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Paradigms of combinatorial optimizat...

Paschos, Vangelis Th.

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Paradigms of combinatorial optimization = problems and new approaches /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Paradigms of combinatorial optimization/ edited by Vangelis Th. Paschos.
Reminder of title:	problems and new approaches /
other author:	Paschos, Vangelis Th.
Published:	London :ISTE, Ltd. ; : 2014.,
Description:	1 online resource (815 p.)
[NT 15003449]:	Cover; Title Page; Copyright; Contents; Preface; PART I: Paradigmatic Problems; Chapter 1: Optimal Satisfiability; 1.1. Introduction; 1.2. Preliminaries; 1.2.1. Constraint satisfaction problems: decision and optimization versions; 1.2.2. Constraint types; 1.3. Complexity of decision problems; 1.4. Complexity and approximation of optimization problems; 1.4.1. Maximization problems; 1.4.2. Minimization problems; 1.5. Particular instances of constraint satisfaction problems; 1.5.1. Planar instances; 1.5.2. Dense instances; 1.5.3. Instances with a bounded number of occurrences.
[NT 15003449]:	1.6. Satisfiability problems under global constraints1.7. Conclusion; 1.8. Bibliography; Chapter 2: Scheduling Problems; 2.1. Introduction; 2.2. New techniques for approximation; 2.2.1. Linear programming and scheduling; 2.2.1.1. Single machine problems; 2.2.1.2. Problems with m machines; 2.2.2. An approximation scheme for PCmax; 2.3. Constraints and scheduling; 2.3.1. The monomachine constraint; 2.3.2. The cumulative constraint; 2.3.3. Energetic reasoning; 2.4. Non-regular criteria; 2.4.1. PERT with convex costs; 2.4.1.1. The equality graph and its blocks; 2.4.1.2. Generic algorithm.
[NT 15003449]:	2.4.1.3. Complexity of the generic algorithm2.4.2. Minimizing the early-tardy cost on one machine; 2.4.2.1. Special cases; 2.4.2.2. The lower bound; 2.4.2.3. The branch-and-bound algorithm; 2.4.2.4. Lower bounds in a node of the search tree; 2.4.2.5. Upper bound; 2.4.2.6. Branching rule; 2.4.2.7. Dominance rules; 2.4.2.8. Experimental results; 2.5. Bibliography; Chapter 3: Location Problems; 3.1. Introduction; 3.1.1. Weber's problem; 3.1.2. A classification; 3.2. Continuous problems; 3.2.1. Complete covering; 3.2.2. Maximal covering; 3.2.2.1. Fixed radius; 3.2.2.2. Variable radius.
[NT 15003449]:	3.2.3. Empty covering3.2.4. Bicriteria models; 3.2.5. Covering with multiple resources; 3.3. Discrete problems; 3.3.1. p-Center; 3.3.2. p-Dispersion; 3.3.3. p-Median; 3.3.3.1. Fixed charge; 3.3.4. Hub; 3.3.5. p-Maxisum; 3.4. On-line problems; 3.5. The quadratic assignment problem; 3.5.1. Definitions and formulations of the problem; 3.5.2. Complexity; 3.5.3. Relaxations and lower bounds; 3.5.3.1. Linear relaxations; 3.5.3.2. Semi-definite relaxations; 3.5.3.3. Convex quadratic relaxations; 3.6. Conclusion; 3.7. Bibliography; Chapter 4: MiniMax Algorithms and Games; 4.1. Introduction.
[NT 15003449]:	4.2. Games of no chance: the simple cases4.3. The case of complex no chance games; 4.3.1. Approximative evaluation; 4.3.2. Horizon effect; 4.3.3. [alpha]-[beta] pruning; 4.4. Quiescence search; 4.4.1. Other refinements of the MiniMax algorithm; 4.5. Case of games using chance; 4.6. Conclusion; 4.7. Bibliography; Chapter 5: Two-dimensional Bin Packing Problems; 5.1. Introduction; 5.2. Models; 5.2.1. ILP models for level packing; 5.3. Upper bounds; 5.3.1. Strip packing; 5.3.2. Bin packing: two-phase heuristics; 5.3.3. Bin packing: one-phase level heuristics; 5.3.4. Bin packing: one-phase non-level heuristics.
Subject:	Combinatorial optimization. -
Online resource:	http://onlinelibrary.wiley.com/book/10.1002/9781119005353
ISBN:	9781119005353

Paradigms of combinatorial optimization = problems and new approaches /
Paradigms of combinatorial optimizationproblems and new approaches /[electronic resource] :edited by Vangelis Th. Paschos. - 2nd ed. - London :ISTE, Ltd. ;2014. - 1 online resource (815 p.) - ISTE. - ISTE..

Includes bibliographical references and index.

Cover; Title Page; Copyright; Contents; Preface; PART I: Paradigmatic Problems; Chapter 1: Optimal Satisfiability; 1.1. Introduction; 1.2. Preliminaries; 1.2.1. Constraint satisfaction problems: decision and optimization versions; 1.2.2. Constraint types; 1.3. Complexity of decision problems; 1.4. Complexity and approximation of optimization problems; 1.4.1. Maximization problems; 1.4.2. Minimization problems; 1.5. Particular instances of constraint satisfaction problems; 1.5.1. Planar instances; 1.5.2. Dense instances; 1.5.3. Instances with a bounded number of occurrences.

