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140122  eng 
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a 9781475730234

100 
1 

a Du, DingZhu
e [editor]

245 
0 
0 
a Handbook of Combinatorial Optimization
h Elektronische Ressource
b Supplement Volume A
c edited by DingZhu Du, Panos M. Pardalos

250 


a 1st ed. 1999

260 


a New York, NY
b Springer US
c 1999, 1999

300 


a VIII, 648 p
b online resource

505 
0 

a 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 Lpplanes  A Cogitative Algorithm for Solving the Equal Circles Packing Problem  Author Index

653 


a Computer science

653 


a Computer science / Mathematics

653 


a Discrete Mathematics in Computer Science

653 


a Probability Theory

653 


a Mathematical Applications in Computer Science

653 


a Discrete Mathematics

653 


a Discrete mathematics

653 


a Theory of Computation

653 


a Probabilities

700 
1 

a Pardalos, Panos M.
e [editor]

041 
0 
7 
a eng
2 ISO 6392

989 


b SBA
a Springer Book Archives 2004

028 
5 
0 
a 10.1007/9781475730234

856 
4 
0 
u https://doi.org/10.1007/9781475730234?nosfx=y
x Verlag
3 Volltext

082 
0 

a 511.1

520 


a 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, computeraided 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 polynomialtime 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 NPhard problems (e.g. dualheuristics)
