Optimization and Uncertainty (Summer 2025)
Topical
- Logbook, with details about schedule and material.
- More details about the course will be published here in due time.
Organizational
- Lecturer: Prof. Dr. Martin Hoefer
- Exercises: Rilind Sahitaj, Finn Seesemann
- RWTH Online: course page, exercise page
- Room in RWTH Moodle
- Lectures:
Mondays, 10:30 - 12:00h in AH I (Informatikzentrum)
Tuedays, 08:30 - 10:00h in AH III (Informatikzentrum) - Exercises: TBD
- First Exam: TBD. (written exam)
- Second Exam: TBD. (written exam)
Material
Lecture Notes
Will be published here.
Exercise Sheets
Weekly exercise sheets will be published via Moodle. Solutions must be composed by groups of (initially) 3 students.
Your solutions must be submitted as a single PDF file via Moodle.
If you score at least 75%, you can obtain one grading step bonus for the exam. To receive the bonus, you must pass the exam, and at least one solution must be presented during an exercise session.
Topic
Suppose you go on a sequence of n dates, after each of which you have to decide if you want to start a relationship or see more people - when is the best time to stop dating to get the best partner? Optimization problems of this type with uncertainty about problem parameters arise frequently in modern computer systems. Often large data sets can be used to infer probabilistic information about the expected benefit of the potential decisions. The course gives an introduction to algorithmic techniques for solving optimization problems with uncertainty. We will start with classic stopping problems as the one sketeched above (i.e., secretary and prophet-inequality problems) and advance to more general online optimization problems in which the input is partly stochastic and partly adversarial. Further topics of interest are recent developments in the theory of online learning, Markov decision processes, as well as probing and recommendation problems.
Literature
- We will provide pointers to the literature in the logbook for each course session.