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
6.–10. rezultāts no 44.
9. lappuse
... cost functions. The proof for max type cost functions is similar. For sum type cost functions, it is sufficient to prove the result when the cost vectors and differ in only one position. Let for and Consider any solution There are two ...
... cost functions. The proof for max type cost functions is similar. For sum type cost functions, it is sufficient to prove the result when the cost vectors and differ in only one position. Let for and Consider any solution There are two ...
10. lappuse
... costs of The similarity in the procedural aspects of the data correcting step de- scribed above (and illustrated in the example) to fathoming rules used in branch and bound implementations makes it convenient to incorporate data ...
... costs of The similarity in the procedural aspects of the data correcting step de- scribed above (and illustrated in the example) to fathoming rules used in branch and bound implementations makes it convenient to incorporate data ...
11. lappuse
... cost Hamiltonian cycle in this graph. This is one of the most studied problems in combinatorial optimization, see Lawler et al. [26] and Gutin and Punnen [20] for a detailed introduction. 4.1 The Data Correcting Algorithm The data ...
... cost Hamiltonian cycle in this graph. This is one of the most studied problems in combinatorial optimization, see Lawler et al. [26] and Gutin and Punnen [20] for a detailed introduction. 4.1 The Data Correcting Algorithm The data ...
13. lappuse
... costs of two solutions for a problem instance, so the stronger the bound, the better would be the performance of any enumeration algorithm dependent on such bounds. It is possible to obtain stronger performance measures for ATSP ...
... costs of two solutions for a problem instance, so the stronger the bound, the better would be the performance of any enumeration algorithm dependent on such bounds. It is possible to obtain stronger performance measures for ATSP ...
15. lappuse
... cost of patching is 9. If we correct the problem data, we will see that the entry (corresponding to arc (5,1)) contributes the maximum amount (7) to the patching. Hence we branch on each arc in the cycle (4564), and construct three ...
... cost of patching is 9. If we correct the problem data, we will see that the entry (corresponding to arc (5,1)) contributes the maximum amount (7) to the patching. Hence we branch on each arc in the cycle (4564), and construct three ...
Saturs
19 | |
Preface | 51 |
Probabilistic Verification and NonApproximablity | 82 |
Steiner Trees in Industry | 193 |
Networkbased Model and Algorithms in Data Mining | 217 |
The Generalized Assignment Problem and Extensions | 259 |
Solution Methods | 271 |
3 | 282 |
Additional Approaches to the | 297 |
Concluding Remarks | 304 |
Optimal Rectangular Partitions | 312 |
Connected Dominating Sets in Sensor Networks | 329 |
Author Index | 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 complexity Computer Science conjecture connected dominating set consider convex corresponding cost data correcting dataset decoding defined degree polynomial denote distribution edges elements encoding feasible Feige finite 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 obtained Operations Research optimal solution optimization problems parameters PCP theorem 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 variables vector verifier vertex vertices WCDS