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＜電子ブック＞
Handbook of Combinatorial Optimization : Supplement Volume A / edited by Ding-Zhu Du, Panos M. Pardalos

版	1st ed. 1999.
出版者	(New York, NY : Springer US : Imprint: Springer)
出版年	1999
本文言語	英語
大きさ	VIII, 648 p : online resource
著者標目	Du, Ding-Zhu editor Pardalos, Panos M editor SpringerLink (Online service)
件　名	LCSH:Discrete mathematics LCSH:Probabilities LCSH:Computer science -- Mathematics  全ての件名で検索 LCSH:Computer science FREE:Discrete Mathematics FREE:Probability Theory FREE:Discrete Mathematics in Computer Science FREE:Theory of Computation FREE:Mathematical Applications in Computer Science
一般注記	The Maximum Clique Problem -- Linear Assignment Problems and Extensions -- Bin Packing Approximation Algorithms: Combinatorial Analysis -- Feedback Set Problems -- Neural Networks Approaches for Combinatorial Optimization Problems -- Frequency Assignment Problems -- Algorithms for the Satisfiability (SAT) Problem -- The Steiner Ratio of Lp-planes -- A Cogitative Algorithm for Solving the Equal Circles Packing Problem -- Author Index 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) HTTP:URL=https://doi.org/10.1007/978-1-4757-3023-4

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