Download A Java Library of Graph Algorithms and Optimization by Hang T. Lau PDF

By Hang T. Lau

Due to its portability and platform-independence, Java is the suitable desktop programming language to exploit whilst engaged on graph algorithms and different mathematical programming difficulties. gathering probably the most well known graph algorithms and optimization systems, A Java Library of Graph Algorithms and Optimization offers the resource code for a library of Java courses that may be used to resolve difficulties in graph conception and combinatorial optimization. Self-contained and principally self sufficient, every one subject starts off with an issue description and an overview of the answer process, via its parameter checklist specification, resource code, and a attempt instance that illustrates the use of the code. The ebook starts off with a bankruptcy on random graph iteration that examines bipartite, standard, hooked up, Hamilton, and isomorphic graphs in addition to spanning, classified, and unlabeled rooted timber. It then discusses connectivity systems, by means of a paths and cycles bankruptcy that comprises the chinese language postman and touring salesman difficulties, Euler and Hamilton cycles, and shortest paths. the writer proceeds to explain try strategies regarding planarity and graph isomorphism. next chapters take care of graph coloring, graph matching, community circulate, and packing and masking, together with the task, bottleneck task, quadratic task, a number of knapsack, set protecting, and set partitioning difficulties. the ultimate chapters discover linear, integer, and quadratic programming. The appendices offer references that supply additional information of the algorithms and contain the definitions of many graph thought phrases utilized in the ebook.

Show description

Read or Download A Java Library of Graph Algorithms and Optimization PDF

Similar algorithms and data structures books

Algorithm Theory — SWAT'98: 6th Scandinavian Workshop on Algorithm Theory Stockholm, Sweden, July 8–10, 1998 Proceedings

This ebook constitutes the refereed lawsuits of the sixth Scandinavian Workshop on set of rules thought, SWAT'98, held in Stockholm, Sweden, in July 1998. the amount offers 28 revised complete papers chosen from fifty six submissions; additionally integrated are 3 invited contributions. The papers current unique learn on algorithms and knowledge constructions in quite a few components together with computational geometry, parallel and disbursed structures, graph concept, approximation, computational biology, queueing, Voronoi diagrams, and combinatorics in most cases.

Robust range image registration: using genetic algorithms and the surface interpenetration measure

This booklet addresses the diversity snapshot registration challenge for automated 3D version building. the point of interest is on acquiring hugely certain alignments among varied view pairs of an identical item to prevent 3D version distortions; not like such a lot earlier paintings, the view pairs may perhaps express rather little overlap and needn't be prealigned.

A Recursive Introduction to the Theory of Computation

The purpose of this textbook is to give an account of the speculation of computation. After introducing the idea that of a version of computation and proposing a number of examples, the writer explores the restrictions of powerful computation through simple recursion thought. Self-reference and different equipment are brought as basic and simple instruments for developing and manipulating algorithms.

Additional resources for A Java Library of Graph Algorithms and Optimization

Example text

The method generates a random graph first, then a random permutation of n objects (perm[1], perm[2], …, perm[n]). The second isomorphic graph is obtained by renaming the vertices of the first random graph by the random permutation. The node i of the first random graph corresponds to the node perm[i] in the second graph. Procedure parameters: int randomIsomorphicGraphs (n, m, seed, simple, directed, firsti, firstj, secondi, secondj, map) randomIsomorphicGraph: int; exit: the method returns the following error code: 0: solution found with normal execution 1: value of m is too large, should be at most n∗(n−1)/2 for simple undirected graph, and n∗(n−1) for simple directed graph.

Directed = true if the graph is directed, false otherwise. weighted = true if the graph is weighted, false otherwise. minimum weight of the edges; if weighted = false then this value is ignored. maxweight: int; entry: maximum weight of the edges; if weighted = false then this value is ignored. nodei, nodej: int[m+1]; exit: the i-th edge is from node nodei[i] to node nodej[i], for i = 1,2,…,m. The Hamilton cycle is given by the first n elements of these two arrays. weight: int[m+1]; exit: weight[i] is the weight of the i-th edge, for i = 1,2,…,m; if weighted = false then this array is ignored.

The node i of the first random graph corresponds to the node perm[i] in the second graph. Procedure parameters: int randomIsomorphicGraphs (n, m, seed, simple, directed, firsti, firstj, secondi, secondj, map) randomIsomorphicGraph: int; exit: the method returns the following error code: 0: solution found with normal execution 1: value of m is too large, should be at most n∗(n−1)/2 for simple undirected graph, and n∗(n−1) for simple directed graph. n: int; entry: number of nodes of each graph. Nodes of each graph are labeled from 1 to n.

Download PDF sample

Rated 4.40 of 5 – based on 29 votes