Universität Wien FIND

390028 UK Convex Representations and Relaxations for Non-convex Quadratic Optimization (2016W)

Continuous assessment of course work

Details

max. 50 participants
Language: Englisch

Lecturers

Classes (iCal) - next class is marked with N

Monday 07.11. 08:45 - 11:45 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Tuesday 08.11. 09:30 - 12:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Tuesday 08.11. 14:00 - 16:00 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 09.11. 09:30 - 12:30 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Wednesday 09.11. 14:00 - 16:00 Seminarraum 5 Oskar-Morgenstern-Platz 1 1.Stock
Thursday 10.11. 09:30 - 12:30 Seminarraum 6 Oskar-Morgenstern-Platz 1 1.Stock
Friday 11.11. 09:00 - 13:00 Seminarraum 14 Oskar-Morgenstern-Platz 1 2.Stock

Information

Aims, contents and method of the course

We consider convex relaxations for non-convex quadratic optimization that utilize semidefiniteness together with constraints obtained from the Reformulation-Linearization Technique (RLT) and generalizations of RLT. From a theoretical standpoint we show that these relaxations dominate convex relaxations obtained using some alternative methodologies, and also that in certain cases the relaxations in fact give exact representations (that is, they are tight). Computational results show that these convex relaxations usually provide excellent bounds, and for some problem classes are often empirically tight even when they are not provably tight.

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Examination topics

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Last modified: Th 30.03.2017 11:51