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 50.
9. lappuse
... optimal solutions between two instances and of the problem is an upper bound to the Proof: We will prove the result for sum type cost functions. The proof for ... feasible solution.) We also construct an instance. Data Correcting Algorithms ...
... optimal solutions between two instances and of the problem is an upper bound to the Proof: We will prove the result for sum type cost functions. The proof for ... feasible solution.) We also construct an instance. Data Correcting Algorithms ...
10. lappuse
... feasible solution.) We also construct an instance of the problem, which will have as an optimal solution. The proximity measure and of an optimal solution to I. is called a correction of the instance I. If the proximity measure is not ...
... feasible solution.) We also construct an instance of the problem, which will have as an optimal solution. The proximity measure and of an optimal solution to I. is called a correction of the instance I. If the proximity measure is not ...
11. lappuse
... 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 ... optimal solutions to and and let. Data Correcting Algorithms 11.
... 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 ... optimal solutions to and and let. Data Correcting Algorithms 11.
12. lappuse
... optimal solutions to and and let and respectively represent the lengths of and in instance Then Before presenting a ... solution (1245631) (corresponding to Notice that (1245631) would be an optimal solution to the assignment problem if ...
... optimal solutions to and and let and respectively represent the lengths of and in instance Then Before presenting a ... solution (1245631) (corresponding to Notice that (1245631) would be an optimal solution to the assignment problem if ...
14. lappuse
... feasible solutions, and compute prox- imity measures, and the patched solution derived from the assignment solution as a feasible solution in the domain. The branching rule used in this example is as follows. At each sub- problem, we ...
... feasible solutions, and compute prox- imity measures, and the patched solution derived from the assignment solution as a feasible solution in the domain. The branching rule used in this example is as follows. At each sub- problem, we ...
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