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.     

Shallow circuits and concise formulae for multiple addition and multiplication

A theory is developed for the construction of carry-save networks with minimal delay, using a given collection of carry-save adders each of which may receive inputs and produce outputs using several different representation standards.The construction of some new carry-save adders is described. Using these carry-save adders optimally, as prescribed by the above theory, we get {, , }-circuits of depth 3.48 log₂ n and {, , }-circuits of depth 4.95 log₂ n for the carry-save addition ofn numbers of arbitrary length. As a consequence we get multiplication circuits of the same depth. These circuits put out two numbers whose sum is the result of the multiplication. If a single output number is required then the depth of the multiplication circuits increases respectively to 4.48 log₂ n and 5.95 log₂ n.We also get {, , }-formulae of sizeO (n ^3.13) and {, }-formulae of sizeO (n ^4.57) for all the output bits of a carry-save addition ofn numbers. As a consequence we get formulae of the same size for the majority function and many other symmetric Boolean functions.     

An optimal parallel algorithm for planar cycle separators

We present an optimal parallel algorithm for computing a cycle separator of ann-vertex embedded planar undirected graph inO(logn) time onn/logn processors. As a consequence, we also obtain an improved parallel algorithm for constructing a depth-first search tree rooted at any given vertex in a connected planar undirected graph in O(log² n) time on n/logn processors. The best previous algorithms for computing depth-first search trees and cycle separators achieved the same time complexities, but withn processors. Our algorithms run on a parallel random access machine that permits concurrent reads and concurrent writes in its shared memory and allows an arbitrary processor to succeed in case of a write conflict.A preliminary version of this paper appeared as Improved Parallel Depth-First Search in Undirected Planar Graphs in theProceedings of the Third Workshop on Algorithms and Data Structures, 1993, pp. 407–420.Supported in part by NSF Grant CCR-9101385.     

The liberalized δ-rule in free variable semantic tableaux

In this paper we have a closer look at one of the rules of the tableau calculus presented by Fitting [4], called the -rule. We prove that a modification of this rule, called the ⁺-rule, which uses fewer free variables, is also sound and complete. We examine the relationship between the ⁺-rule and variations of the -rule presented by Smullyan [9]. This leads to a second proof of the soundness of the ⁺-rule. An example shows the relevance of this modification for building tableau-based theorem provers.     

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.     

Computing convexity properties of images on a pyramid computer

We present efficient parallel algorithms for using a pyramid computer to determine convexity properties of digitized black/white pictures and labeled figures. Algorithms are presented for deciding convexity, identifying extreme points of convex hulls, and using extreme points in a variety of fashions. For a pyramid computer with a base ofn simple processing elements arranged in ann ^1/2 ×n ^1/2 square, the running times of the algorithms range from (logn) to find the extreme points of a convex figure in a digitized picture, to (n ^1/6) to find the diameter of a labeled figure, (n ^1/4 logn) to find the extreme points of every figure in a digitized picture, to (n ^1/2) to find the extreme points of every labeled set of processing elements. Our results show that the pyramid computer can be used to obtain efficient solutions to nontrivial problems in image analysis. We also show the sensitivity of efficient pyramid-computer algorithms to the rate at which essential data can be compressed. Finally, we show that a wide variety of techniques are needed to make full and efficient use of the pyramid architecture.This research was partially supported by National Science Foundation Grants MCS-83-01019, DCR-8507851, DCR-8608640, IRI-8800514 and an Incentives for Excellence Award from the Digital Equipment Corporation.     

A parallel algorithm to construct a dominance graph on nonoverlapping rectangles

A parallel algorithm to generate the dominance graph on a collection of nonoverlapping iso-oriented rectangles is presented. This graph arises from the constraint graph commonly used in compaction algorithms for VLSI circuits. The dominance graph expresses the notion of aboveness on a collection of nonoverlapping rectangles: it is the directed graph which contains an edge from a rectangleb to rectanglec iffc is immediately aboveb. The algorithm is based on the divide and conquer paradigm; in the EREW PRAM model, it has time complexityO(log² n), usingn/logn processors. Its processor-time product isO(nlogn), which is optimal.     

