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 74.
3. lappuse
... Consider a real-valued function where D is the domain on which the function is defined. We assume that is not analytically tractable over D, but is computable in polynomial time for any and concern our- selves with the problem of ...
... Consider a real-valued function where D is the domain on which the function is defined. We assume that is not analytically tractable over D, but is computable in polynomial time for any and concern our- selves with the problem of ...
5. lappuse
... Consider the problem of finding an of the function shown in Figure 1. The function is assumed to be well-defined, though analytically intractable on the domain D. Figure 1: A general function The data correcting approach can be used to ...
... Consider the problem of finding an of the function shown in Figure 1. The function is assumed to be well-defined, though analytically intractable on the domain D. Figure 1: A general function The data correcting approach can be used to ...
6. lappuse
... Consider the partition{D1, D2, D3, D4, D5} of D shown in Figure 2. Let us suppose that we have a regular function with solution to in the domain D1. We then consider the next interval in the partition, D2. We first obtain a lower bound ...
... Consider the partition{D1, D2, D3, D4, D5} of D shown in Figure 2. Let us suppose that we have a regular function with solution to in the domain D1. We then consider the next interval in the partition, D2. We first obtain a lower bound ...
8. lappuse
... consider is that of obtaining regular functions approximating over subsets of the solution space. So if the values of the entries in C are changed, undergoes a change as well. Therefore, cost functions corresponding to polynomially ...
... consider is that of obtaining regular functions approximating over subsets of the solution space. So if the values of the entries in C are changed, undergoes a change as well. Therefore, cost functions corresponding to polynomially ...
9. lappuse
... Consider any solution There are two cases to consider: In this case it is clear that In this case, of Lemma 3.1 is an upper bound for the difference between and where and are optimal solutions to and respectively. Therefore, for any ...
... Consider any solution There are two cases to consider: In this case it is clear that In this case, of Lemma 3.1 is an upper bound for the difference between and where and are optimal solutions to and respectively. Therefore, for any ...
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 |
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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