Handbook of combinatorial optimization, 3. sējumsDingzhu Du, Panos M. Pardalos Springer Science & Business Media, 1998. gada 15. dec. - 2403 lappuses 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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Saturs
Methodology | 32 |
Single Machine Problems | 40 |
UnitLength Jobs and Preemption | 65 |
No Preemption | 72 |
MultiStage Problems | 86 |
Concluding Remarks | 127 |
Warwick Business School | 129 |
University of Warwick | 137 |
Asymptotic Behavior | 313 |
Algorithmic Aspects of Domination in Graphs | 339 |
Selected Algorithmic Techniques for Parallel Optimization | 407 |
Multispace Search for Combinatorial Optimization | 457 |
The Equitable Coloring of Graphs | 543 |
Randomized Parallel Algorithms | 567 |
Tabu Search | 621 |
Introduction | 709 |
1 | 140 |
Institut für Mathematik | 166 |
Routing and Topology Embedding in Lightwave Networks | 171 |
The Quadratic Assignment Problem | 241 |
QAP Polytopes | 258 |
Exact Solution Methods | 287 |
Available Computer Codes for the | 302 |
Scheduling Models | 736 |
759 | |
779 | |
781 | |
845 | |
Bieži izmantoti vārdi un frāzes
algorithm for problem applied approach approximation algorithm bound algorithm branch and bound branching rule Bruijn graphs C.N. Potts chordal graphs Cmax coloring combinatorial optimization competitive ratio completion connected denote Discrete dominating set due date dynamic programming edges embedding feasible formulation genetic algorithm graph G heuristic Hoogeveen IEEE improved instances integer interval graphs iteration Journal of Operational Lawler Lemma linear programming local search lower bound machine scheduling makespan Mathematics matrix maximum method minimize minimum move multispace search neighborhood node objective function Operations Research optical passive star optimal solution parallel machines partition path permutation pmtn polynomially solvable precedence constraints processing processors quadratic assignment problem random relaxation release dates Sahni scheduling problems search algorithm search space Section sequence Shmoys simulated annealing single machine slots solving strategy strongly NP-hard structure subproblems subset tabu search Theorem tion transmitter tree tunable variables vertex vertices Wassenhove wavelength Woeginger