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 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.     

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).     

An improved parallel algorithm for integer GCD

We present a simple parallel algorithm for computing the greatest common divisor (gcd) of twon-bit integers in the Common version of the CRCW model of computation. The run-time of the algorithm in terms of bit operations isO(n/logn), usingn ¹⁺ processors, where is any positive constant. This improves on the algorithm of Kannan, Miller, and Rudolph, the only sublinear algorithm known previously, both in run time and in number of processors; they requireO(n log logn/logn),n ² log² n, respectively, in the same CRCW model.We give an alternative implementation of our algorithm in the CREW model. Its run-time isO(n log logn/logn), usingn ¹⁺ processors. Both implementations can be modified to yield the extended gcd, within the same complexity bounds.Supported in part by an IBM Graduate Fellowship and a Bantrell Postdoctoral Fellowship.Supported in part by a Weizmann Postdoctoral Fellowship.4 All logarithms are to base 2.     

Undirecteds-t connectivity in polynomial time and sublinear space

Thes-t connectivity problem for undirected graphs is to decide whether two designated vertices,s andt, are in the same connected component. This paper presents the first known deterministic algorithms solving undirecteds-t connectivity using sublinear space and polynomial time. Our algorithms provide a nearly smooth time-space tradeoff between depth-first search and Savitch's algorithm. Forn vertex,m edge graphs, the simplest of our algorithms uses spaceO(s),n ^1/2log₂ nsnlog₂ n, and timeO(((m+n)n ² log² n)/s). We give a variant of this method that is faster at the higher end of the space spectrum. For example, with space (nlogn), its time bound isO((m+n)logn), close to the optimal time for the problem. Another generalization uses less space, but more time: spaceO(n ^1/logn), for 2log₂ n, and timen ^O(). For constant the time remains polynomial.     

Randomized range-maxima in nearly-constant parallel time

Given an array ofn input numbers, therange-maxima problem is that of preprocessing the data so that queries of the type what is the maximum value in subarray [i..j] can be answered quickly using one processor. We present a randomized preprocessing algorithm that runs inO(log^* n) time with high probability, using an optimal number of processors on a CRCW PRAM; each query can be processed in constant time by one processor. We also present a randomized algorithm for a parallel comparison model. Using an optimal number of processors, the preprocessing algorithm runs inO( (n)) time with high probability; each query can be processed inO ( (n)) time by one processor. (As is standard, (n) is the inverse of Ackermann function.) A constant time query can be achieved by some slowdown in the performance of the preprocessing stage.     

Using geometry to solve the transportation problem in the plane

LetU andV be two sets of points in the plane, where ¦U¦=k,¦V¦=, andn=k+. These two sets of points induce a directed complete bipartite graph in which the points represent nodes and an edge is directed from each node inU to each node in K Each edge is given a cost equal to the distance between the corresponding nodes measured by some metricd on the plane. We consider thetransportation problem on such a graph. We present an 0(n^2,5 logn logN) algorithm, whereN is the magnitude of the largest supply or demand. The algorithm uses some fundamental results of computational geometry and scaling of supplies and demands. The algorithm is valid for the ₁ metric, the ₂ metric, and the metric. The running time for the ₁ and metrics can be improved to 0(n²(logn)³ logN).D. S. Atkinson was supported by the National Science Foundation under Grant CCR90-57481PYI. P. M. Vaidya was supported by the National Science Foundation under Grants CCR-9057481 and CCR-9007195.     

