Online Lecture on 'Nonlinear Optimization'


November 2, 2020 - February 26, 2021

Weekly lecture material: Videos and manuscript (45-60 minutes per week),
Weekly tutorials (45 minutes per week)

Content and Format

Content: Nonlinear Optimization

Unconstrained optimization problems are considered and different solution methods are presented, e.g. steepest descent, the conjugate gradient method, and Newtons method. In addition to the numerical implementation, an analysis of the methods will be carried out. Programming tasks (in MATLAB or PYTHON) are an important part of the exercises. Prerequisites for the course are basic knowledge in Analysis, Linear Algebra and an introduction to Numerical Analysis as well as MATLAB or PYTHON programming skills

This course has been held online by Jun.Prof. Gabriele Ciaramella at the University of Konstanz in the (German) summer term 2020 with lecture material in English, Italian and German. In the given time frame it will be repeated for participants in Peru and South America. Participants will have access to the lecture videos, a manuscript and corresponding exercises each week. An advanced student from the University of Konstanz will guide through the course and discuss the solution of the exercise sheets (in English).

There are two possibilities to participate. The first one is an individual participation as capacity-building program. A certificate will be issued in the end for regular participation. The second possibility is that university docents embed this lecture into their class, which means that both students and professors will have access to the lecture material and the students will be additionally guided by their docent.


More information can be found on the course website at the University of Konstanz. For queries concerning the content and format of the course, please contact Jun.Prof. Dr. Gabriele Ciaramella, for questions concerning the registration consult Dr. Stefan Frei (PeC3).


The registration is closed. Please contact Stefan Frei (PeC3) for additional information.