An optimal visibility graph algorithm for triangulated simple polygons

LetP be a triangulated simple polygon withn sides. The visibility graph ofP has an edge between every pair of polygon vertices that can be connected by an open segment in the interior ofP. We describe an algorithm that finds the visibility graph ofP inO(m) time, wherem is the number of edges in the visibility graph. Becausem can be as small asO(n), the algorithm improves on the more general visibility algorithms of Asanoet al. [AAGHI] and Welzl [W], which take Θ(n ²) time, and on Suri'sO(m logn) visibility graph algorithm for simple polygons [S].     

Visibility of disjoint polygons

Consider a collection of disjoint polygons in the plane containing a total ofn edges. We show how to build, inO(n ²) time and space, a data structure from which inO(n) time we can compute the visibility polygon of a given point with respect to the polygon collection. As an application of this structure, the visibility graph of the given polygons can be constructed inO(n ²) time and space. This implies that the shortest path that connects two points in the plane and avoids the polygons in our collection can be computed inO(n ²) time, improving earlierO(n ² logn) results.     

A fast algorithm for computing sparse visibility graphs

AnO(¦E¦log² n) algorithm is presented to construct the visibility graph for a collection ofn nonintersecting line segments, where ¦E¦ is the number of edges in the visibility graph. This algorithm is much faster than theO(n ²)-time andO(n ²)-space algorithms by Asanoet al., and by Welzl, on sparse visibility graphs. Thus we partially resolve an open problem raised by Welzl. Further, our algorithm uses onlyO(n) working storage.     

Visibility of disjoint polygons

Consider a collection of disjoint polygons in the plane containing a total ofn edges. We show how to build, inO(n ²) time and space, a data structure from which inO(n) time we can compute the visibility polygon of a given point with respect to the polygon collection. As an application of this structure, the visibility graph of the given polygons can be constructed inO(n ²) time and space. This implies that the shortest path that connects two points in the plane and avoids the polygons in our collection can be computed inO(n ²) time, improving earlierO(n ² logn) results.     

A fast algorithm for computing sparse visibility graphs

AnO(¦E¦log² n) algorithm is presented to construct the visibility graph for a collection ofn nonintersecting line segments, where ¦E¦ is the number of edges in the visibility graph. This algorithm is much faster than theO(n ²)-time andO(n ²)-space algorithms by Asanoet al., and by Welzl, on sparse visibility graphs. Thus we partially resolve an open problem raised by Welzl. Further, our algorithm uses onlyO(n) working storage.     

Visibility between two edges of a simple polygon

One of the most recurring themes in many computer applications such as graphics automated cartography, image processing and robotics is the notion of visibility. We are concerned with the visibility between two edges of a simplen-vertex polygon. Four natural definitions of edge-to-edge visibility are proposed. There existO(nlogn) algorithms and complicatedO(nlog logn) algorithms to solve this problem partially and indirectly. A linear running time, and thus optimal algorithm is presented to determine edge-to-edge visibility under any of the four definitions. This simple, efficient, and direct algorithm without computing the triangulation of the simple polygon also identifies the visibility region if it exists.     

Drawings of graphs on surfaces with few crossings

We give drawings of a complete graphK _n withO(n ⁴ log² g/g) many crossings on an orientable or nonorientable surface of genusg 2. We use these drawings ofK _n and give a polynomial-time algorithm for drawing any graph withn vertices andm edges withO(m ² log² g/g) many crossings on an orientable or nonorientable surface of genusg 2. Moreover, we derive lower bounds on the crossing number of any graph on a surface of genusg 0. The number of crossings in the drawings produced by our algorithm are within a multiplicative factor ofO(log² g) from the lower bound (and hence from the optimal) for any graph withm 8n andn ²/m g m/64.The research of the third and the fourth authors was partially supported by Grant No. 2/1138/94 of the Slovak Academy of Sciences and by EC Cooperative action IC1000 Algorithms for Future Technologies (Project ALTEC). A preliminary version of this paper was presented at WG93 and published in Lecture Notes in Computer Science, Vol. 790, 1993, pp. 388–396.     

Efficient Graph-Theoretic Algorithms on a Linear Array with a Reconfigurable Pipelined Bus System

We present efficient algorithms for solving several fundamental graph-theoretic problems on a Linear Array with a Reconfigurable Pipelined Bus System (LARPBS), one of the recently proposed models of computation based on optical buses. Our algorithms include finding connected components, minimum spanning forest, biconnected components, bridges and articulation points for an undirected graph. We compute the connected components and minimum spanning forest of a graph in O(log n) time using O(m+n) processors where m and n are the number of edges and vertices in the graph and m=O(n ²) for a dense graph. Both the processor and time complexities of these two algorithms match the complexities of algorithms on the Arbitrary and Priority CRCW PRAM models which are two of the strongest PRAM models. The algorithms for these two problems published by Li et al. [7] have been considered to be the most efficient on the LARPBS model till now. Their algorithm [7] for these two problems require O(log n) time and O(n ³/log n) processors. Hence, our algorithms have the same time complexity but require less processors. Our algorithms for computing biconnected components, bridges and articulation points of a graph run in O(log n) time on an LARPBS with O(n ²) processors. No previous algorithm was known for these latter problems on the LARPBS.     

