Flexible usage suitable for undergraduate, graduate, mathematics, computer science, engineering, or mixed classes

15 end-of-chapter projects are provided, allowing advanced exploration of desired topics

Includes numerous exercises throughout to test knowledge and advance understanding

Autorentext
Dr. Charles Audet is a Professor of Mathematics at the École Polytechnique de Montréal. His research interests include the analysis and development of algorithms for blackbox nonsmooth optimization, and structured global optimization. He obtained a Ph.D. degree in applied mathematics from the École Polytechnique de Montréal, and worked as a post-doc at Rice University in Houston, Texas.

Dr. Warren Hare received his Ph.D. in Mathematical Optimization from Simon Fraser University. He complete postdoctoral research at IMPA (Brazil) and McMaster (Canada), before joining the University of British Columbia (Canada).

Inhalt
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.