Achtung! Das Lehrangebot ist noch nicht vollständig und wird bis Semesterbeginn laufend ergänzt.
052312 VO Computational Optimisation (2021W)
Labels
DIGITAL
An/Abmeldung
Hinweis: Ihr Anmeldezeitpunkt innerhalb der Frist hat keine Auswirkungen auf die Platzvergabe (kein "first come, first served").
Details
Sprache: Englisch
Prüfungstermine
- Montag 31.01.2022 18:30 - 20:00 Digital
- Montag 16.05.2022 10:00 - 11:30 Digital
- Montag 27.06.2022 10:00 - 11:30 Digital
Lehrende
Termine (iCal) - nächster Termin ist mit N markiert
The lecture will take place partially in presence and remote. Detailed information will be published in Moodle.
- Montag 04.10. 18:30 - 20:45 Digital
- Montag 11.10. 18:30 - 20:45 Digital
- Montag 18.10. 18:30 - 20:45 Digital
- Montag 25.10. 18:30 - 20:45 Digital
- Montag 08.11. 18:30 - 20:45 Digital
- Montag 15.11. 18:30 - 20:45 Digital
- Montag 22.11. 18:30 - 20:45 Digital
- Montag 29.11. 18:30 - 20:45 Digital
- Montag 06.12. 18:30 - 20:45 Digital
- Montag 13.12. 18:30 - 20:45 Digital
- Montag 10.01. 18:30 - 20:45 Digital
- Montag 17.01. 18:30 - 20:45 Digital
- Montag 24.01. 18:30 - 20:45 Digital
Information
Ziele, Inhalte und Methode der Lehrveranstaltung
Art der Leistungskontrolle und erlaubte Hilfsmittel
Oral exam after the semester (presumably online). Four "Sammeltermine" will be announced, candidates should register to one of them.
Mindestanforderungen und Beurteilungsmaßstab
At least half of the questions at the exam must be correctly answered to pass the course.
Prüfungsstoff
For each of the two parts of the course (each given by one of the two lecturers), slides will be made available to the participants. The content of these slides defines the topics of the exam.
Literatur
Any introductory textbook on integer programming/combinatorial optimization should cover most/all of the topics.
Zuordnung im Vorlesungsverzeichnis
Module: SWI STW CO
Letzte Änderung: Fr 12.05.2023 00:13
Topics addressed include:
- Mathematical Programming
- Discussion of various classical discrete optimization problems (facility location, traveling salesperson, ...)
- Theory of NP-completeness
- Metaheuristics
- Problems on Graphs and Networks (Maximum Flow, Spanning/Steiner tree and variants)
- Nonlinear Optimization Methods (e.g., Frank-Wolfe Method)This course is done as lecture; there is an accompanying exercise-part as an own course, students are encouraged to take both courses in the same semester.Due to the current Covid-19 situation, parts of the course will presumably be given in digital form (online via MS Teams, at the times assigned to the course). Switches between physical and digital presentation will be announced to the participants in time.