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Handbook of Combinatorial Optimization 1998 Edition
Contributor(s): Ding-Zhu Du (Author), Pardalos, Panos M. (Author)

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 ISBN: 1461379873     ISBN-13: 9781461379874
Publisher: Springer
OUR PRICE: $313.49  

Binding Type: Paperback - See All Available Formats & Editions
Published: November 2011
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Temporarily out of stock - Will ship within 2 to 5 weeks
Additional Information
BISAC Categories:
- Mathematics | Combinatorics
- Computers | Data Processing
- Computers | Computer Science
Dewey: 004.015
Physical Information: 4.9" H x 5.8" W x 9.2" L (7.20 lbs) 2406 pages
Features: Illustrated
 
Descriptions, Reviews, Etc.
Publisher Description:
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. dual heuristics).
 
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