-AC02-76ER03077.     

Robot Awareness in Cooperative Mobile Robot Learning

Most of the straight-forward learning approaches in cooperative robotics imply for each learning robot a state space growth exponential in the number of team members. To remedy the exponentially large state space, we propose to investigate a less demanding cooperation mechanism—i.e., various levels of awareness—instead of communication. We define awareness as the perception of other robots locations and actions. We recognize four different levels (or degrees) of awareness which imply different amounts of additional information and therefore have different impacts on the search space size ((0), (1), (N), o(N),¹ where N is the number of robots in the team). There are trivial arguments in favor of avoiding binding the increase of the search space size to the number of team members. We advocate that, by studying the maximum number of neighbor robots in the application context, it is possible to tune the parameters associated with a (1) increase of the search space size and allow good learning performance. We use the cooperative multi-robot observation of multiple moving targets (CMOMMT) application to illustrate our method. We verify that awareness allows cooperation, that cooperation shows better performance than a purely collective behavior and that learned cooperation shows better results than learned collective behavior.     

Finding all periods and initial palindromes of a string in parallel

An optimalO(log logn)-time CRCW-PRAM algorithm for computing all period lengths of a string is presented. Previous parallel algorithms compute the period only if it is shorter than half of the length of the string. The algorithm can be used to find all initial palindromes of a string in the same time and processor bounds. Both algorithms are the fastest possible over a general alphabet. We derive a lower bound for finding initial palindromes by modifying a known lower bound for finding the period length of a string [9]. Whenp processors are available the bounds become (n/p+log_1+p/n2p).This work was partially supported by NSF Grant CCR-90-14605. D. Breslauer was partially supported by an IBM Graduate Fellowship while studying at Columbia University and by a European Research Consortium for Informatics and Mathematics postdoctoral fellowship.     

Visibility,occlusion, and the aspect graph

This chapter studies the ways in which the topology of the image of a polyhedron changes with changing viewpoint. We catalog the ways that the topological appearance, or aspect, can change. This enables us to find maximal regions of viewpoints of the same aspect. We use these techniques to construct the viewpoint space partition (VSP), a partition of viewpoint space into maximal regions of constant aspect, and its dual, the aspect graph. Here, we present tight bounds on the maximum size of the VSP and the aspect graph and give algorithms for their construction, first in the convex case and then in the general case. In particular, we give bounds on the maximum size of (n ²) and (n ⁶) under an orthographic projection viewing model and of (n ³) and (n ⁹) under a perspective viewing model. The algorithms make use of a new representation of the appearance of polyhedra from all viewpoints, called the aspect representation or asp. We believe that this representation is one of the significant contributions of this paper.This work was supported in part by the NSF under grants DCR-8520870 and IRI-8802436.     

Efficient parallel recognition of some circular arc graphs,I

We present the first efficient parallel algorithms for recognizing some subclasses of circular arc graphs including circular arc graphs and proper interval graphs. These algorithms run in O(log² n) time withO(n ³) processors on a CRCW PRAM. An intersection representation can also be constructed within the same resource bounds. Furthermore, we propose some new characterizations of circular arc graphs and proper interval graphs.Portions of this paper have appeared in preliminary form in theProceedings of the 1989 IEEE international Symposium on Circuits and Systems [9], theProceedings of the 1989 Workshop on Algorithms and Data Structures [10], and theProceedings of the 1990 Canadian Conference on Computational Geometry [11].     

Simultaneous inner and outer approximation of shapes

For compact Euclidean bodiesP, Q, we define (P, Q) to be the smallest ratior/s wherer > 0,s > 0 satisfy . HeresQ denotes a scaling ofQ by the factors, andQ,Q are some translates ofQ. This function gives us a new distance function between bodies which, unlike previously studied measures, is invariant under affine transformations. If homothetic bodies are identified, the logarithm of this function is a metric. (Two bodies arehomothetic if one can be obtained from the other by scaling and translation.)For integerk 3, define (k) to be the minimum value such that for each convex polygonP there exists a convexk-gonQ with (P, Q) (k). Among other results, we prove that 2.118 ... <-(3) 2.25 and (k) = 1 + (k ^–2). We give anO(n ² log² n)-time algorithm which, for any input convexn-gonP, finds a triangleT that minimizes (T, P) among triangles. However, in linear time we can find a trianglet with (t, P)<-2.25.Our study is motivated by the attempt to reduce the complexity of the polygon containment problem, and also the motion-planning problem. In each case we describe algorithms which run faster when certain implicitslackness parameters of the input are bounded away from 1. These algorithms illustrate a new algorithmic paradigm in computational geometry for coping with complexity.Work of all authors was partially supported by the ESPRIT II Basic Research Actions Program of the EC under Contract No. 3075 (project ALCOM). Rudolf Fleischer and Kurt Mehlhorn acknowledge also DFG (Grant SPP Me 620/6). Chee Yap acknowledges also DFG (Grant Be 142/46-1) and NSF (Grants DCR-84-01898 and CCR-87-03458). This research was performed when Günter Rote and Chee Yap were at the Freie Universität Berlin.     

Parallel general prefix computations with geometric,algebraic, and other applications

We introduce a generic problem component that captures the most common, difficult kernel of many problems. This kernel involves general prefix computations (GPC). GPC's lower bound complexity of (n logn) time is established, and we give optimal solutions on the sequential model inO(n logn) time, on the CREW PRAM model inO(logn) time, on the BSR (broadcasting with selective reduction) model in constant time, and on mesh-connected computers inO(n) time, all withn processors, plus anO(log² n) time solution on the hypercube model. We show that GPC techniques can be applied to a wide variety of geometric (point set and tree) problems, including triangulation of point sets, two-set dominance counting, ECDF searching, finding two-and three-dimensional maximal points, the reconstruction of trees from their traversals, counting inversions in a permutation, and matching parentheses.work partially supported by NSF IRI/8709726work partially supported by NSERC.     

Primitives for asynchronous list compression

In this paper we study the problem of asynchronous processors traversing a list with path compression. We show that if an atomic splice operation is available, the worst-case work forp processors traversing a list of length h is (np ^1/2). The splice operation can be generalized to removek elements from the list. For thek-splice operation the worst-case work is (np ^1/ k+1).This research was supported by an NSF Presidential Young Investigator Award CCR-8657562, Digital Equipment Corporation, NSF CER Grant CCR-861966, and NSF/Darpa Grant CCR-8907960. A preliminary version of this paper was presented at the Fourth Annual ACM Symposium on Parallel Algorithms and Architectures.