© 2017

Derivative-Free and Blackbox Optimization

  • 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


Table of contents

  1. Front Matter
    Pages i-xviii
  2. Introduction and Background Material

    1. Front Matter
      Pages 1-1
    2. Charles Audet, Warren Hare
      Pages 15-31
    3. Charles Audet, Warren Hare
      Pages 33-54
  3. Popular Heuristic Methods

    1. Front Matter
      Pages 55-55
    2. Charles Audet, Warren Hare
      Pages 57-73
    3. Charles Audet, Warren Hare
      Pages 75-91
  4. Direct Search Methods

    1. Front Matter
      Pages 93-93
    2. Charles Audet, Warren Hare
      Pages 95-114
    3. Charles Audet, Warren Hare
      Pages 115-134
    4. Charles Audet, Warren Hare
      Pages 135-156
  5. Model-Based Methods

    1. Front Matter
      Pages 157-157
    2. Charles Audet, Warren Hare
      Pages 159-181
    3. Charles Audet, Warren Hare
      Pages 183-200
    4. Charles Audet, Warren Hare
      Pages 201-218
  6. Extensions and Refinements

    1. Front Matter
      Pages 219-219
    2. Charles Audet, Warren Hare
      Pages 221-234
    3. Charles Audet, Warren Hare
      Pages 235-246
    4. Charles Audet, Warren Hare
      Pages 247-262

About this book


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 while Part II focuses on heuristic methods (Genetic Algorithms and Nelder-Mead).  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).  Part V discusses dealing with constraints, using surrogates, and bi-objective optimization.

End of chapter exercises are included throughout as well as 15 end of chapter projects and over 40 figures.  Benchmarking techniques are also presented in the appendix.


Derivative-Free Optimization Blackbox Optimization Heuristic Methods Direct Search Methods Mesh Adaptive Direct Search Model-based Methods Model-based Trust-region Nonsmooth Constraints Surrogate Models Optimization Benchmarking

Authors and affiliations

  1. 1.Dépt. Mathématiques et Génie IndustrielEcole Polytechnique de MontréalMontréalCanada
  2. 2.Department of MathematicsUniversity of British ColumbiaKelownaCanada

About the authors

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).  

Bibliographic information

  • Book Title Derivative-Free and Blackbox Optimization
  • Authors Charles Audet
    Warren Hare
  • Series Title Springer Series in Operations Research and Financial Engineering
  • Series Abbreviated Title Operations Research,Financ.Engin. ( Operations R.)
  • DOI
  • Copyright Information Springer International Publishing AG 2017
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics Mathematics and Statistics (R0)
  • Hardcover ISBN 978-3-319-68912-8
  • Softcover ISBN 978-3-319-88680-0
  • eBook ISBN 978-3-319-68913-5
  • Series ISSN 1431-8598
  • Series E-ISSN 2197-1773
  • Edition Number 1
  • Number of Pages XVIII, 302
  • Number of Illustrations 38 b/w illustrations, 0 illustrations in colour
  • Topics Optimization
    Numerical Analysis
Industry Sectors
Finance, Business & Banking
IT & Software


“It is a wonderful textbook that can be used entirely or partially to support optimization courses. … the authors have achieved gloriously their stated goal of ‘providing a clear grasp of the foundational concepts in derivative-free and blackbox optimization.’ … I wish that it will find its way somehow to the desks of engineering design optimization practitioners.” (Michael Kokkolaras, Optimization and Engineering, Vol. 20, 2019)

“This book targets two audiences: individuals interested in understanding derivative-free optimization (DFO) and blackbox optimization and practitioners who have to solve real-world problems that cannot be approached by traditional gradient-based methods. … The book is written in a clear style with sufficient details, examples and proofs of theoretical results. The authors pay equal attention to careful theoretical development and analysis of the methods, and to practical details of the algorithms.” (Olga Brezhneva, Mathematical Reviews, October, 2018)

“The authors present a comprehensive textbook being an introduction to blackbox and derivative- free optimization. … The book is for sure a necessary position for students of mathematics, IT or engineering that would like to explore the subject of blackbox and derivative-free optimization. Also the researchers in the area of optimization could treat it as an introductory reading. Finally, the book would be also a good choice for practitionners dealing with such kind of problems.” (Marcin Anholcer, zbMATH 1391.90001, 2018)