Universität Wien

053612 VU Optimisation Methods for Data Science (2020W)

4.00 ECTS (3.00 SWS), SPL 5 - Informatik und Wirtschaftsinformatik
Continuous assessment of course work
Moodle

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

max. 25 participants
Language: English

Lecturers

Classes (iCal) - next class is marked with N

  • Thursday 08.10. 11:30 - 14:45 Digital
  • Thursday 15.10. 11:30 - 13:00 Digital
  • Thursday 22.10. 11:30 - 14:45 Digital
  • Thursday 29.10. 11:30 - 13:00 Digital
  • Thursday 05.11. 11:30 - 14:45 Digital
  • Thursday 12.11. 11:30 - 13:00 Digital
  • Thursday 19.11. 11:30 - 14:45 Digital
  • Thursday 26.11. 11:30 - 13:00 Digital
  • Thursday 03.12. 11:30 - 14:45 Digital
  • Thursday 10.12. 11:30 - 13:00 Digital
  • Thursday 17.12. 11:30 - 14:45 Digital
  • Thursday 07.01. 11:30 - 13:00 Digital
  • Thursday 14.01. 11:30 - 14:45 Digital
  • Thursday 21.01. 11:30 - 13:00 Digital
  • Thursday 28.01. 11:30 - 14:45 Digital

Information

Aims, contents and method of the course

Full digital synchronous mode (e-meet the professors live every week; recording is not guaranteed!)

Platform is moodle:

https://moodle.univie.ac.at/course/view.php?id=169428

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Contents:

1. Geometric foundations of duality

1.1 Convexity and minimal distance projection
1.2 Properties of the minimal distance projection
1.3 Separation of convex sets
1.4 Supporting hyperplane and Farkas' Lemma

2. The concept of duality in optimization

2.1 Lagrange duality for constrained optimization problems
2.2 Duality gap, quality guarantee, and complementary slack
2.3 Minimax, saddle points, and optimality conditions
2.4 Convex problems: Slater condition, Wolfe dual

3. Practical aspects of duality in optimization

3.1 Linear and quadratic optimization
3.2 Ascent directions for the dual function
3.3 Dual (steepest) ascent method
3.4 (Dual) cutting planes
3.5 Duality for discrete problems; branch-and-bound

Assessment and permitted materials

(1) virtual-oral presentations of exercises (from the lecture notes, to be prepared in advance; format: a single .pdf with your name, max.size 5MB) which will be awarded by up to 30 points.

(2) a take-home exam (scheduled by majoritry vote to 14 January 2021). Net working time will be set tight, so we suggest to prepare well (from experience, you will lack time to find the answer during exam without having thought of the topic before). Details will be communicated in due course.
Exam will be awarded by up to 20 points.

(3) Active virtual cooperation during class will be awarded by up to 20 points, depending on the intensity and relevance of your communication (e.g., questions regarding administration won't be relevant for grading)

(4) To pass the exam/course successfully, you need 36 points.

Grades:

0-35: nicht genuegend/fail (5)
36-43: genuegend/pass (4)
44-53: befriedigend/satisfactory (3)
54-63: gut/good (2)
64-70: sehr gut/excellent (1)

Minimum requirements and assessment criteria

see above

Examination topics

all material covered by lecture notes (see moodle)

Reading list

Lecture notes

Bazaraa, M.S., Sherali, H.D., Shetty, C.M.: Nonlinear Programming: Theory and Algorithms, Wiley

Association in the course directory

Modul: OMD

Last modified: Fr 12.05.2023 00:13