product iamge

Derivative-free and blackbox optimization

This document was uploaded by one of our users. The uploader already confirmed that they had the permission to publish it. If you are author/publisher or own the copyright of this documents, please report to us by using this DMCA report form.

Download
Search on Amazon

Simply click on the Download Book button.

Yes, Book downloads on Ebookily are 100% Free.

Sometimes the book is free on Amazon As well, so go ahead and hit "Search on Amazon"

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 discusses what is meant by Derivative-Free and Blackbox Optimization, provides background material, and early basics  Read more...

Abstract:
Part III presents direct search methods (Generalized Pattern Search and Mesh Adaptive Direct Search) and Part IV focuses on model-based methods (Simplex Gradient and Trust Region).  Read more...

Author(s): Audet, Charles; Hare, Warren
Series: Springer Series in Operations Research and Financial Engineering
Publisher: Springer International Publishing : Imprint : Springer
Year: 2017

Language: English
Pages: 302
Tags: Mathematics.;Numerical analysis.;Mathematical optimization.;Optimization.;Numerical Analysis.

Content: 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.