Introduction

Author

Marcel Angenvoort

Published

July 1, 2025

Abstract

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Keywords

math, optimization, julia, 90-01, 90C30

Why learn Optimization?

normal text, italic text, bold text, bold italics

nonlinear optimization source: arXiv:1805.04829

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.

Literature

Online Courses

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.