Introduction

Marcel Angenvoort

2025-07-01

Why learn Optimization?

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nonlinear optimization
source: arXiv:1805.04829

About this Course

Target audience:

Prerequisites:

• Linear Algebra
• Calculus
• Programming (Python/Julia)

Syllabus

• Week 1 – 5: Intro to Julia
• Week 6 – 10: Algorithms for dense matrices

Note

The exact structure of this course is subject to change and may vary.

Recommended Textbooks

(Boyd and Vandenberghe 2004)

 

(Nocedal and Wright 2006)

 

(Rüdiger Reinhardt and Hoffmann 2013)

 

(Kochenderfer and Wheeler 2019)

Online Courses

• Stanford EE364A Convex Optimization by Stephen Boyd, 2023
• Optimization Method for Machine Learning by Julius Pfrommer, KIT 2020/21

References

Boyd, Stephen, and Lieven Vandenberghe. 2004. Convex Optimization. Cambridge University Press. https://web.stanford.edu/~boyd/cvxbook/.

Kochenderfer, Mykel J., and Tim A. Wheeler. 2019. Algorithms for Optimization. Cambridge, MA: The MIT Press. https://algorithmsbook.com/optimization/.

Nocedal, Jorge, and Stephen J. Wright. 2006. Numerical Optimization. 2nd ed. Springer.

Rüdiger Reinhardt, Armin, and Tobias Gerlach Hoffmann. 2013. Nichtlineare Optimierung: Theorie, Numerik Und Experimente. Springer Spektrum.