Universität Wien
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390047 UK VGSCO Course (2021S)

Continuous Optimization: between Mathematics and Computation

Continuous assessment of course work
REMOTE

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

14 June, 13.15 - 14.45 (Introduction)
15 June, 13.15 - 14.45 (Stochastic gradient descent I)
16 June, 9.45 - 11.15 (Stochastic gradient descent II)
16 June, 13.15 - 14.45 (Mirror descent I)
17 June, 9.45 - 11.15 (Mirror descent II)
17 June, 13.15 - 14.45 (Exercise session)
18 June, 9.45 - 11.15 (Online optimization)
18 June, 13.15 - 14.45 (Multiplicative weight update method)
21 June, 13.15 - 14.45 (Matrix scaling)
22 June, 13.15 - 14.45 (Exercise session)
23 June, 9.45 - 11.15 (Optimal transport)
23 June, 13.15 - 14.45 (Optimal transport II)
24 June, 9.45 - 11.15 (Matrix games)
24 June, 13.15 - 14.45 (Exercise session)
25 June, 9.45 - 11.15 (Final lecture)


Information

Aims, contents and method of the course

Abstract: Continuous optimization plays a prominent role between applications and pure mathematics. On one hand, it is indispensable for most machine learning problems. On the other, it constantly helps to break complexity barriers in theoretical computer science. In this series of lectures, we will explore different themes that illustrate both these aspects. We show how many seemingly unrelated themes fall under the same umbrella, the umbrella of continuous optimization.

Content:
1. Stochastic gradient descent.
2. Mirror descent.
3. Optimal transport.
4. Matrix scaling.
5. Online optimization.
6. Multiplicative weight update method.
7. Matrix games.
8. Some non-standard applications of mirror descent.

Assessment and permitted materials

Minimum requirements and assessment criteria

Standard analysis and basic probability courses

Examination topics

Reading list


Association in the course directory

Last modified: Tu 07.03.2023 00:31