Derivative-Free and Blackbox Optimization

Beschreibung

The second edition of Derivative-Free and Blackbox Optimization offers a comprehensive introduction to the field of optimization when derivatives are unavailable, unreliable, or impractical. Whether you’re a student, instructor, or self-learner, this book is designed to guide you through both the foundations and advanced techniques of derivative-free and blackbox optimization. This new edition features significantly expanded exercises, updated and intuitive notation, over 30 new figures, and a wide range of pedagogical enhancements aimed at making complex concepts accessible and engaging. The book is structured into five parts. Part 1 established foundational principles, including an expanded chapter on proper benchmarking. Parts 2, 3, and 4, take an in-depth look at heuristics, direct search, and model based approaches (respectively). Part 5 extends these approaches to specialised settings. Finally, a new appendix contributed by Sébastien Le Digabel, details several real-world applications of blackbox optimization, and links to software for each application. Whether used in the classroom or for independent exploration, this book is a powerful resource for understanding and applying optimization methods – no gradients required. 

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

Autor*in

Charles Audet

Themen in »Derivative-Free and Blackbox Optimization«

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

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