[下载]Elsevier06新书《约束规划手册》（Handbook of Constraint Programming）-经管之家官网！

<p><strong><font size="3">Handbook of Constraint Programming (Foundations of Artificial Intelligence)</font></strong></p><p><strong><font color="#ff0000" size="3">约束规划对经济学的意义不言而喻，这本书涵括了约束规划的所有领域，特此推荐！</font></strong></p><font size="3"><li><strong><img class="bookDetail" height="226" alt="Handbook of Constraint Programming" src="http://img.ebooks.com/previews/000/000274/000274219/000274219-sml-1.jpg" width="161" style="WIDTH: 161px; HEIGHT: 226px;"/></strong><br/>  </li><li><strong>AMAZON价格： <table class="product"><tbody><tr><td class="productLabel">List Price:</td><td class="listprice"><font color="#f70909"><strong>$180.00</strong></font><br/>       </td></tr><tr><td class="productLabel">Price:</td><td><font color="#ee3d11"><b class="price">$169.20</b><br/>        </font></td></tr></tbody></table></strong></li><li><strong>Hardcover: </strong><strong><font color="#ee3d11" size="4">978 pages</font></strong><br/>  </li></font><strong><font color="#ee3d11" size="4">978 pages</font></strong><br/> <font size="3"><li><strong>Publisher:</strong> Elsevier Science <strong>(October 13, 2006)</strong><br/>  </li><li><strong>Language:</strong> English </li><li><strong>Book Description<br/></strong>Constraint programming is a powerful paradigm for solving combinatorial search problems that draws on a wide range of techniques from artificial intelligence, computer science, databases, programming languages, and operations research. Constraint programming is currently applied with success to many domains, such as scheduling, planning, vehicle routing, configuration, networks, and bioinformatics.<br/><br/>The aim of this handbook is to capture the full breadth and depth of the constraint programming field and to be encyclopedic in its scope and coverage. While there are several excellent books on constraint programming, such books necessarily focus on the main notions and techniques and cannot cover also extensions, applications, and languages. The handbook gives a reasonably complete coverage of all these lines of work, based on constraint programming, so that a reader can have a rather precise idea of the whole field and its potential. Of course each line of work is dealt with in a survey-like style, where some details may be neglected in favor of coverage. However, the extensive bibliography of each chapter will help the interested readers to find suitable sources for the missing details. Each chapter of the handbook is intended to be a self-contained survey of a topic, and is written by one or more authors who are leading researchers in the area.<br/><br/>The intended audience of the handbook is researchers, graduate students, higher-year undergraduates and practitioners who wish to learn about the state-of-the-art in constraint programming. No prior knowledge about the field is necessary to be able to read the chapters and gather useful knowledge. Researchers from other fields should find in this handbook an effective way to learn about constraint programming and to possibly use some of the constraint programming concepts and techniques in their work, thus providing a means for a fruitful cross-fertilization among different research areas.<br/><br/>The handbook is organized in two parts. The first part covers the basic foundations of constraint programming, including the history, the notion of constraint propagation, basic search methods, global constraints, tractability and computational complexity, and important issues in modeling a problem as a constraint problem. The second part covers constraint languages and solver, several useful extensions to the basic framework (such as interval constraints, structured domains, and distributed CSPs), and successful application areas for constraint programming.<br/><br/><strong>-<font color="#f70909"> Covers the whole field of constraint programming<br/>- Survey-style chapters<br/>- Five chapters on applications </font></strong></li><li><font color="#f70909"><strong><font color="#000000">Contents</font><br/></strong><font color="#000000"><strong>Foreword v<br/></strong>Editors vii<br/>Contributors ix<br/>Contents xiii<br/><strong><font color="#ff0000">I Foundations 1</font></strong><br/><strong>1 Introduction 3</strong><br/>Francesca Rossi, Peter van Beek, TobyWalsh<br/>1.1 Purpose of the Handbook . . . . . . . . . . . . . . . . . . . . . . . . . 