Cartesian graph factorization at logarithmic cost per edge

LetG be a connected graph withn vertices andm edges. We develop an algorithm that finds the (unique) prime factors ofG with respect to the Cartesian product inO(m logn) time andO(m) space. This shows that factoringG is at most as costly as sorting its edges. The algorithm gains its efficiency and practicality from using only basic properties of product graphs and simple data structures.     

Connected component and simple polygon intersection searching

Efficient data structures are given for the following two query problems: (i) preprocess a setP of simple polygons with a total ofn edges, so that all polygons ofP intersected by a query segment can be reported efficiently, and (ii) preprocess a setS ofn segments, so that the connected components of the arrangement ofS intersected by a query segment can be reported quickly. In these problems we do not want to return the polygons or connected components explicitly (i.e., we do not wish to report the segments defining the polygon, or the segments lying in the connected components). Instead, we assume that the polygons (or connected components) are labeled and we just want to report their labels. We present data structures of sizeO(n ¹⁺) that can answer a query in timeO(n ¹⁺+k), wherek is the output size. If the edges ofP (or the segments inS) are orthogonal, the query time can be improved toO(logn+k) usingO(n logn) space. We also present data structures that can maintain the connected components as we insert new segments. For arbitrary segments the amortized update and query time areO(n ^1/2+) andO(n ^1/2++k), respectively, and the space used by the data structure isO(n ¹⁺. If we allowO(n ^4/3+ space, the amortized update and query time can be improved toO(n ^1/3+ andO(n ^1/3++k, respectively. For orthogonal segments the amortized update and query time areO(log² n) andO(log² n+klogn), and the space used by the data structure isO (n logn). Some other related results are also mentioned.Part of this work was done while the second author was visiting the first author on a grant by the Dutch Organization for Scientific Research (N.W.O.). The research of the second author was also supported by the ESPRIT Basic Research Action No. 3075 (project ALCOM). The research of the first author was supported by National Science Foundation Grant CCR-91-06514.     

Selection, Routing, and Sorting on the Star Graph

We consider the problems of selection, routing, and sorting on ann-star graph (withn! nodes), an interconnection network which has been proven to possess many special properties. We identify a tree like subgraph (which we call a “(k, 1,k) chain network”) of the star graph which enables us to design efficient algorithms for the above mentioned problems. We present an algorithm that performs a sequence ofnprefix computations inO(n²) time. This algorithm is used as a subroutine in our other algorithms. We also show that sorting can be performed on then-star graph in timeO(n³) and that selection of a set of uniformly distributednkeys can be performed inO(n²) time with high probability. Finally, we also present a deterministic (nonoblivious) routing algorithm that realizes any permutation inO(n³) steps on then-star graph. There exists an algorithm in the literature that can perform a single prefix computation inO(nlgn) time. The best-known previous algorithm for sorting has a run time ofO(n³lgn) and is deterministic. To our knowledge, the problem of selection has not been considered before on the star graph.     

On approximation behavior of the greedy triangulation for convex polygons

We prove that the greedy triangulation heuristic for minimum weight triangulation of convex polygons yields solutions within a constant factor from the optimum. For interesting classes of convex polygons, we derive small upper bounds on the constant approximation factor. Our results contrast with Kirkpatrick's (n) bound on the approximation factor of the Delaunay triangulation heuristic for minimum weight triangulation of convexn-vertex polygons. On the other hand, we present a straightforward implementation of the greedy triangulation heuristic for ann-vertex convex point set or a convex polygon takingO(n ²) time andO(n) space. To derive the latter result, we show that given a convex polygonP, one can find for all verticesv ofP a shortest diagonal ofP incident tov in linear time. Finally, we observe that the greedy triangulation for convex polygons having so-called semicircular property can be constructed in timeO(n logn).     

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.     

Maintaining bridge-connected and biconnected components on-line

We consider the twin problems of maintaining the bridge-connected components and the biconnected components of a dynamic undirected graph. The allowed changes to the graph are vertex and edge insertions. We give an algorithm for each problem. With simple data structures, each algorithm runs inO(n logn +m) time, wheren is the number of vertices andm is the number of operations. We develop a modified version of the dynamic trees of Sleator and Tarjan that is suitable for efficient recursive algorithms, and use it to reduce the running time of the algorithms for both problems toO(m(m,n)), where is a functional inverse of Ackermann's function. This time bound is optimal. All of the algorithms useO(n) space.Research at Princeton University supported in part by National Science Foundation Grant DCR-86-05962 and Office of Naval Research Contract N00014-91-J-1463.This work was partially done while the author was at the Department of Computer Science, Princeton University, Princeton, NJ 08544, USA.     

