This website is under construction. We will be adding additional information as we get closer to the start of the summer semester.
This class provides a detailed overview of algorithms for unconstrained and constrained smooth, nonlinear optimization problems in finite dimensions. It will emphasize the interplay of optimization and numerical linear algebra. Optimality conditions and associated constraint qualifications for constrained problems will be presented and discussed. Moreover, the class will give an introduction to algorithmic differentiation (AD).
Skills from Analysis I and II (MA1/ MA2), Lineare Algebra I (MA4) as well as basic optimization skills are highly recommended.
This class is comprised of:
- Detailed lecture notes
- 2x 2h lectures per week
- Exercise sheets with corresponding answer sheets
- Labs for co-working on exercise sheets
- An exam following the end of the lecture period
The detailed concept and how to activly participate in the class will be discussed in the first lecture on Monday 17.04.2023 at 11:15h in the main lecture hall of the Mathematikon.
Dates and Schedule
As soon as it is updated for the upcoming semester, you will find the information on lecture meetings in the LSF. Information on the exercises can be found in Müsli.
Please also referr to the class’ schedule.
Exercises will be centered around one exercise sheet per week containing both theoretical/analytical questions and numerical/implementation exercises. Default programming language – in the sense that it will be used in our answer sheets and we will be providing support for it – will be Python. Feel free to work with another programming laguage that is suitable for implementing the optimization algorithms if you are self sufficient working with it.
In the exercise lab groups (see Müsli), you will have the oportunity to work on the exercise sheets with your peers and with support from our tutors. You can get the most out of their time if you show up having already taken a first look at the exercises and bringing your own questions and discussion topics to the meetings. Feel free to ask questions on the lecture notes or the lectures as well.
For successful participation in this semester’s exercises, you will be asked to submit your exercises weekly, see exams. To evaluate your success on the exercise sheets, please compare your work with the answer sheets we will provide and, of course, feel free to talk to your tutor in the labs.
You may work on the exercises in whichever type of group you would like but we request that you submit your answers/programs in groups of at most three people.
To register for this class, please register for an exercise group at Müsli. Please also register in case you are interested in participating in this class but not in the exercises. This will allow us to get an idea of class demand.
Attention(!): Registration for the class does not automatically include registration for an examination. More information on the exams can be found in the section exams.
We will be offering exams after the lecture period ends. Whether those will be oral or written and when they will take place will be decided based on demand and we will communicate that information as soon as possible.
If you want to take the exam with us, we require you to be registered in Müsli and to meet at least one of the following criteria:
- You have been admitted to an exam for this class in a previous semester and let us know via E-Mail when and by whom you have been admitted by 30.06.2023.
- You have successfully participated in this semester’s exercise program in the sense that:
- You have worked every exercise on every exercise sheet except for at most one sheet to a reasonable degree. “To a reasonable degree” means that you have at least sketched an approach of how to solve the excercise and made a connection to the material of the lecture.
- You have presented in the labs a working implementation of your answer to a programming exercise on at least one of the first 6 and at least one of the final 7 exercise sheets to your tutor.
This class is self sufficient and does not rely on outside literature. If you would like to read up additionally, we can however recommend the following literature: