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Solving Combinatorial Optimization Problems in Parallel Methods and Techniques

Methods and Techniques

  • Book
  • © 1996

Overview

Editors:
  1. Afonso Ferreira
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  2. Panos Pardalos
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Part of the book series: Lecture Notes in Computer Science (LNCS, volume 1054)

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Table of contents (11 chapters)

  1. Front Matter

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  2. SCOOP: Solving Combinatorial Optimization problems in parallel

    • Afonso Ferreira, Panos M. Pardalos
    Pages 1-6

  3. Parallel approximation of optimization problems

    • D. P. Bovet, A. Clementi, P. Crescenzi, R. Silvestri
    Pages 7-24

  4. Randomized parallel algorithms

    • Andrea Clementi, José D. P. Rolim, Erik Urland
    Pages 25-50

  5. Automatic synthesis of parallel algorithms

    • G. M. Megson, L. Rapanotti, X. Chen
    Pages 51-86

  6. An introduction to parallel dynamic programming

    • Marc Gengler
    Pages 87-114

  7. Mapping tree-structured combinatorial optimization problems onto parallel computers

    • Reinhard Lüling, Burkhard Monien, Alexander Reinefeld, Stefan Tschöke
    Pages 115-144

  8. Towards an abstract parallel branch and bound machine

    • A. de Bruin, G. A. P. Kindervater, H. W. J. M. Trienekens
    Pages 145-170

  9. Parallel best-first branch- and-bound in discrete optimization: A framework

    • Ricardo Corrêa, Afonso Ferreira
    Pages 171-200

  10. Building a parallel branch and bound library

    • Mohamed Benaïchouche, Van-Dat Cung, Salah Dowaji, Bertrand Le Cun, Thierry Mautor, Catherine Roucairol
    Pages 201-231

  11. Parallel algorithms for global optimization problems

    • P. M. Pardalos, Guoliang Xue, P. D. Panagiotopoulos
    Pages 232-247

  12. Parallel heuristic search — Introductions and a new approach

    • Per S. Laursen
    Pages 248-274

  13. Back Matter

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Keywords

About this book

Solving combinatorial optimization problems can often lead to runtime growing exponentially as a function of the input size. But important real-world problems, industrial applications, and academic research challenges, may demand exact optimal solutions. In such situations, parallel processing can reduce the runtime from days or months, typical when one workstation is used, to a few minutes or even seconds.
Partners of the CEC-sponsored SCOOP Project (Solving Combinatorial Optimization Problems in Parallel) contributed, on invitation, to this book; much attention was paid to competent coverage of the topic and the style of writing. Readers will include students, scientists, engineers, and professionals interested in the design and implementation of parallel algorithms for solving combinatorial optimization problems.

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