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Könyv Optimization and Learning via Stochastic Gradient Search Felisa Vazquez-Abad

Optimization and Learning via Stochastic Gradient Search

Szerző: Felisa Vazquez-Abad
Nyelv: Angol
Kötés: Kemény kötésű
Kiadó: Princeton University Press
Elérhetőség: Beszállítói készleten
Küldés 9-15 napon belül
34 305 Ft
An introduction to gradient-based stochastic optimization that integrates theory and implementationT...

Információk a könyvről

Szerző
Felisa Vazquez-Abad
Nyelv
Angol
Kötés
Könyv - Kemény kötésű
Kiadva
2025
oldal
448
EAN
9780691245867
ISBN
069124586X
Enbook ID
47215199
Kiadó
Princeton University Press
Súly
965
Méretek
178 x 254

Teljes leírás

An introduction to gradient-based stochastic optimization that integrates theory and implementation

This book explains gradient-based stochastic optimization, exploiting the methodologies of stochastic approximation and gradient estimation. Although the approach is theoretical, the book emphasizes developing algorithms that implement the methods. The underlying philosophy of this book is that when solving real problems, mathematical theory, the art of modeling, and numerical algorithms complement each other, with no one outlook dominating the others.

The book first covers the theory of stochastic approximation including advanced models and state-of-the-art analysis methodology, treating applications that do not require the use of gradient estimation. It then presents gradient estimation, developing a modern approach that incorporates cutting-edge numerical algorithms. Finally, the book culminates in a rich set of case studies that integrate the concepts previously discussed into fully worked models. The use of stochastic approximation in statistics and machine learning is discussed, and in-depth theoretical treatments for selected gradient estimation approaches are included.

Numerous examples show how the methods are applied concretely, and end-of-chapter exercises enable readers to consolidate their knowledge. Many chapters end with a section on “Practical Considerations” that addresses typical tradeoffs encountered in implementation. The book provides the first unified treatment of the topic, written for a wide audience that includes researchers and graduate students in applied mathematics, engineering, computer science, physics, and economics.

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