Overview
- Editors:
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Ding-Zhu Du
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University of Minnesota, Minneapolis
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Panos M. Pardalos
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University of Florida, Gainesville
- The material presented in this supplement to the 3-volume Handbook of Combinatorial Optimization will be useful for any researcher who uses combinatorial optimization methods to solve problems
- Includes supplementary material: sn.pub/extras
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Table of contents (8 chapters)
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- Diptesh Ghosh, Boris Goldengorin, Gerard Sierksma
Pages 1-53
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- Xiuzhen Cheng, Yingshu Li, Ding-Zhu Du, Hung Q. Ngo
Pages 193-216
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- Vladimir Boginski, Panos M. Pardalos, Alkis Vazacopoulos
Pages 217-258
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- Dolores Romero Morales, H. Edwin Romeijn
Pages 259-311
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- Xiuzhen Cheng, Ding-Zhu Du, Joon-Mo Kim, Lu Ruan
Pages 313-327
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- Jeremy Blum, Min Ding, Andrew Thaeler, Xiuzhen Cheng
Pages 329-369
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Back Matter
Pages 371-394
About this book
Combinatorial (or discrete) optimization is one of the most active fields in the interface of operations research, computer science, and applied ma- ematics. Combinatorial optimization problems arise in various applications, including communications network design, VLSI design, machine vision, a- line crew scheduling, corporate planning, computer-aided design and m- 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, allo- 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 discov- ies, the ellipsoid method (1979) and interior point approaches (1984) both provide polynomial time algorithms for linear programming. These al- 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 ad- tion, linear programming relaxations are often the basis for many appro- mation algorithms for solving NP-hard problems (e.g. dual heuristics).
Editors and Affiliations
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University of Minnesota, Minneapolis
Ding-Zhu Du
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University of Florida, Gainesville
Panos M. Pardalos
