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An Introduction to Optimization: With Applications to Machine Learning (4th Edition)

Author: Edwin K. P. Chong
Binding: Paperback
Paper Quality: Yellow Paper
Category: Mathematics, Optimization, Machine Learning
Recommended For:
BS/MS students in Computer Science, Engineering, Mathematics, and Data Science; also ideal for machine learning enthusiasts and researchers in optimization.

Key Points:

  1. Foundations of Optimization Theory
    Covers both linear and nonlinear optimization, constrained and unconstrained problems, providing a strong mathematical base.

  2. Machine Learning Applications
    Updated to include real-world ML use cases, such as gradient descent, Lagrangian methods, and stochastic optimization, relevant to modern AI systems.

  3. Algorithmic Approach
    Focuses on practical algorithms with step-by-step derivations, including Newton’s method, simplex method, and KKT conditions.

  4. Exercises and MATLAB Examples
    Includes MATLAB code snippets and exercises to help students simulate and visualize optimization problems.

  5. Well-Structured for Learning
    Designed to suit both classroom instruction and self-paced study with clear explanations and problem sets.

                                                 ════ ★⋆ ═══

Writer                               Edwin K. P. Chong (Author),
                                             Stanislaw H. Zak (Author)

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