4<br/>1.2 Structure andContent . . . . . . . . . . . . . . . . . . . . . . . . . . . 4<br/>1.3 Future Research . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10<br/><strong>2 Constraint Satisfaction: An Emerging Paradigm 13</strong><br/>Eugene C. Freuder and Alan K. Mackworth<br/>2.1 TheEarlyDays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 13<br/>2.2 The Constraint Satisfaction Problem: Representation and Reasoning . . 16<br/>2.3 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 23<br/><strong>3 Constraint Propagation 29</strong><br/>Christian Bessiere<br/>3.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30<br/>3.2 FormalViewpoint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33<br/>3.3 ArcConsistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37<br/>3.4 HigherOrderConsistencies . . . . . . . . . . . . . . . . . . . . . . . . 50<br/>3.5 Domain-Based Consistencies Stronger than AC . . . . . . . . . . . . . 57<br/>3.6 Domain-Based Consistencies Weaker than AC . . . . . . . . . . . . . . 62<br/>3.7 Constraint Propagation as Iteration of Reduction Rules . . . . . . . . . . 68<br/>3.8 SpecificConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70<br/><strong>4 Backtracking Search Algorithms 85</strong><br/>Peter van Beek<br/>4.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86<br/>4.2 BranchingStrategies . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87<br/>4.3 ConstraintPropagation . . . . . . . . . . . . . . . . . . . . . . . . . . 90<br/>4.4 Nogood Recording . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96<br/>4.5 Non-Chronological Backtracking . . . . . . . . . . . . . . . . . . . . . 102<br/>4.6 Heuristics for Backtracking Algorithms . . . . . . . . . . . . . . . . . . 105<br/>4.7 RandomizationandRestartStrategies . . . . . . . . . . . . . . . . . . . 111<br/>4.8 Best-FirstSearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 116<br/>4.9 Optimization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117<br/>4.10 ComparingBacktrackingAlgorithms . . . . . . . . . . . . . . . . . . . 118<br/><strong>5 Local Search Methods 135</strong><br/>Holger H. Hoos and Edward Tsang<br/>5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136<br/>5.2 RandomisedIterative ImprovementAlgorithms . . . . . . . . . . . . . 142<br/>5.3 TabuSearchandRelatedAlgorithms . . . . . . . . . . . . . . . . . . . 144<br/>5.4 Penalty-Based Local Search Algorithms . . . . . . . . . . . . . . . . . 148<br/>5.5 OtherApproaches . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 154<br/>5.6 Local Search for Constraint Optimisation Problems . . . . . . . . . . . 155<br/>5.7 Frameworks andToolkits forLocalSearch . . . . . . . . . . . . . . . . 157<br/>5.8 Conclusions andOutlook . . . . . . . . . . . . . . . . . . . . . . . . . 158<br/><strong>6 Global Constraints 169</strong><br/>Willem-Jan van Hoeve and Irit Katriel<br/>6.1 Notation and Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . 170<br/>6.2 ExamplesofGlobalConstraints . . . . . . . . . . . . . . . . . . . . . . 176<br/>6.3 Complete Filtering Algorithms . . . . . . . . . . . . . . . . . . . . . . 182<br/>6.4 Optimization Constraints . . . . . . . . . . . . . . . . . . . . . . . . . 189<br/>6.5 Partial Filtering Algorithms . . . . . . . . . . . . . . . . . . . . . . . . 193<br/>6.6 GlobalVariables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 200<br/>6.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 203<br/><strong>7 Tractable Structures for Constraint Satisfaction Problems 209</strong><br/>Rina Dechter<br/>7.1 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 210<br/>7.2 Structure-Based Tractability in Inference . . . . . . . . . . . . . . . . . 213<br/>7.3 Trading Time and Space by Hybrids of Search and Inference . . . . . . 231<br/>7.4 Structure-Based Tractability in Search . . . . . . . . . . . . . . . . . . 239<br/>7.5 Summary andBibliographicalNotes . . . . . . . . . . . . . . . . . . . 241<br/><strong>8 The Complexity of Constraint Languages 245</strong><br/>David Cohen and Peter Jeavons<br/>8.1 BasicDefinitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 246<br/>8.2 Examples of Constraint Languages . . . . . . . . . . . . . . . . . . . . 247<br/>8.3 Developing an Algebraic Theory . . . . . . . . . . . . . . . . . . . . . 251<br/>8.4 Applicationsof theAlgebraicTheory . . . . . . . . . . . . . . . . . . . 