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 39.
7. lappuse
... feasible solutions S into smaller subsets. This is done under the assumption that optimizing the cost function over a more restricted solution space is easier than optimizing it over the whole space. Data Correcting Algorithms 7.
... feasible solutions S into smaller subsets. This is done under the assumption that optimizing the cost function over a more restricted solution space is easier than optimizing it over the whole space. Data Correcting Algorithms 7.
8. lappuse
... subset in the partition is ignored in the search for an optimal solution. The second method of fathoming is by computing the optimum value of the cost function over the particular subset of the solution space (if that can be easily ...
... subset in the partition is ignored in the search for an optimal solution. The second method of fathoming is by computing the optimum value of the cost function over the particular subset of the solution space (if that can be easily ...
19. lappuse
... subsets of N. A function is called submodular if for each I, The solution process of many classical combinatorial optimization problems, like the generalized transportation problem, the quadratic cost partition (QCP) problem with ...
... subsets of N. A function is called submodular if for each I, The solution process of many classical combinatorial optimization problems, like the generalized transportation problem, the quadratic cost partition (QCP) problem with ...
29. lappuse
... subset such that the function will be maximized. The density of a QCP instance is the ratio of the number of finite values to and is expressed as a percentage. It is proved in Theorem 2.2 of Lee et al. [27] that is submodular. In ...
... subset such that the function will be maximized. The density of a QCP instance is the ratio of the number of finite values to and is expressed as a percentage. It is proved in Theorem 2.2 of Lee et al. [27] that is submodular. In ...
34. lappuse
... subset P of I consists of two components, namely the fixed costs and the transportation costs i.e. and the SPLP is the problem of finding In the remainder of this subsection we describe the pseudo-Boolean formulation of the SPLP due to ...
... subset P of I consists of two components, namely the fixed costs and the transportation costs i.e. and the SPLP is the problem of finding In the remainder of this subsection we describe the pseudo-Boolean formulation of the SPLP due to ...
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