Optimization Software Class Libraries

By: Stefan Voss, Stefan Vos, David L. Woodruff

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Hardcover | 30 April 2002

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Optimization problems in practice are diverse and evolve over time, giving rise to - quirements both for ready-to-use optimization software packages and for optimization software libraries, which provide more or less adaptable building blocks for app- cation-specific software systems. In order to apply optimization methods to a new type of problem, corresponding models and algorithms have to be "coded" so that they are accessible to a computer. One way to achieve this step is the use of a mod- ing language. Such modeling systems provide an excellent interface between models and solvers, but only for a limited range of model types (in some cases, for example, linear) due, in part, to limitations imposed by the solvers. Furthermore, while m- eling systems especially for heuristic search are an active research topic, it is still an open question as to whether such an approach may be generally successful. Modeling languages treat the solvers as a "black box" with numerous controls. Due to variations, for example, with respect to the pursued objective or specific problem properties, - dressing real-world problems often requires special purpose methods. Thus, we are faced with the difficulty of efficiently adapting and applying appropriate methods to these problems. Optimization software libraries are intended to make it relatively easy and cost effective to incorporate advanced planning methods in application-specific software systems. A general classification provides a distinction between callable packages, nume- cal libraries, and component libraries.

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Non-Fiction Mathematics Optimisation Reference, Information & Interdisciplinary Subjects Encyclopaedias & Reference Works Computing & I.T.Computer Science Medicine Medicine in General Applied Mathematics Computer Programming & Software Development Software Engineering

Engineering & Technology Mechanical Engineering & Materials Production & Industrial Engineering

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Published: 28th March 2013

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Preface	p. ix
Optimization Software Class Libraries	p. 1
Introduction	p. 2
Component Libraries	p. 3
Callable Packages and Numerical Libraries	p. 20
Conclusions and Outlook	p. 23
Distribution, Cooperation, and Hybridization for Combinatorial Optimization	p. 25
Introduction	p. 25
Overview of the Templar Framework	p. 26
Distribution	p. 36
Cooperation	p. 43
Hybridization	p. 49
Cost of Supporting a Framework	p. 51
Summary	p. 57
A Framework for Local Search Heuristics for Combinatorial Optimization Problems	p. 59
Introduction	p. 60
Design Patterns	p. 61
The Searcher Framework	p. 65
Using the Design Patterns	p. 69
Implementation Issues	p. 74
Related Work	p. 77
Conclusions and Extensions	p. 78
HotFrame: A Heuristic Optimization Framework	p. 81
Introduction	p. 81
A Brief Overview	p. 83
Analysis	p. 85
Design	p. 103
Implementation	p. 137
Application	p. 146
Conclusions	p. 153
Writing Local Search Algorithms Using EasyLocal++	p. 155
Introduction	p. 155
An Overview of EasyLocal++	p. 156
The Course Timetabling Problem	p. 161
Solving Course Timetabling Using EasyLocal++	p. 162
Debugging and Running the Solver	p. 172
Discussion and Conclusions	p. 174
Integrating Heuristic Search and One-Way Constraints in the iOpt Toolkit	p. 177
Introduction	p. 177
One-Way Constraints	p. 178
Constraint Satisfaction Algorithms for One-Way Constraints	p. 179
The Invariant Library of iOpt	p. 180
The Heuristic Search Framework of iOpt	p. 182
Experimentation on the Graph Coloring and the Vehicle Routing Problem	p. 186
Related Work and Discussion	p. 190
Conclusions	p. 190
The OptQuest Callable Library	p. 193
Introduction	p. 193
Scatter Search	p. 196
The OCL Optimizer	p. 198
OCL Functionality	p. 202
OCL Application	p. 211
Conclusions	p. 215
A Constraint Programming Toolkit for Local Search	p. 219
Introduction	p. 219
Constraint Programming Preliminaries	p. 221
The Local Search Toolkit	p. 225
Industrial Example: Facility Location	p. 239
Extending the Toolkit	p. 249
Specializing the Toolkit: ILOG Dispatcher	p. 250
Related Work	p. 259
Conclusion	p. 260
The Modeling Language OPL--A Short Overview	p. 263
Introduction	p. 263
Frequency Allocation	p. 265
Sport Scheduling	p. 269
Job-Shop Scheduling	p. 276
The Trolley Application	p. 279
Research Directions	p. 290
Conclusion	p. 294
Genetic Algorithm Optimization Software Class Libraries	p. 295
Introduction	p. 296
C++ Class Library Software	p. 304
Java Class Library Software	p. 317
Genetic Algorithm Optimization Software Survey	p. 319
Conclusions	p. 328
Abbreviations	p. 331
References	p. 335
Index	p. 357
Table of Contents provided by Syndetics. All Rights Reserved.

Optimization Software Class Libraries

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Hardcover

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