Books

Introduction to Nonlinear Optimization - Theory, Algorithms and Applications 

Amir Beck

MOS-SIAM Series on Optimization. SIAM. 2014

Book's website 

• Contents 

• Sample Chapter 2 

• Sample Chapter 8 

• m-files

• Errata

• Additional Exercises

Lecture notes based on the book

• 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)

Second Edition - Introduction to Nonlinear Optimization - Theory, Algorithms and Applications with Python and Matlab

Amir Beck

MOS-SIAM Series on Optimization. SIAM. 2023

Book's website 

• Additional Material

First-Order Methods in Optimization

MOS-SIAM Series on Optimization. SIAM. 2017

Book's website 

• Contents

• Sample Chapter 6

• Sample Chapter 10

Tables

• strong subdifferential results

• conjugate functions and calculus rules

• orthogonal projections

• prox calculus rules

• prox computations

• smooth functions/strongly convex functions