258<br/>8.5 Constraint Languages Over an Infinite Set . . . . . . . . . . . . . . . . 263<br/>8.6 Multi-Sorted Constraint Languages . . . . . . . . . . . . . . . . . . . . 264<br/>8.7 AlternativeApproaches . . . . . . . . . . . . . . . . . . . . . . . . . . 269<br/>8.8 FutureDirections . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 274<br/><strong>9 Soft Constraints 281</strong><br/>Pedro Meseguer, Francesca Rossi, Thomas Schiex<br/>9.1 Background: Classical Constraints . . . . . . . . . . . . . . . . . . . . 282<br/>9.2 SpecificFrameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 283<br/>9.3 GenericFrameworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 287<br/>9.4 Relations among Soft Constraint Frameworks . . . . . . . . . . . . . . 291<br/>9.5 Search . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 297<br/>9.6 Inference . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 300<br/>9.7 CombiningSearchandInference . . . . . . . . . . . . . . . . . . . . . 313<br/>9.8 UsingSoftConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . 316<br/>9.9 Promising Directions for Further Research . . . . . . . . . . . . . . . . 321<br/><strong>10 Symmetry in Constraint Programming 329</strong><br/>Ian P. Gent, Karen E. Petrie, Jean-Franc¸ois Puget<br/>10.1 Symmetries and Group Theory . . . . . . . . . . . . . . . . . . . . . . 331<br/>10.2 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 337<br/>10.3 Reformulation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 340<br/>10.4 Adding Constraints Before Search . . . . . . . . . . . . . . . . . . . . 343<br/>10.5 Dynamic Symmetry Breaking Methods . . . . . . . . . . . . . . . . . . 350<br/>10.6 Combinations of Symmetry Breaking Methods . . . . . . . . . . . . . . 362<br/>10.7 Successful Applications . . . . . . . . . . . . . . . . . . . . . . . . . . 363<br/>10.8 Symmetry Expression and Detection . . . . . . . . . . . . . . . . . . . 364<br/>10.9 Further Research Themes . . . . . . . . . . . . . . . . . . . . . . . . . 366<br/>10.10 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 368<br/><strong>11 Modelling 377</strong><br/>Barbara M. Smith<br/>11.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 378<br/>11.2 Representing a Problem . . . . . . . . . . . . . . . . . . . . . . . . . . 379<br/>11.3 Propagation andSearch . . . . . . . . . . . . . . . . . . . . . . . . . . 379<br/>11.4 Viewpoints . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 381<br/>11.5 Expressing the Constraints . . . . . . . . . . . . . . . . . . . . . . . . 382<br/>11.6 Auxiliary Variables . . . . . . . . . . . . . . . . . . . . . . . . . . . . 386<br/>11.7 ImpliedConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . . . 387<br/>11.8 Reformulations ofCSPs . . . . . . . . . . . . . . . . . . . . . . . . . . 391<br/>11.9 CombiningViewpoints . . . . . . . . . . . . . . . . . . . . . . . . . . 394<br/>11.10 Symmetry and Modelling . . . . . . . . . . . . . . . . . . . . . . . . . 398<br/>11.11 Optimization Problems . . . . . . . . . . . . . . . . . . . . . . . . . . 400<br/>11.12 Supporting Modelling and Reformulation . . . . . . . . . . . . . . . . . 401<br/><strong><font color="#f70909">II Extensions, Languages, and Applications 407</font><br/>12 Constraint Logic Programming 409</strong><br/>Kim Marriott, Peter J. Stuckey, MarkWallace<br/>12.1 History ofCLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 411<br/>12.2 Semantics of Constraint Logic Programs . . . . . . . . . . . . . . . . . 413<br/>12.3 CLPforConceptualModeling . . . . . . . . . . . . . . . . . . . . . . . 425<br/>12.4 CLPforDesignModeling . . . . . . . . . . . . . . . . . . . . . . . . . 430<br/>12.5 SearchinCLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 437<br/>12.6 Impact ofCLP . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 442<br/>12.7 Future of CLP and Interesting Research Questions . . . . . . . . . . . . 444<br/><strong>13 Constraints in Procedural and Concurrent Languages 453</strong><br/>Thom Fr¨uhwirth, Laurent Michel, and Christian Schulte<br/>13.1 Procedural and Object-Oriented Languages . . . . . . . . . . . . . . . . 454<br/>13.2 Concurrent Constraint Programming . . . . . . . . . . . . . . . . . . . 465<br/>13.3 Rule-Based Languages . . . . . . . . . . . . . . . . . . . . . . . . . . 473<br/>13.4 Challenges and Opportunities . . . . . . . . . . . . . . . . . . . . . . . 