Universität Wien FIND

Due to the COVID-19 pandemic, changes to courses and exams may be necessary at short notice. Inform yourself about the current status on u:find and check your e-mails regularly.

Please read the information on https://studieren.univie.ac.at/en/info.

250063 VO Nonlinear optimization (2021W)

6.00 ECTS (4.00 SWS), SPL 25 - Mathematik
REMOTE
Tu 07.12. 16:30-18:00 Digital

Registration/Deregistration

Note: The time of your registration within the registration period has no effect on the allocation of places (no first come, first served).

Details

Language: English

Lecturers

Classes (iCal) - next class is marked with N

Tuesday 05.10. 16:30 - 18:00 Digital
Wednesday 06.10. 13:15 - 14:45 Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 12.10. 16:30 - 18:00 Digital
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 13.10. 13:15 - 14:45 Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 19.10. 16:30 - 18:00 Digital
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 20.10. 13:15 - 14:45 Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 27.10. 13:15 - 14:45 Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 03.11. 13:15 - 14:45 Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 09.11. 16:30 - 18:00 Digital
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday 10.11. 13:15 - 14:45 Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday 16.11. 16:30 - 18:00 Digital
Wednesday 17.11. 13:15 - 14:45 Digital
Tuesday 23.11. 16:30 - 18:00 Digital
Wednesday 24.11. 13:15 - 14:45 Digital
Tuesday 30.11. 16:30 - 18:00 Digital
Wednesday 01.12. 13:15 - 14:45 Digital
Tuesday 14.12. 16:30 - 18:00 Digital
Wednesday 15.12. 13:15 - 14:45 Digital
Tuesday 11.01. 16:30 - 18:00 Digital
Wednesday 12.01. 13:15 - 14:45 Digital
Tuesday 18.01. 16:30 - 18:00 Digital
Wednesday 19.01. 13:15 - 14:45 Digital
Tuesday 25.01. 16:30 - 18:00 Digital
Wednesday 26.01. 13:15 - 14:45 Digital

Information

Aims, contents and method of the course

Goal is the thorough understanding of design, properties, and practical behavior of algorithms for the solution of smooth optimization problems with finitely many discrete and continuous variables, with and without constraints. Black box methods using function values only, local gradient-based methods and global (branch and bound) methods will be discussed. The emphasis will be on methods that scale well to high-dimensional problems. Complexity results will be derived where appropriate.

Assessment and permitted materials

Exams are oral after the end of the semester, approx. 45 minutes, by personal arrangement.

Minimum requirements and assessment criteria

To follow the course you need a thorough knowledge of linear algebra, analysis, and numerical analysis.

To pass the exam you need to be able to give a coherent account of the concepts, algorithms and theorems presented, with motivations and outlines of the main arguments. For sehr gut (1) you need to be able to give proof details.

Examination topics

Relevant for the exam is the material from the lecture notes covered in the course.

Reading list

There will be detailed lecture notes for most of what is covered. Additional relevant literature will be given in the course during the first week.

Association in the course directory

MAMO

Last modified: Mo 15.11.2021 13:49