250063 VO Nonlinear optimization (2021W)
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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
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday
12.10.
16:30 - 18:00
Digital
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
13.10.
13:15 - 14:45
Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday
19.10.
16:30 - 18:00
Digital
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
20.10.
13:15 - 14:45
Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
27.10.
13:15 - 14:45
Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
03.11.
13:15 - 14:45
Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
Tuesday
09.11.
16:30 - 18:00
Digital
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Hörsaal 13 Oskar-Morgenstern-Platz 1 2.Stock
Wednesday
10.11.
13:15 - 14:45
Digital
Seminarraum 11 Oskar-Morgenstern-Platz 1 2.Stock
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
N
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: Mo 15.11.2021 13:49