Universität Wien
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250063 VO Nonlinear optimization (2021W)

6.00 ECTS (4.00 SWS), SPL 25 - Mathematik
REMOTE
Moodle

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Details

Language: English

Examination dates

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 07.12. 16:30 - 18:00 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: Fr 12.05.2023 00:21