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 48.
. lappuse
... approximation algorithms for solving NP-hard problems (e.g. dual heuristics). Two other developments with a great effect on combinatorial optimization are the design of efficient integer programming software and the availability of ...
... approximation algorithms for solving NP-hard problems (e.g. dual heuristics). Two other developments with a great effect on combinatorial optimization are the design of efficient integer programming software and the availability of ...
2. lappuse
... approximation algorithms. Much remains to be done in this area. This chapter describes a step in the direction of incorporating polynomially solvable special cases into approximation algorithms. We review data correcting algorithms ...
... approximation algorithms. Much remains to be done in this area. This chapter describes a step in the direction of incorporating polynomially solvable special cases into approximation algorithms. We review data correcting algorithms ...
4. lappuse
... algorithm to find an approximate minimum of afunction over a certain domain D. The procedure consists of three steps: the ... approximation of by regular functions is called data correcting, since an easy way of obtaining the regular ...
... algorithm to find an approximate minimum of afunction over a certain domain D. The procedure consists of three steps: the ... approximation of by regular functions is called data correcting, since an easy way of obtaining the regular ...
8. lappuse
... algorithm presented in the previous section, i.e. partitioning and bounding ... approximating over subsets of the solution space. Notice that the cost function is a ... approximation of the cost function of the original instance in For ...
... algorithm presented in the previous section, i.e. partitioning and bounding ... approximating over subsets of the solution space. Notice that the cost function is a ... approximation of the cost function of the original instance in For ...
30. lappuse
... algorithm of choice. We consider instances of the QCP with size ranging from 100 to 500 and densities varying ... approximate solutions with DCA-MSFr(3) and DCA-MSF. The entries marked '*' could not be solved within 10 minutes. From the ...
... algorithm of choice. We consider instances of the QCP with size ranging from 100 to 500 and densities varying ... approximate solutions with DCA-MSFr(3) and DCA-MSF. The entries marked '*' could not be solved within 10 minutes. From the ...
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