485<br/>13.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 486<br/><strong>14 Finite Domain Constraint Programming Systems 495</strong><br/>Christian Schulte and Mats Carlsson<br/>14.1 Architecture for Constraint Programming Systems . . . . . . . . . . . . 496<br/>14.2 Implementing Constraint Propagation . . . . . . . . . . . . . . . . . . . 506<br/>14.3 ImplementingSearch . . . . . . . . . . . . . . . . . . . . . . . . . . . 513<br/>14.4 SystemsOverview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 517<br/>14.5 Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 519<br/><strong>15 Operations Research Methods in Constraint Programming 527</strong><br/>John N. Hooker<br/>15.1 Schemes for IncorporatingORintoCP . . . . . . . . . . . . . . . . . . 527<br/>15.2 Plan of theChapter . . . . . . . . . . . . . . . . . . . . . . . . . . . . 528<br/>15.3 Linear Programming . . . . . . . . . . . . . . . . . . . . . . . . . . . . 530<br/>15.4 MixedInteger/LinearModeling . . . . . . . . . . . . . . . . . . . . . . 534<br/>15.5 Cutting Planes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 536<br/>15.6 Relaxation of Global Constraints . . . . . . . . . . . . . . . . . . . . . 539<br/>15.7 Relaxation of Piecewise Linear and Disjunctive Constraints . . . . . . . 545<br/>15.8 LagrangeanRelaxation . . . . . . . . . . . . . . . . . . . . . . . . . . 547<br/>15.9 Dynamic Programming . . . . . . . . . . . . . . . . . . . . . . . . . . 550<br/>15.10 Branch-and-Price Methods . . . . . . . . . . . . . . . . . . . . . . . . 554<br/>15.11 Benders Decomposition . . . . . . . . . . . . . . . . . . . . . . . . . . 556<br/>15.12 Toward IntegrationofCPandOR . . . . . . . . . . . . . . . . . . . . . 560<br/><strong>16 Continuous and Interval Constraints 571</strong><br/>Fr´ed´eric Benhamou and Laurent Granvilliers<br/>16.1 From Discrete to Continuous Constraints . . . . . . . . . . . . . . . . . 574<br/>16.2 TheBranch-and-ReduceFramework . . . . . . . . . . . . . . . . . . . 575<br/>16.3 ConsistencyTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . 577<br/>16.4 NumericalOperators . . . . . . . . . . . . . . . . . . . . . . . . . . . . 583<br/>16.5 HybridTechniques . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 587<br/>16.6 FirstOrderConstraints . . . . . . . . . . . . . . . . . . . . . . . . . . 590<br/>16.7 Applications andSoftware packages . . . . . . . . . . . . . . . . . . . 593<br/>16.8 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 595<br/><strong>17 Constraints over Structured Domains 605</strong><br/>Carmen Gervet<br/>17.1 History andApplications . . . . . . . . . . . . . . . . . . . . . . . . . 606<br/>17.2 Constraints over Regular and Constructed Sets . . . . . . . . . . . . . . 609<br/>17.3 Constraints over Finite Set Intervals . . . . . . . . . . . . . . . . . . . . 613<br/>17.4 Influential Extensions to Subset Bound Solvers . . . . . . . . . . . . . . 619<br/>17.5 Constraints over Maps, Relations and Graphs . . . . . . . . . . . . . . . 628<br/>17.6 Constraints over Lattices and Hierarchical Trees . . . . . . . . . . . . . 631<br/>17.7 ImplementationAspects . . . . . . . . . . . . . . . . . . . . . . . . . . 631<br/>17.8 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633<br/>17.9 FurtherTopics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 633<br/><strong>18 Randomness and Structure 639</strong><br/>Carla Gomes and TobyWalsh<br/>18.1 Random Constraint Satisfaction . . . . . . . . . . . . . . . . . . . . . . 640<br/>18.2 Random Satisfiability . . . . . . . . . . . . . . . . . . . . . . . . . . . 644<br/>18.3 Random Problems with Structure . . . . . . . . . . . . . . . . . . . . . 648<br/>18.4 Runtime Variability . . . . . . . . . . . . . . . . . . . . . . . . . . . . 651<br/>18.5 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 657<br/>18.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 658<br/><strong>19 Temporal CSPs 665</strong><br/>Manolis Koubarakis<br/>19.1 Preliminaries . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 666<br/>19.2 Constraint-Based Formalisms for Reasoning About Time . . . . . . . . 669<br/>19.3 EfficientAlgorithms forTemporalCSPs . . . . . . . . . . . . . . . . . 677<br/>19.4 More Expressive Queries for Temporal CSPs . . . . . . . . . . . . . . . 681<br/>19.5 First-Order Temporal Constraint Languages . . . . . . . . . . . . . . . 