Handbook of Combinatorial Optimization

1. Optimization Problems in Optical Networks

   • Peng-Jun Wan

   Pages 1289-1334

2. Shortest Networks on Surfaces

   • J. F. Weng

   Pages 1335-1362

3. Minimum Weight Triangulations

   • Yin-Feng Xu

   Pages 1363-1380

4. Optimization Applications in the Airline Industry

   • Gang Yu, Jian Yang

   Pages 1381-1472

5. Semidefinite Relaxations, Multivariate Normal Distributions, and Order Statistics

   • Dimitris Bertsimas, Yinyu Ye

   Pages 1473-1491

6. A Review of Machine Scheduling: Complexity, Algorithms and Approximability

   • Bo Chen, Chris N. Potts, Gerhard J. Woeginger

   Pages 1493-1641

7. Routing and Topology Embedding in Lightwave Networks

   • Feng Cao

   Pages 1643-1711

8. The Quadratic Assignment Problem

   • Rainer E. Burkard, Eranda Çela, Panos M. Pardalos, Leonidas S. Pitsoulis

   Pages 1713-1809

9. Algorithmic Aspects of Domination in Graphs

   • Gerard J. Chang

   Pages 1811-1877

10. Selected Algorithmic Techniques for Parallel Optimization

    • Ricardo C. Corrêa, Afonso Ferreira, Stella C. S. Porto

    Pages 1879-1928

11. Multispace Search for Combinatorial Optimization

    • Jun Gu

    Pages 1929-2013

12. The Equitable Coloring of Graphs

    • Ko-Wei Lih

    Pages 2015-2038

13. Randomized Parallel Algorithms for Combinatorial Optimization

    • Sanguthevar Rajasekaran, José D. P. Rolim

    Pages 2039-2092

14. Tabu Search

    • Fred Glover, Manuel Laguna

    Pages 2093-2229

15. Back Matter

    Pages 2233-2403

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Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied math­ ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, air­ line crew scheduling, corporate planning, computer-aided design and man­ ufacturing, database query design, cellular telephone frequency assignment, constraint directed reasoning, and computational biology. Furthermore, combinatorial optimization problems occur in many diverse areas such as linear and integer programming, graph theory, artificial intelligence, and number theory. All these problems, when formulated mathematically as the minimization or maximization of a certain function defined on some domain, have a commonality of discreteness. Historically, combinatorial optimization starts with linear programming. Linear programming has an entire range of important applications including production planning and distribution, personnel assignment, finance, alloca­ tion of economic resources, circuit simulation, and control systems. Leonid Kantorovich and Tjalling Koopmans received the Nobel Prize (1975) for their work on the optimal allocation of resources. Two important discover­ ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These algo­ rithms have had a profound effect in combinatorial optimization. Many polynomial-time solvable combinatorial optimization problems are special cases of linear programming (e.g. matching and maximum flow). In addi­ tion, linear programming relaxations are often the basis for many approxi­ mation algorithms for solving NP-hard problems (e.g. dualheuristics).

• algorithms
• artificial intelligence
• combinatorial optimization
• communication
• dynamical systems
• graph theory
• heuristics
• linear optimization
• network
• nonlinear optimization
• number theory
• operations research
• optimization
• programming
• statistics
• combinatorics
• information and communication, circuits

• University of Minnesota, Minneapolis, USA

  Ding-Zhu Du

• University of Florida, Gainesville, USA

  Panos M. Pardalos

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