Handbook of Combinatorial Optimization: Supplement Volume B, 2. sējumsDing-Zhu Du, Panos M. Pardalos Springer Science & Business Media, 2006. gada 18. aug. - 394 lappuses 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). |
No grāmatas satura
1.5. rezultāts no 12.
10. lappuse
... subproblems. The similarity in the procedural aspects of the data correcting step described above (and illustrated in the example) to fathoming rules used in branch and bound implementations makes it convenient to incorporate data ...
... subproblems. The similarity in the procedural aspects of the data correcting step described above (and illustrated in the example) to fathoming rules used in branch and bound implementations makes it convenient to incorporate data ...
14. lappuse
... subproblems, 11 2 - 11 10 a patching algorithm to create feasible solutions, and compute proximity measures, and the ... subproblem, we construct the assignment problem solution and then patch it. We also correct the original matrix to a ...
... subproblems, 11 2 - 11 10 a patching algorithm to create feasible solutions, and compute proximity measures, and the ... subproblem, we construct the assignment problem solution and then patch it. We also correct the original matrix to a ...
15. lappuse
... subproblems, the first with the additional constraint that arc (4,5) be excluded from the solution, the second with ... subproblems. However if the value of is set to 1, then enumeration stops after node 4, since the lower bound obtained ...
... subproblems, the first with the additional constraint that arc (4,5) be excluded from the solution, the second with ... subproblems. However if the value of is set to 1, then enumeration stops after node 4, since the lower bound obtained ...
29. lappuse
... subproblems generated when is increased from a value of 0 (i.e. DCA-MSF) to 5. As is intuitive, the number of subproblems reduce with increasing for all density values. Figure 9 shows the execution times of with varying and values ...
... subproblems generated when is increased from a value of 0 (i.e. DCA-MSF) to 5. As is intuitive, the number of subproblems reduce with increasing for all density values. Figure 9 shows the execution times of with varying and values ...
30. lappuse
... subproblems approximately balance the increase in the time at each subproblem for values in the range 0 through 4. When the computation times for increase significantly for all densities. From Figure 9 it seems that for dense graphs ...
... subproblems approximately balance the increase in the time at each subproblem for values in the range 0 through 4. When the computation times for increase significantly for all densities. From Figure 9 it seems that for dense graphs ...
Saturs
2 | |
5 | |
The Steiner Ratio of BanachMinkowski Space A Survey | 55 |
Probabilistic Verification and NonApproximablity 83 | 82 |
Steiner Trees in Industry Xiuzhen Cheng Yingshu Li DingZhu Du and Hung Q Ngo | 193 |
Networkbased Model and Algorithms in Data Mining | 217 |
The Generalized Assignment Problem and Extensions Dolores Romero Morales and H Edwin Romeijn | 259 |
Additional Approaches to the | 297 |
Concluding Remarks | 304 |
Optimal Rectangular Partitions Xiuzhen Cheng DingZhu Du JoonMo Kim and Lu Ruan 313 | 329 |
Introduction | 330 |
Author Index 371 | 370 |
Subject Index | 381 |
Citi izdevumi - Skatīt visu
Handbook of combinatorial optimization, 2. sējums Dingzhu Du,Panos M. Pardalos Ierobežota priekšskatīšana - 1998 |
Handbook of Combinatorial Optimization: Supplement Volume B Ding-Zhu Du,Panos M. Pardalos Priekšskatījums nav pieejams - 2011 |
Bieži izmantoti vārdi un frāzes
agent applied approximation algorithms approximation scheme Arora assignment problem Banach-Minkowski capacity constraints CDS construction checkable cluster clusterhead combinatorial optimization complexity Computer Science conjecture connected dominating set consider convex corresponding cost data correcting algorithm dataset decoding defined denote distribution edges elements encoding Feige graph product greedy heuristic guillotine Hadamard code Håstad holographic codes independent sets input Journal Lemma length linear lower bound market graph matrix maximum clique minimal minimum spanning tree multi-degree neighbors Neural Networks nodes non-approximability NP complete NP-hard obtain Operations Research optimal solution optimization problems parameters performance ratio polynomial polynomial-time approximation polynomial-time approximation scheme probabilistic problem instances procedure proof prove random rectangular partition rectilinear reduce Romero Morales Section segment solve space SPLP Steiner minimum tree Steiner points Steiner ratio Steiner tree problem subproblems subset tasks techniques Theorem Theory upper bound variables vector verifier vertex vertices WCDS