683<br/>19.6 The Scheme of Indefinite Constraint Databases . . . . . . . . . . . . . . 685<br/>19.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 691<br/><strong>20 Distributed Constraint Programming 699</strong><br/>Boi Faltings<br/>20.1 Definitions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 701<br/>20.2 DistributedSearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 702<br/>20.3 Improvements andVariants . . . . . . . . . . . . . . . . . . . . . . . . 713<br/>20.4 DistributedLocalSearch . . . . . . . . . . . . . . . . . . . . . . . . . 718<br/>20.5 Open Constraint Programming . . . . . . . . . . . . . . . . . . . . . . 721<br/>20.6 Further Issues . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 724<br/>20.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 726<br/><strong>21 Uncertainty and Change 731</strong><br/>Kenneth N. Brown and Ian Miguel<br/>21.1 Background and Definitions . . . . . . . . . . . . . . . . . . . . . . . . 732<br/>21.2 Example: Course Scheduling . . . . . . . . . . . . . . . . . . . . . . . 732<br/>21.3 UncertainProblems . . . . . . . . . . . . . . . . . . . . . . . . . . . . 733<br/>21.4 Problems thatChange . . . . . . . . . . . . . . . . . . . . . . . . . . . 738<br/>21.5 Pseudo-dynamic Formalisms . . . . . . . . . . . . . . . . . . . . . . . 752<br/>21.6 Challenges andFutureTrends . . . . . . . . . . . . . . . . . . . . . . . 753<br/>21.7 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 755<br/><strong>22 Constraint-Based Scheduling and Planning 761</strong><br/>Philippe Baptiste, Philippe Laborie, Claude Le Pape, Wim Nuijten<br/>22.1 Constraint Programming Models for Scheduling . . . . . . . . . . . . . 763<br/>22.2 Constraint Programming Models for Planning . . . . . . . . . . . . . . 771<br/>22.3 Constraint Propagation for Resource Constraints . . . . . . . . . . . . . 778<br/>22.4 Constraint Propagation on Optimization Criteria . . . . . . . . . . . . . 785<br/>22.5 HeuristicSearch . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 789<br/>22.6 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 794<br/><strong>23 Vehicle Routing 801</strong><br/>Philip Kilby and Paul Shaw<br/>23.1 TheVehicleRoutingProblem . . . . . . . . . . . . . . . . . . . . . . . 802<br/>23.2 Operations Research Approaches . . . . . . . . . . . . . . . . . . . . . 804<br/>23.3 Constraint Programming Approaches . . . . . . . . . . . . . . . . . . . 809<br/>23.4 Constraint Programming in Search . . . . . . . . . . . . . . . . . . . . 819<br/>23.5 Using Constraint Programming as a Subproblem Solver . . . . . . . . . 823<br/>23.6 CP-VRPintheRealWorld . . . . . . . . . . . . . . . . . . . . . . . . 825<br/>23.7 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 828<br/><strong>24 Configuration 837</strong><br/>Ulrich Junker<br/>24.1 What Is Configuration? . . . . . . . . . . . . . . . . . . . . . . . . . . 838<br/>24.2 ConfigurationKnowledge . . . . . . . . . . . . . . . . . . . . . . . . . 844<br/>24.3 Constraint Models for Configuration . . . . . . . . . . . . . . . . . . . 853<br/>24.4 ProblemSolving forConfiguration . . . . . . . . . . . . . . . . . . . . 863<br/>24.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 868<br/>25 Constraint Applications in Networks 875<br/>Helmut Simonis<br/><strong>25.1 ElectricityNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . 876</strong><br/>25.2 Water (Oil)Networks . . . . . . . . . . . . . . . . . . . . . . . . . . . 878<br/>25.3 DataNetworks . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 879<br/>25.4 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 898<br/><strong>26 Bioinformatics and Constraints 905<br/></strong>Rolf Backofen and David Gilbert<br/>26.1 WhatBiologistsWant fromBioinformatics . . . . . . . . . . . . . . . . 906<br/>26.2 TheCentralDogma . . . . . . . . . . . . . . . . . . . . . . . . . . . . 907<br/>26.3 A Classification of Problem Areas . . . . . . . . . . . . . . . . . . . . 908<br/>26.4 SequenceRelatedProblems . . . . . . . . . . . . . . . . . . . . . . . . 908<br/>26.5 StructureRelatedProblems . . . . . . . . . . . . . . . . . . . . . . . . 922<br/>26.6 FunctionRelatedProblems . . . . . . . . . . . . . . . . . . . . . . . . 935<br/>26.7 Microarrays . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 937<br/><strong>Index 945</strong></font></font></li><li><font color="#f70909"><font color="#000000"> </font></font></li></font>