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Derivative-Free Optimization

By Yang Yu

Derivative-Free Optimization Computer Science

Derivative-Free Optimization

By Yang Yu

0 - Default Title
Description
This book offers a pioneering exploration of classification-based derivative-free optimization (DFO), providing researchers and professionals in artificial intelligence, machine learning, AutoML, and optimization with a robust framework for addressing complex, large-scale problems where gradients are unavailable. By bridging theoretical foundations with practical implementations, it fills critical gaps in the field, making it an indispensable resource for both academic and industrial audiences. The book introduces innovative frameworks such as sampling-and-classification (SAC) and sampling-and-learning (SAL), which underpin cutting-edge algorithms like Racos and SRacos. These methods are designed to excel in challenging optimization scenarios, including high-dimensional search spaces, noisy environments, and parallel computing. A dedicated section on the ZOOpt toolbox provides practical tools for implementing these algorithms effectively. The book’s structure moves from foundational principles and algorithmic development to advanced topics and real-world applications, such as hyperparameter tuning, neural architecture search, and algorithm selection in AutoML. Readers will benefit from a comprehensive yet concise presentation of modern DFO methods, gaining theoretical insights and practical tools to enhance their research and problem-solving capabilities. A foundational understanding of machine learning, probability theory, and algorithms is recommended for readers to fully engage with the material.
Product details
Number of Pages:
212
Release Date:
2025-07-03
Publication Date:
2025-07-03
Publisher:
Springer Nature Singapore
Languages:
Original: English
ISBN10:
9819659280
ISBN13:
9789819659289
GPSR Manufacturer Reference:
[email protected]
Weight:
487 g
Height:
160 cm
Width:
241 cm
Thickness:
18 cm
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