Optimization 2021

Lecturer: Zsófia Lendek. TAs: Matthias Rosynski. See also the Romanian-language version of the course: Optimizari.

This course concerns numerical methods for optimization. It is part of the Bachelor of Science program of the Automation Department, UTCluj (4th year 2nd semester). As prerequisites, knowledge of analysis and linear algebra is needed, together with notions of Matlab programming. The responsible lecturer is Zsófia Lendek.

Grading:

• 60% partial exam
• 40% final exam.

Below the schedule, including the partial exam.

Lecture notes

The lecture notes are available below:

• Chapter 1: Analytical methods.
• Chapter 2: Numerical optimization.
• Chapter 3: Linear programming.
• Chapter 4: Quadratic programming.
• Chapter 5: Genetic algorithms and some kangaroos.
• Appendix: Matrix differentiation.

In the practical classes, a set of assignments must be solved. You may work in groups of at most 2. An exercises is completed when the method is implemented and the corresponding problem is solved. For a (very) short introduction of the algorithms to be used and examples, see the lab guide. The Matlab files corresponding to the first lab session are available here. The first lab session is a Matlab introduction: see the Matlab tutorial and an exercise to be solved.

The exercises you have to solve are those corresponding to the labs, given in situation. If you do not have an exercise scheduled, please contact the TA. The requirements for the labs are as follows:

• 1. Function approximation
• 2. Analytical methods
• 3. Elimination methods
• 4. Newton and gradient
• 5. Conjugate gradient
• 6. Quasi-Newton
• 7. Nelder-Mead
• 8. Rosenbrock
• 9. Simplex
• 10. Active set

Comments, suggestions, questions etc. related to this course or website are welcome; please contact the lecturer.