Combinatorial 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.

ISBN: 9781119005353Subjects--Topical Terms:

544215
Combinatorial optimization.

LC Class. No.: QA402.5 / .P384 2014

Dewey Class. No.: 519.703

Paradigms of combinatorial optimization = problems and new approaches /
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245 0 0 $a Paradigms of combinatorial optimization $h [electronic resource] : $b problems and new approaches / $c edited by Vangelis Th. Paschos.
250 $a 2nd ed.
260 $a London : $b ISTE, Ltd. ; $a Hoboken : $b Wiley, $c 2014.
300 $a 1 online resource (815 p.)
490 1 $a ISTE
504 $a Includes bibliographical references and index.
505 0 $a Cover; Title Page; Copyright; Contents; Preface; PART I: Paradigmatic Problems; Chapter 1: Optimal Satisfiability; 1.1. Introduction; 1.2. Preliminaries; 1.2.1. Constraint satisfaction problems: decision and optimization versions; 1.2.2. Constraint types; 1.3. Complexity of decision problems; 1.4. Complexity and approximation of optimization problems; 1.4.1. Maximization problems; 1.4.2. Minimization problems; 1.5. Particular instances of constraint satisfaction problems; 1.5.1. Planar instances; 1.5.2. Dense instances; 1.5.3. Instances with a bounded number of occurrences.
505 8 $a 1.6. Satisfiability problems under global constraints1.7. Conclusion; 1.8. Bibliography; Chapter 2: Scheduling Problems; 2.1. Introduction; 2.2. New techniques for approximation; 2.2.1. Linear programming and scheduling; 2.2.1.1. Single machine problems; 2.2.1.2. Problems with m machines; 2.2.2. An approximation scheme for PCmax; 2.3. Constraints and scheduling; 2.3.1. The monomachine constraint; 2.3.2. The cumulative constraint; 2.3.3. Energetic reasoning; 2.4. Non-regular criteria; 2.4.1. PERT with convex costs; 2.4.1.1. The equality graph and its blocks; 2.4.1.2. Generic algorithm.
505 8 $a 2.4.1.3. Complexity of the generic algorithm2.4.2. Minimizing the early-tardy cost on one machine; 2.4.2.1. Special cases; 2.4.2.2. The lower bound; 2.4.2.3. The branch-and-bound algorithm; 2.4.2.4. Lower bounds in a node of the search tree; 2.4.2.5. Upper bound; 2.4.2.6. Branching rule; 2.4.2.7. Dominance rules; 2.4.2.8. Experimental results; 2.5. Bibliography; Chapter 3: Location Problems; 3.1. Introduction; 3.1.1. Weber's problem; 3.1.2. A classification; 3.2. Continuous problems; 3.2.1. Complete covering; 3.2.2. Maximal covering; 3.2.2.1. Fixed radius; 3.2.2.2. Variable radius.
505 8 $a 3.2.3. Empty covering3.2.4. Bicriteria models; 3.2.5. Covering with multiple resources; 3.3. Discrete problems; 3.3.1. p-Center; 3.3.2. p-Dispersion; 3.3.3. p-Median; 3.3.3.1. Fixed charge; 3.3.4. Hub; 3.3.5. p-Maxisum; 3.4. On-line problems; 3.5. The quadratic assignment problem; 3.5.1. Definitions and formulations of the problem; 3.5.2. Complexity; 3.5.3. Relaxations and lower bounds; 3.5.3.1. Linear relaxations; 3.5.3.2. Semi-definite relaxations; 3.5.3.3. Convex quadratic relaxations; 3.6. Conclusion; 3.7. Bibliography; Chapter 4: MiniMax Algorithms and Games; 4.1. Introduction.
505 8 $a 4.2. Games of no chance: the simple cases4.3. The case of complex no chance games; 4.3.1. Approximative evaluation; 4.3.2. Horizon effect; 4.3.3. [alpha]-[beta] pruning; 4.4. Quiescence search; 4.4.1. Other refinements of the MiniMax algorithm; 4.5. Case of games using chance; 4.6. Conclusion; 4.7. Bibliography; Chapter 5: Two-dimensional Bin Packing Problems; 5.1. Introduction; 5.2. Models; 5.2.1. ILP models for level packing; 5.3. Upper bounds; 5.3.1. Strip packing; 5.3.2. Bin packing: two-phase heuristics; 5.3.3. Bin packing: one-phase level heuristics; 5.3.4. Bin packing: one-phase non-level heuristics.
520 $a Combinatorial 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.
588 $a Description based on print version record.
650 0 $a Combinatorial optimization. $3 544215
650 0 $a Programming (Mathematics) $3 547124
700 1 $a Paschos, Vangelis Th. $3 1314724
830 0 $a ISTE. $3 2084372
856 4 0 $u http://onlinelibrary.wiley.com/book/10.1002/9781119005353