Course description
The talk will start with familiar facts about solving polynomial equations in 1 complex variable, and show how optimization can be helpful. Then I will present some results about optimization algorithms, in the deterministic setting. After that, it is explained why it is important to allow uncertainty (stochastic elements) into the picture, what is the main objective, and how the deterministic algorithm can be used. At the end, we will run some experiments to illustrate the theoretical considerations. Along the way, necessary knowledge in mathematics will be mentioned.
Target audience
Undergraduate students, Master students, PhD candidates, researchers who are interested in optimization, equation solving or Deep Learning/Machine Learning.
Learning outcomes
The participants will:
- Understand crucial ideas in optimization and iterative algorithms.
- Understand how to treat the stochastic setting, and why it is important.
- Understand the relation between equation solving and optimization, and how optimization actually helps.
- See how algorithms actually work in practice, through experiments.
Prerequisites
- A good command of undergraduate calculus is useful. However, I will recall necessary things, so don't worry if you forget.
Technical and Material Requirements
- The participants should bring something which can connect to the internet and run algorithms there.
Lunch
- There will be a complementary buffet-lunch at 12:00-13:00 from the Sverdrups spiseri (Georg Sverdrups Hus, 2nd floor).
- After you get your lunch, join us at Store møterom where we have reserved seats for #DSDays2026 participants.
Number of participants
After the soft deadline, a waiting list will be automatically handled upon de-registrations.
Contact
If you have any questions regarding this course, or anything else about the DSDays 2026 event, please contact us per email: digitalscholarship@ub.uio.no