Sorting Jordan sequences in linear time

For a Jordan curve C in the plane, let x_{1},x_{2},...,x_{n} be the abscissas of the intersection points of C with the x-axis, listed in the order the points occur on C. We call x_{1},x_{2},...,x_{n} a Jordan sequence. In this paper we describe an O(n)-time algorithm for recognizing and sorting Jordan sequences. The problem of sorting such sequences arises in computational geometry and computational geography. Our algorithm is based on a reduction of the recognition and sorting problem to a list-splitting problem. To solve the list-splitting problem we use level linked search trees

Parallel Algorithms for Depth-First Search

In this paper we examine parallel algorithms for performing a depth-first search (DFS) of a directed or undirected graph in sub-linear time. this subject is interesting in part because DFS seemed at first to be an inherently sequential process, and for a long time many researchers believed that no such algorithms existed. We survey three seminal papers on the subject. The first one proves that a special case of DFS is (in all likelihood) inherently sequential; the second shows that DFS for planar undirected graphs is in NC; and the third shows that DFS for general undirected graphs is in RNC. We also discuss randomnized algorithms, P-completeness and matching, three topics that are essential for understanding and appreciating the results in these papers

An efficient parallel algorithm for planarity

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERINGBibliography: leaves 56-57.by Philip Nathan Klein.M.S

Finding 3-edge-connected components in parallel

A parallel algorithm for finding 3-edge-connected components of an undirected graph on a CRCW PRAM is presented. The time and work complexity of this algorithm is O(logn) and O((m+n)loglogn), respectively, where n is the number of vertices and m is the number of edges in the input graph. The algorithm is based on ear decomposition and reduction of 3-edge-connectivity to 1-vertex-connectivity. This is the first 3-edge-connected component algorithm of a parallel model

An efficient parallel algorithm for planarity

Thesis (M.S.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 1986.MICROFICHE COPY AVAILABLE IN ARCHIVES AND ENGINEERINGBibliography: leaves 56-57.by Philip Nathan Klein.M.S

Design and Analysis of Algorithms: Course Notes

These are my lecture notes from CMSC 651: Design and Analysis of Algorithms}, a one semester course that I taught at University of Maryland in the Spring of 1993. The course covers core material in algorithm design, and also helps students prepare for research in the field of algorithms. The reader will find an unusual emphasis on graph theoretic algorithms, and for that I am to blame. The choice of topics was mine, and is biased by my personal taste. The material for the first few weeks was taken primarily from the (now not so new) textbook on Algorithms by Cormen, Leiserson and Rivest. A few papers were also covered, that I personally feel give some very important and useful techniques that should be in the toolbox of every algorithms researcher. (Also cross-referenced as UMIACS-TR-93-72

Planarity testing and embedding algorithms.

Thesis (M.Sc.)-University of Natal, Durban,1990.This thesis deals with several aspects of planar graphs, and some of the problems associated with non-planar graphs. Chapter 1 is devoted to introducing some of the fundamental notation and tools used in the remainder of the thesis. Graphs serve as useful models of electronic circuits. It is often of interest to know if a given electronic circuit has a layout on the plane so that no two wires cross. In Chapter 2, three efficient algorithms are described for determining whether a given 2-connected graph (which may model such a circuit) is planar. The first planarity testing algorithm uses a path addition approach. Although this algorithm is efficient, it does not have linear complexity. However, the second planarity testing algorithm has linear complexity, and uses a recursive fragment addition technique. The last planarity testing algorithm also has linear complexity, and relies on a relatively new data structure called PQ-trees which have several important applications to planar graphs. This algorithm uses a vertex addition technique. Chapter 3 further develops the idea of modelling an electronic circuit using a graph. Knowing that a given electronic circuit may be placed in the plane with no wires crossing is often insufficient. For example, some electronic circuits often have in excess of 100 000 nodes. Thus, obtaining a description of such a layout is important. In Chapter 3 we study two algorithms for obtaining such a description, both of which rely on the PQ-tree data structure. The first algorithm determines a rotational embedding of a 2-connected graph. Given a rotational embedding of a 2-connected graph, the second algorithm determines if a convex drawing of a graph is possible. If a convex drawing is possible, then we output the convex drawing. In Chapter 4, we concern ourselves with graphs that have failed a planarity test of Chapter 2. This is of particular importance, since complex electronic circuits often do not allow a layout on the plane. We study three different ways of approaching the problem of an electronic circuit modelled on a non-planar graph, all of which use the PQ-tree data structure. We study an algorithm for finding an upper bound on the thickness of a graph, an algorithm for determining the subgraphs of a non-planar graph which are subdivisions of the Kuratowski graphs K5 and K3,3, and lastly we present a new algorithm for finding an upper bound on the genus of a non-planar graph

Shortest Path Queries in Very Large Spatial Databases

Finding the shortest paths in a graph has been studied for a long time, and there are many main memory based algorithms dealing with this problem. Among these, Dijkstra's shortest path algorithm is one of the most commonly used efficient algorithms to the non-negative graphs. Even more efficient algorithms have been developed recently for graphs with particular properties such as the weights of edges fall into a range of integer. All of the mentioned algorithms require the graph totally reside in the main memory. Howevery, for very large graphs, such as the digital maps managed by Geographic Information Systems (GIS), the requirement cannot be satisfied in most cases, so the algorithms mentioned above are not appropriate. My objective in this thesis is to design and evaluate the performance of external memory (disk-based) shortest path algorithms and data structures to solve the shortest path problem in very large digital maps. In particular the following questions are studied:What have other researchers done on the shortest path queries in very large digital maps?What could be improved on the previous works? How efficient are our new shortest paths algorithms on the digital maps, and what factors affect the efficiency? What can be done based on the algorithm? In this thesis, we give a disk-based Dijkstra's-like algorithm to answer shortest path queries based on pre-processing information. Experiments based on our Java implementation are given to show what factors affect the running time of our algorithms

On optimal and near-optimal algorithms for some computational graph problems

PhD ThesisSome computational graph problems are considered in this thesis and algorithms for solving these problems are described in detail. The problems can be divided into three main classes, namely, problems involving partially ordered sets, finding cycles in graphs, and shortest path problems. Most of the algorithms are based on recursive procedures using depth-first search. The efficiency of each algorithm is derived and it can be concluded that the majority of the proposed algorithms are either optimal and near-optimal within a constant factor. The efficiency of the algorithms is measured by the time and space requirements for their implementation.Conselho Nacional de Pesquisas,Brazil: Universidade Federal do Rio de Janeiro, Brazil