Overview
- Editors:
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Ding-Zhu Du
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Department of Computer Science, University of Minnesota, USA
Institute of Applied Mathematics, Academia Sinica, P. R. China
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Panos M. Pardalos
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Department of Industrial and Systems Engineering, University of Florida, USA
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About this book
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).
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Table of contents (9 chapters)
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Front Matter
Pages i-viii
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- Immanuel M. Bomze, Marco Budinich, Panos M. Pardalos, Marcello Pelillo
Pages 1-74
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- Rainer E. Burkard, Eranda Çela
Pages 75-149
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- Edward G. Coffman, Gabor Galambos, Silvano Martello, Daniele Vigo
Pages 151-207
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- Paola Festa, Panos M. Pardalos, Mauricio G. C. Resende
Pages 209-258
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- Theodore B. Trafalis, Suat Kasap
Pages 259-293
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- Robert A. Murphey, Panos M. Pardalos, Mauricio G. C. Resende
Pages 295-377
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- Jun Gu, Paul W. Purdom, John Franco, Benjamin W. Wah
Pages 379-572
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- Jens Albrecht, Dietmar Cieslik
Pages 573-589
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- Wenqi Huang, Yu-Liang Wu, C. K. Wong
Pages 591-605
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Back Matter
Pages 607-648
Editors and Affiliations
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Department of Computer Science, University of Minnesota, USA
Ding-Zhu Du
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Institute of Applied Mathematics, Academia Sinica, P. R. China
Ding-Zhu Du
-
Department of Industrial and Systems Engineering, University of Florida, USA
Panos M. Pardalos