Introduction to Nonlinear Optimization: Theory, Algorithms, and Applications with Python and MATLAB, Second Edition Amir Beck

Second Edition Files

• bisection.m
• chebyshev_center.m
• cls.m
• examples and algorithms.ipynb
• gradient_method_backtracking.m
• gradient_method_constant.m
• gradient_method_quadratic.m
• gradient_scaled_quadratic.m
• newton_backtracking.m
• newton_hybrid.m
• proj_polytope.m
• proj_unit_simplex.m
• pure_newton.m
• trs.m

First Edition Files

Download the M-files associated with the book.
Additional Exercises
Errata

Lecture slides based on the book (these link will redirect you to GoogleDrive):

1. Mathematical Preliminaries (without layers/with layers)
2. Unconstrained Optimization (without layers/with layers)
3. Least Squares (without layers/with layers)
4. The Gradient Method (without layers/with layers)
5. Newton's Method (without layers/with layers)
6. Convex Sets (without layers/with layers)
7. Convex Functions (without layers/with layers)
8. Convex Optimization (without layers/with layers)
9. Optimization over a Convex Set (without layers/with layers)
10. Linearly Constrained Problems (without layers/with layers)
11. The Karush-Kuhn-Tucker Conditions (without layers/with layers)
12. Duality (without layers/with layers)

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