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| 035 | _a(OCoLC)887507297 | ||
| 050 | 4 |
_aQA402.5 _b.C545123 2014 |
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_aMAT _x003000 _2bisacsh |
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_aMAT _x029000 _2bisacsh |
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| 082 | 0 | 4 |
_a519.64 _223 |
| 049 | _aMAIN | ||
| 245 | 0 | 0 |
_aConcepts of combinatorial optimization / _cedited by Vangelis Th. Paschos. |
| 250 | _a2nd ed. | ||
| 260 |
_aHoboken : _bWiley, _c2014. |
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| 300 | _a1 online resource (409 pages). | ||
| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 490 | 1 | _aISTE | |
| 588 | 0 | _aPrint version record. | |
| 505 | 0 | _aCover; Title Page; Copyright; Contents; Preface; PART I: Complexity of CombinatorialOptimization Problems; Chapter 1: Basic Concepts in Algorithmsand Complexity Theory; 1.1. Algorithmic complexity; 1.2. Problem complexity; 1.3. The classes P, NP and NPO; 1.4. Karp and Turing reductions; 1.5. NP-completeness; 1.6. Two examples of NP-complete problems; 1.6.1. MIN VERTEX COVER; 1.6.2. MAX STABLE; 1.7. A few words on strong and weak NP-completeness; 1.8. A few other well-known complexity classes; 1.9. Bibliography; Chapter 2: Randomized Complexity; 2.1. Deterministic and probabilistic algorithms. | |
| 505 | 8 | _a2.1.1. Complexity of a Las Vegas algorithm2.1.2. Probabilistic complexity of a problem; 2.2. Lower bound technique; 2.2.1. Definitions and notations; 2.2.2. Minimax theorem; 2.2.3. The Loomis lemma and the Yao principle; 2.3. Elementary intersection problem; 2.3.1. Upper bound; 2.3.2. Lower bound; 2.3.3. Probabilistic complexity; 2.4. Conclusion; 2.5. Bibliography; PART II: Classical Solution Methods; Chapter 3: Branch-and-Bound Methods; 3.1. Introduction; 3.2. Branch-and-bound method principles; 3.2.1. Principle of separation; 3.2.2. Pruning principles; 3.2.2.1. Bound. | |
| 505 | 8 | _a3.2.2.2. Evaluation function3.2.2.3. Use of the bound and of the evaluation function for pruning; 3.2.2.4. Other pruning principles; 3.2.2.5. Pruning order; 3.2.3. Developing the tree; 3.2.3.1. Description of development strategies; 3.2.3.2. Compared properties of the depth first and best first strategies; 3.3. A detailed example: the binary knapsack problem; 3.3.1. Calculating the initial bound; 3.3.2. First principle of separation; 3.3.3. Pruning without evaluation; 3.3.4. Evaluation; 3.3.5. Complete execution of the branch-and-bound method for finding only oneoptimal solution. | |
| 505 | 8 | _a3.3.6. First variant: finding all the optimal solutions3.3.7. Second variant: best first search strategy; 3.3.8. Third variant: second principle of separation; 3.4. Conclusion; 3.5. Bibliography; Chapter 4: Dynamic Programming; 4.1. Introduction; 4.2. A first example: crossing the bridge; 4.3. Formalization; 4.3.1. State space, decision set, transition function; 4.3.2. Feasible policies, comparison relationships and objectives; 4.4. Some other examples; 4.4.1. Stock management; 4.4.2. Shortest path bottleneck in a graph; 4.4.3. Knapsack problem; 4.5. Solution; 4.5.1. Forward procedure. | |
| 505 | 8 | _a4.5.2. Backward procedure4.5.3. Principles of optimality and monotonicity; 4.6. Solution of the examples; 4.6.1. Stock management; 4.6.2. Shortest path bottleneck; 4.6.3. Knapsack; 4.7. A few extensions; 4.7.1. Partial order and multicriteria optimization; 4.7.1.1. New formulation of the problem; 4.7.1.2. Solution; 4.7.1.3. Examples; 4.7.2. Dynamic programming with variables; 4.7.2.1. Sequential decision problems under uncertainty; 4.7.2.2. Solution; 4.7.2.3. Example; 4.7.3. Generalized dynamic programming; 4.8. Conclusion; 4.9. Bibliography; PART III: Elements from MathematicalProgramming. | |
| 500 | _aChapter 5: Mixed Integer Linear Programming Models forCombinatorial Optimization Problems. | ||
| 520 | _aCombinatorial optimization is a multidisciplinary scientific area, lying in the interface of three major scientific domains: mathematics, theoretical computer science and management. The three volumes of the Combinatorial Optimization series aim to cover a wide range of topics in this area. These topics also deal with fundamental notions and approaches as with several classical applications of combinatorial optimization. Concepts of Combinatorial Optimization, is divided into three parts:- On the complexity of combinatorial optimization problems, presenting basics abo. | ||
| 650 | 0 | _aCombinatorial optimization. | |
| 650 | 0 | _aProgramming (Mathematics) | |
| 650 | 7 |
_aMATHEMATICS _xApplied. _2bisacsh |
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| 650 | 7 |
_aMATHEMATICS _xProbability & Statistics _xGeneral. _2bisacsh |
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| 650 | 7 |
_aCombinatorial optimization. _2fast _0(OCoLC)fst00868980 |
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| 650 | 7 |
_aProgramming (Mathematics) _2fast _0(OCoLC)fst01078701 |
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| 650 | 7 |
_aKombinatorische Optimierung. _0(DE-588)4031826-6 _2gnd |
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| 655 | 4 | _aElectronic books. | |
| 700 | 1 | _aPaschos, Vangelis Th. | |
| 776 | 0 | 8 |
_iPrint version: _aPaschos, Vangelis Th. _tConcepts of Combinatorial Optimization. _dHoboken : Wiley, ©2014 _z9781848216563 |
| 830 | 0 | _aISTE. | |
| 856 | 4 | 0 |
_uhttp://onlinelibrary.wiley.com/book/10.1002/9781119005216 _zWiley Online Library [Free Download only for SUST IP] |
| 938 |
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