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Selected Algorithmic Techniques for Parallel Optimization

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Handbook of Combinatorial Optimization

Abstract

The use of parallel algorithms for solving computationally hard problems becomes attractive as parallel systems, consisting of a collection of powerful processors, offer large computing power and memory storage capacity. Even though parallelism will not be able to overdue the assumed worst case exponential time or memory complexity of those problems (unless an exponential number of processors is used) [11], the average execution time of heuristic search algorithms which find good suboptimal solutions for many hard problems is polynomial. Consequently, parallel systems, possibly with hundreds or thousands of processors, give us the perspective of efficiently solving relatively large instances of hard problems.

Partially supported by the brazilian agency FAPERJ.

Partially supported by the brazilian agency CNPq.

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Authors and Affiliations

  1. Núcleo de Computação Eletrônica, Universidade Federal do Rio de Janeiro, Caixa Postal 2324, 20001-970, Rio de Janeiro, RJ, Brazil

    Ricardo C. Corrêa

  2. SLOOP Project, CNRS-I3S-INRIA, BP93, 06902, Sophia-Antipolis, France

    Afonso Ferreira

  3. Computação Aplicada e Automação, Universidade Federal Fluminense, Rua Passo da Pátria, 156, 24210-240, Niterói, RJ, Brazil

    Stella C. S. Porto

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  1. Ricardo C. Corrêa
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  2. Afonso Ferreira
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  3. Stella C. S. Porto
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Editors and Affiliations

  1. University of Minnesota, Minneapolis, USA

    Ding-Zhu Du

  2. University of Florida, Gainesville, USA

    Panos M. Pardalos

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Corrêa, R.C., Ferreira, A., Porto, S.C.S. (1998). Selected Algorithmic Techniques for Parallel Optimization. In: Du, DZ., Pardalos, P.M. (eds) Handbook of Combinatorial Optimization. Springer, Boston, MA. https://doi.org/10.1007/978-1-4613-0303-9_29

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