Parallel Processing of Discrete Problems

1. Front Matter

   Pages i-ix

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2. Distributed Branch and Bound Algorithms for Global Optimization

   • Ioannis P. Androulakis, Christodoulos A. Floudas

   Pages 1-35

3. Large-Scale Structured Discrete Optimization via Parallel Genetic Algorithms

   • Ioannis T. Christou, W. W. Donaldson, R. R. Meyer

   Pages 37-57

4. Pushing the Limits of Solvable QAP Problems Using Parallel Processing - Is Nugent30 within Reach?

   • Jens Clausen

   Pages 59-74

5. On the Design of Parallel Discrete Algorithms for High Performance Computing Systems

   • Afonso Ferreira

   Pages 75-83

6. Parallel Algorithms for Satisfiability (SAT) Testing

   • Jun Gu

   Pages 85-138

7. Sequential and Parallel Branch-and-Bound Search under Limited-Memory Constraints

   • Nihar R. Mahapatra, Shantanu Dutt

   Pages 139-158

8. A Parallel Grasp for the Data Association Multidimensional Assignment Problem

   • R. A. Murphey, P. M. Pardalos, L. Pitsoulis

   Pages 159-179

9. Basic Algorithms on Parallel Optical Models of Computing

   • Sanguthevar Rajasekaran

   Pages 181-195

10. Randomized Parallel Algorithms

    • Jose D. P. Rolim

    Pages 197-222

11. Finite Behavior of Simulated Annealing: A Probabilistic Study

    • Guoliang Xue

    Pages 223-235

12. Back Matter

    Pages 237-242

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In the past two decades, breakthroughs in computer technology have made a tremendous impact on optimization. In particular, availability of parallel computers has created substantial interest in exploring the use of parallel processing for solving discrete and global optimization problems. The chapters in this volume cover a broad spectrum of recent research in parallel processing of discrete and related problems. The topics discussed include distributed branch-and-bound algorithms, parallel genetic algorithms for large scale discrete problems, simulated annealing, parallel branch-and-bound search under limited-memory constraints, parallelization of greedy randomized adaptive search procedures, parallel optical models of computing, randomized parallel algorithms, general techniques for the design of parallel discrete algorithms, parallel algorithms for the solution of quadratic assignment and satisfiability problems. The book will be a valuable source of information to faculty, students and researchers in combinatorial optimization and related areas.

• algorithms
• combinatorial optimization
• discrete optimization
• genetic algorithms
• global optimization
• high performance computing
• optimization
• combinatorics

• Center for Applied Optimization Industrial and Systems Engineering Department, University of Florida, Gainesville, USA

  Panos M. Pardalos

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