Solving shortest paths efficiently on nearly acyclic directed graphs

Shortest path problems can be solved very efficiently when a directed graph is nearly acyclic. Earlier results defined a graph decomposition, now called the 1-dominator set, which consists of a unique collection of acyclic structures with each single acyclic structure dominated by a single associated trigger vertex. In this framework, a specialised shortest path algorithm only spends delete-min operations on trigger vertices, thereby making the computation of shortest paths through non-trigger vertices easier. A previously presented algorithm computed the 1-dominator set in O(mn) worst-case time, which allowed it to be integrated as part of an O(mn+nrlogr) time all-pairs algorithm. Here m and n respectively denote the number of edges and vertices in the graph, while r denotes the number of trigger vertices. A new algorithm presented in this paper computes the 1-dominator set in just O(m) time. This can be integrated as part of the O(m+rlogr) time spent solving single-source, improving on the value of r obtained by the earlier tree-decomposition single-source algorithm. In addition, a new bidirectional form of 1-dominator set is presented, which further improves the value of r by defining acyclic structures in both directions over edges in the graph. The bidirectional 1-dominator set can similarly be computed in O(m) time and included as part of the O(m+rlogr) time spent computing single-source. This paper also presents a new all-pairs algorithm under the more general framework where r is defined as the size of any predetermined feedback vertex set of the graph, improving the previous all-pairs time complexity from O(mn+nr²) to O(mn+r³).     

A model of sequential computation with Pipelined access to memory

We introduce a new sequential model of computation, called the Logarithmic Pipelined Model (LPM), in which a RAM processor of fixed size has pipelined access to a memory ofm cells in time logm. Our motivation is that the usual assumption that a memory can be accessed in constant time becomes theoretically unacceptable asm increases, while an access time of logm is consistent with VLSI technologies. For a problem II of sizen, IT P, we denote byS(n) the time required by the fastest known sequential algorithm, and byT(n) the time required by the fastest algorithm solving II in the LPM. LettingO(logn) =O(logm), we define several complexity classes; in particular, LP₀ = {II P:T(n) =O(S(n))}, the class of problems for which the LPM is as efficient as the standard model, and LP =IIP:T(n) =O(S(n) logn), where the problems are less adequately solved in the new model. We first study the relations between the LPM and other models of computation. Of particular relevance is comparison with the PRAM model. Then we discuss several problems and derive the relative upper and lower bounds in the LPM. Our results lead to a new organization of parallel algorithms for list-linked structures.This work was supported in part by M.U.R.S.T. of Italy under a research grant.     

An algorithm for recognizing palm polygons

A polygonP is said to be apalm polygon if there exists a pointxP such that the Euclidean shortest path fromx to any pointyP makes only left turns or only right turns. The set of all such pointsx is called thepalm kernel. In this paper we propose an O(E) time algorithm for recognizing a palm polygonP, whereE is the size of the visibility graph ofP. The algorithm recognizes the given polygonP as a palm polygon by computing the palm kernel ofP. If the palm kernel is not empty,P is a palm polygon.The extended abstract of this paper was reported at the Second Canadian Conference in Computational Geometry, pp. 246–251, 1990     

Ray shooting in polygons using geodesic triangulations

LetP be a simple polygon withn vertices. We present a simple decomposition scheme that partitions the interior ofP intoO(n) so-called geodesic triangles, so that any line segment interior toP crosses at most 2 logn of these triangles. This decomposition can be used to preprocessP in a very simple manner, so that any ray-shooting query can be answered in timeO(logn). The data structure requiresO(n) storage andO(n logn) preprocessing time. By using more sophisticated techniques, we can reduce the preprocessing time toO(n). We also extend our general technique to the case of ray shooting amidstk polygonal obstacles with a total ofn edges, so that a query can be answered inO( logn) time.Work by Bernard Chazelle has been supported by NSF Grant CCR-87-00917. Work by Herbert Edelsbrunner has been supported by NSF Grant CCR-89-21421. Work by Micha Sharir has been supported by ONR Grants N00014-89-J-3042 and N00014-90-J-1284, by NSF Grant CCR-89-01484, and by grants from the U.S.-Israeli Binational Science Foundation, the Fund for Basic Research administered by the Israeli Academy of Sciences, and the G.I.F., the German-Israeli Foundation for Scientific Research and Development.     

Optimum sweeps of simple polygons with two guards

A polygon P admits a sweep if two mobile guards can detect an unpredictable, moving target inside P  , no matter how fast the target moves. Two guards move on the polygon boundary and are required to always be mutually visible. The objective of this study is to find an optimum sweep such that the sum of the distances travelled by the two guards in the sweep is minimized. We present an O(n²)$O(n^2)$ time and O(n)$O(n)$ space algorithm for optimizing this metric, where n   is the number of vertices of the given polygon. Our result is obtained by reducing this problem to finding a shortest path between two nodes in a graph of size O(n)$O(n)$.