Derivative-Free and Blackbox Optimization
This book is designed as a textbook, suitable for self-learning or for teaching an upper-year university course on derivative-free and blackbox optimization. The book is split into 5 parts and is designed to be modular; any individual part depends only on the material in Part I. Part I of the book d...
| Main Authors: | , |
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| Corporate Author: | |
| Language: | English |
| Published: |
Cham :
Springer International Publishing : Imprint: Springer,
2017.
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| Edition: | 1st ed. 2017. |
| Series: | Springer Series in Operations Research and Financial Engineering,
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| Subjects: | |
| Online Access: | https://doi.org/10.1007/978-3-319-68913-5 |
Table of Contents:
- Part I: Introduction and Background Material
- Introduction: Tools and Challenges
- Mathematical Background
- The Beginnings of DFO Algorithms
- Part I: Some Remarks on DFO
- Part II: Popular Heuristic Methods
- Genetic Algorithms
- Nelder-Mead
- Part II: Further Remarks on Heuristics
- Part III: Direct Search Methods
- Positive bases and Nonsmooth Optimization
- Generalized Pattern Search
- Mesh Adaptive Direct Search
- Part III: Further Remarks on Direct Search Methods
- Part IV: Model-based Methods
- Model-based Descent
- Model-based Trust Region
- Part IV: Further Remarks on Model-based Methods
- Part V: Extensions and Refinements
- Variables and Constraints
- Optimization Using Surrogates and Models
- Biobjective Optimization
- Part V: Final Remarks on DFO/BBO
- Part VI: Appendix: Comparing Optimization Methods
- Solutions to Selected Exercises.