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 93.
3. lappuse
... function value of is close to the minimum function value of over the domain. at the best among the minima of all the over their respective domains Theorem 2.1 Let and let Then Proof: Let Then for. Data Correcting Algorithms 3.
... function value of is close to the minimum function value of over the domain. at the best among the minima of all the over their respective domains Theorem 2.1 Let and let Then Proof: Let Then for. Data Correcting Algorithms 3.
7. lappuse
... proof of Theorem 2.1, it is sufficient for to be a cover of D (as opposed to a partition as required in the pseudocode of DC-G). approximating do not need to have the same functional form. For. 3. Data. Correcting. for. Combinatorial.
... proof of Theorem 2.1, it is sufficient for to be a cover of D (as opposed to a partition as required in the pseudocode of DC-G). approximating do not need to have the same functional form. For. 3. Data. Correcting. for. Combinatorial.
9. lappuse
... Proof: We will prove the result for sum type cost functions. The proof for max type cost functions is similar. For sum type cost functions, it is sufficient to prove the result when the cost vectors and differ in only one position. Let ...
... Proof: We will prove the result for sum type cost functions. The proof for max type cost functions is similar. For sum type cost functions, it is sufficient to prove the result when the cost vectors and differ in only one position. Let ...
23. lappuse
... Proof: We prove only part 1 since the proof of the part 2 is similar. We may represent the partition of interval [S, T] as follows: Using this representation on the interval we have Let There are twocases to consider: and can apply ...
... Proof: We prove only part 1 since the proof of the part 2 is similar. We may represent the partition of interval [S, T] as follows: Using this representation on the interval we have Let There are twocases to consider: and can apply ...
24. lappuse
... proof. In the second case which implies that or Corollary 5.5 (Preservation rules of order Let be a submodular func- tion on and let Then the following assertions hold. 1. First Preservation Rule of Order If then ). 2. Second ...
... proof. In the second case which implies that or Corollary 5.5 (Preservation rules of order Let be a submodular func- tion on and let Then the following assertions hold. 1. First Preservation Rule of Order If then ). 2. Second ...
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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