Introduction to Nonlinear Optimization
This Module is offered by TUM Department of Mathematics.
This module handbook serves to describe contents, learning outcome, methods and examination type as well as linking to current dates for courses and module examination in the respective
Module version of SS 2012 (current)
There are historic module descriptions of this module. A module description is valid until replaced by a newer one.
MA2503 is a semester module
in German language
at Bachelor’s level
which is offered in winter semester.
|Total workload||Contact hours||Credits (ECTS)|
Modelling of practical problems as optimization problems, unconstrained optimization (optimality conditions, globally convergent descent methods, Newton method and Newton-type methods, globalization of locally convergent methods), elements of constrained optimization.
After successful completion of the module students are able to formulate practical problems as optimization problems as well as to understand and to apply the theoretical basics of nonlinear optimization.
Further, they understand modern optimization methods and elements of their convergence theory, and they are familiar with and can apply fundamental concepts in the theoretical analysis of nonlinear optimization problems.
MA1001 Analysis 1, MA1002 Analysis 2, MA1101 Lineare Algebra 1, MA1102 Lineare Algebra 2
Courses and Schedule
Learning and Teaching Methods
The module is offered as lectures with accompanying practice sessions. In the lectures, the contents will be presented in a talk with demonstrative examples, as well as through discussion with the students. The lectures should motivate the students to carry out their own analysis of the themes presented and to independently study the relevant literature. Corresponding to each lecture, practice sessions will be offered, in which exercise sheets and solutions will be available. In this way, students can deepen their understanding of the methods and concepts taught in the lectures and independently check their progress. At the beginning of the module, the practice sessions will be offered under guidance, but during the term the sessions will become more independent, and intensify learning individually as well as in small groups.
Geiger, Kanzow: Numerische Verfahren zur Lösung unrestringierter Optimierungsaufgaben, Springer, 1999.
Geiger, Kanzow: Theorie und Numerik restringierter Optimierungsaufgaben, Springer, 2002.
Nocedal, Wright: Numerical Optimization, Springer, 2006.
Description of exams and course work
The module examination is based on a written exam (60 minutes). Students have to know the theoretical foundations of nonlinear optimization and modern optimization methods including their convergence theory. They can model practical tasks as optimization problems and give solution approaches.
The exam may be repeated at the end of the semester.