Universität Wien
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269011 VO Numerical Methods III - Optimisation (2022S)

3.00 ECTS (2.00 SWS), SPL 26 - Physik

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

Examination dates

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 10.03. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 17.03. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 24.03. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 31.03. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 07.04. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 28.04. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 05.05. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 12.05. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 19.05. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 02.06. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 09.06. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien
  • Thursday 23.06. 10:30 - 12:00 Ernst-Mach-Hörsaal, Boltzmanngasse 5, 2. Stk., 1090 Wien

Information

Aims, contents and method of the course

Basic concepts of continuous optimization from theory with simple proofs to (large-scale) numerical methods. Basic knowledge in analysis and (numerical) linear algebra (e.g. Numerical Methods I & II) are helpful but not strictly required.

Topics: line search and trust region algorithms, Newton and (large-scale) Quasi-Newton methods, nonlinear conjugate gradient (NCG) methods, theory of (nonlinear) constrained optimization, linear programming, (sequential) quadratic programming, penalty and augmented Lagrangian methods, interior point methods.
Both theoretical background and practical numerical aspects (e.g. machine learning, python scikit-learn, (nonlinear) dimensionality reduction, etc) will be emphases.

Unfortunately this lecture lacks its accompanying exercise course. Therefore, in addition, exercise examples will be made available, which can be worked out on a voluntary basis.

Assessment and permitted materials

Oral exam (via announced "collective" or individual appointment).

Minimum requirements and assessment criteria

The lecture covers continuous optimization from theorey to algorithms. Positive assessment of the oral exam.

Examination topics

Topics discussed in the lecture.

Reading list

Lecture notes.

Optional:
J. Nocedal, S.J. Wright, Numerical Optimization, 2006 Springer.
R. Fletcher, Practical methods of optimization, John Wiley & Sons, 2013.

Association in the course directory

CO-MAT3

Last modified: We 25.01.2023 11:10