Parallel algorithms for routing in nonblocking networks

We construct nonblocking networks that are efficient not only as regards their cost and delay, but also as regards the time and space required to control them. In this paper we present the first simultaneous weakly optimal solutions for the explicit construction of nonblocking networks, the design of algorithms and data-structures. Weakly optimal is in the sense that all measures of complexity (size and depth of the network, time for the algorithm, space for the data-structure, and number of processor-time product) are within one or more logarithmic factors of their smallest possible values. In fact, we construct a scheme in which networks withn inputs andn outputs have sizeO(n(logn)²) and depthO(logn), and we present deterministic and randomized on-line parallel algorithms to establish and abolish routes dynamically in these networks. In particular, the deterministic algorithm usesO((logn)⁵) steps to process any number of transactions in parallel (with one processor per transaction), maintaining a data structure that useO(n(logn)²) words.     

Simple adaptive stabilization of output feedback stabilizable distributed parameter systems

We show that the simple universal adaptive control lawu(t)=N(k(t))y(t)=|y(t)| ², withN(k)=(logk) cos((logk)) and 3+<1, stabilizes all detectable and stabilizable infinite dimensional systems of Pritchard-Salamon type which are externally stabilized by somescalar output feedback. The same controller is also shown to stabilize time varying systems satisfying the same type of output feedback stabilizability.     

Algorithms for dense graphs and networks on the random access computer

We improve upon the running time of several graph and network algorithms when applied to dense graphs. In particular, we show how to compute on a machine with word size = (logn) a maximal matching in ann-vertex bipartite graph in timeO(n ²+n ^2.5/)=O(n ^2.5/logn), how to compute the transitive closure of a digraph withn vertices andm edges in timeO(n ²+nm/), how to solve the uncapacitated transportation problem with integer costs in the range [O.C] and integer demands in the range [–U.U] in timeO ((n ³ (log log/logn)^1/2+n² logU) lognC), and how to solve the assignment problem with integer costs in the range [O.C] in timeO(n ^2.5 lognC/(logn/loglogn)^1/4).Assuming a suitably compressed input, we also show how to do depth-first and breadth-first search and how to compute strongly connected components and biconnected components in timeO(n+n ²/), and how to solve the single source shortest-path problem with integer costs in the range [O.C] in time0 (n ²(logC)/logn). For the transitive closure algorithm we also report on the experiences with an implementation.Most of this research was carried out while both authors worked at the Fachbereich Informatik, Universität des Saarlandes, Saarbrücken, Germany. The research was supported in part by ESPRIT Project No. 3075 ALCOM. The first author acknowledges support also from NSERC Grant No. OGPIN007.     

Constructing strongly convex hulls using exact or rounded arithmetic

One useful generalization of the convex hull of a setS ofn points is the -strongly convex -hull. It is defined to be a convex polygon with vertices taken fromS such that no point inS lies farther than outside and such that even if the vertices of are perturbed by as much as , remains convex. It was an open question as to whether an -strongly convexO()-hull existed for all positive . We give here anO(n logn) algorithm for constructing it (which thus proves its existence). This algorithm uses exact rational arithmetic. We also show how to construct an -strongly convexO( + )-hull inO(n logn) time using rounded arithmetic with rounding unit . This is the first rounded-arithmetic convex-hull algorithm which guarantees a convex output and which has error independent ofn.     

A geometric view of parametric linear programming

We present a new definition of optimality intervals for the parametric right-hand side linear programming (parametric RHS LP) Problem () = min{c ^t x¦Ax =b + ¯b,x 0}. We then show that an optimality interval consists either of a breakpoint or the open interval between two consecutive breakpoints of the continuous piecewise linear convex function (). As a consequence, the optimality intervals form a partition of the closed interval {; ¦()¦ < }. Based on these optimality intervals, we also introduce an algorithm for solving the parametric RHS LP problem which requires an LP solver as a subroutine. If a polynomial-time LP solver is used to implement this subroutine, we obtain a substantial improvement on the complexity of those parametric RHS LP instances which exhibit degeneracy. When the number of breakpoints of () is polynomial in terms of the size of the parametric problem, we show that the latter can be solved in polynomial time.This research was partially funded by the United States Navy-Office of Naval Research under Contract N00014-87-K-0202. Its financial support is gratefully acknowledged.