A Java Library of Graph Algorithms and Optimization

Hang T. Lau

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$103.96

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October 20, 2006 by Chapman and Hall/CRC
Reference - 386 Pages
ISBN 9781584887188 - CAT# C7184
Series: Discrete Mathematics and Its Applications

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Features

  • Contains the source code for a software library of roughly 60 Java procedures for the computation of standard problems in graph theory and optimization
  • Explores numerous graph algorithms and combinatorial optimization procedures
  • Provides a list of simple parameters for each topic, enabling minimal effort for problem solving
  • Features numerous worked examples as guides to using each program
  • Includes a CD-ROM with all of the Java code used in the book
  • Summary

    Because of its portability and platform-independence, Java is the ideal computer programming language to use when working on graph algorithms and other mathematical programming problems. Collecting some of the most popular graph algorithms and optimization procedures, A Java Library of Graph Algorithms and Optimization provides the source code for a library of Java programs that can be used to solve problems in graph theory and combinatorial optimization. Self-contained and largely independent, each topic starts with a problem description and an outline of the solution procedure, followed by its parameter list specification, source code, and a test example that illustrates the usage of the code.

    The book begins with a chapter on random graph generation that examines bipartite, regular, connected, Hamilton, and isomorphic graphs as well as spanning, labeled, and unlabeled rooted trees. It then discusses connectivity procedures, followed by a paths and cycles chapter that contains the Chinese postman and traveling salesman problems, Euler and Hamilton cycles, and shortest paths. The author proceeds to describe two test procedures involving planarity and graph isomorphism. Subsequent chapters deal with graph coloring, graph matching, network flow, and packing and covering, including the assignment, bottleneck assignment, quadratic assignment, multiple knapsack, set covering, and set partitioning problems. The final chapters explore linear, integer, and quadratic programming. The appendices provide references that offer further details of the algorithms and include the definitions of many graph theory terms used in the book.