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An Introduction to Optimization: With Applications to Machine Learning (5th Edition)
by Edwin K. P. Chong, Wu-Sheng Lu, Stanislaw H. Zak

The 5th edition of An Introduction to Optimization: With Applications to Machine Learning is a comprehensive and updated resource for those looking to understand optimization theory and its critical applications in modern machine learning. This textbook provides an accessible approach to optimization, presenting both theoretical foundations and practical applications. The book’s inclusion of relevant machine learning contexts makes it an ideal choice for students and professionals in engineering, computer science, and applied mathematics.

Key Features:

1. Updated Content:
   The 5th edition has been updated with new examples, exercises, and applications that reflect the latest developments in machine learning and optimization techniques.

2. Comprehensive Introduction to Optimization:
   The book introduces key optimization methods such as convex optimization, gradient descent, linear and nonlinear programming, and more. Each topic is discussed with practical examples and algorithmic solutions.

3. Applications to Machine Learning:
   A significant portion of the book is dedicated to demonstrating how optimization methods are applied to machine learning problems, including model training, deep learning, and support vector machines.

4. Practical Problem-Solving:
   The book includes numerous worked-out examples and exercises, allowing readers to apply optimization techniques to real-world problems. MATLAB code is provided for some examples to encourage practical engagement.

5. Theoretical and Computational Aspects:
   The authors cover both the theoretical background and the computational complexity of various optimization techniques, making the book suitable for those with a strong mathematical foundation as well as those looking to apply these methods practically.

Book Structure:

The book is divided into chapters that gradually build up the reader's understanding of optimization, starting from the basics and advancing to more complex methods used in machine learning:

• Introduction to Optimization

• Linear and Nonlinear Programming

• Convex Optimization

• Gradient Methods

• Optimization in Machine Learning Applications

• Support Vector Machines and Deep Learning

• Numerical Methods and Algorithms for Optimization

Recommended For:

• Students in computer science, engineering, mathematics, or applied sciences who are studying optimization and machine learning

• Professionals and researchers in machine learning, data science, and AI who require a solid grounding in optimization techniques

• Anyone working with optimization algorithms in industries like data science, machine learning, finance, and engineering

Authors:

• Edwin K. P. Chong: Known for his expertise in optimization theory and applications, especially in machine learning.

• Wu-Sheng Lu: An expert in both theoretical and computational optimization methods.

• Stanislaw H. Zak: Known for his contributions to the field of optimization and applied mathematics.