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 45.
6. lappuse
... lower bound on the minimum value of on D2. If this bound is larger than we ignore this domain and examine domain D3. Let this not be the case in our example. So we construct a regular function with and find its minimum point over D2 ...
... lower bound on the minimum value of on D2. If this bound is larger than we ignore this domain and examine domain D3. Let this not be the case in our example. So we construct a regular function with and find its minimum point over D2 ...
8. lappuse
... lower bounds to the value that the cost function can attain over a particular member of the partition. If this bound is not better than the best solution found thus far, the corresponding subset in the partition is ignored in the search ...
... lower bounds to the value that the cost function can attain over a particular member of the partition. If this bound is not better than the best solution found thus far, the corresponding subset in the partition is ignored in the search ...
11. lappuse
... lower bound is to be computed, or which solution to choose in the feasible region, or how to partition the domain into sub-domains. These are details that vary from problem to problem, and are an important part in the engineering ...
... lower bound is to be computed, or which solution to choose in the feasible region, or how to partition the domain into sub-domains. These are details that vary from problem to problem, and are an important part in the engineering ...
13. lappuse
... bound to the difference between the costs of two solutions for a problem instance, so the stronger the bound, the better would be the performance of any enumeration ... lower bound can be incorporated into. Data Correcting Algorithms 13.
... bound to the difference between the costs of two solutions for a problem instance, so the stronger the bound, the better would be the performance of any enumeration ... lower bound can be incorporated into. Data Correcting Algorithms 13.
14. lappuse
... lower bound can be incorporated into DCA-ATSP to make it more efficient. We next illustrate the DCA-ATSP algorithm above on an instance of the ATSP. Consider the 8-city ATSP instance with the distance matrix shown below. (This example ...
... lower bound can be incorporated into DCA-ATSP to make it more efficient. We next illustrate the DCA-ATSP algorithm above on an instance of the ATSP. Consider the 8-city ATSP instance with the distance matrix shown below. (This example ...
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