

BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//Computational Optimisation Group - ECPv6.15.11//NONSGML v1.0//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Computational Optimisation Group
X-ORIGINAL-URL:http://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
REFRESH-INTERVAL;VALUE=DURATION:PT1H
X-Robots-Tag:noindex
X-PUBLISHED-TTL:PT1H
BEGIN:VTIMEZONE
TZID:Europe/Paris
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20170326T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20171029T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20180325T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20181028T010000
END:STANDARD
BEGIN:DAYLIGHT
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
TZNAME:CEST
DTSTART:20190331T010000
END:DAYLIGHT
BEGIN:STANDARD
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
TZNAME:CET
DTSTART:20191027T010000
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTART;TZID=Europe/Paris:20181003T150000
DTEND;TZID=Europe/Paris:20181003T160000
DTSTAMP:20260419T031342
CREATED:20181001T080116Z
LAST-MODIFIED:20181001T084704Z
UID:1154-1538578800-1538582400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Random projections in mathematical programming
DESCRIPTION:Title: Random projections in mathematical programming\nSpeaker: Dr Leo Liberti\nAffiliation: CNRS LIX\, École Polytechnique\nLocation: 218 Huxley Building\nTime: 15:00 – 16:00 \nAbstract. In the algorithmic trade-off between generality and efficiency\, sometimes the only way out is to accept approximate methods. If all else fails\, we can always fall back on heuristic methods. But some form of approximation guarantee is usually preferable. In this talk we shall discuss a set of approximating reformulations to various classes of mathematical programming problems involving matrices. Random projections are a dimensionality reduction methodology which projects a set of vectors into a much lower dimensional space\, so that the projected vector set is approximately congruent to the original one with high probability. The probability of failure falls exponentially fast as the dimension increases\, making this a truly “big data” methodology. We shall show how to apply this methodology to Linear and Conic Programming\, as well as (bounded) Quadratic Programming. We shall discuss applications to Quantile Regression and Error-Correcting Codes. \nBiography. Dr Leo Liberti is CNRS Research Director (and part-time professor) at CNRS LIX (the Computer Science Laboratory) of École Polytechnique\, France. Dr Liberti did his undergraduate studies in mathematics at Imperial College London and the University of Turin. He received his Ph.D. from Imperial College London in 2004 and HDR (French professorship diploma) from Paris-Dauphine University\, Paris\, France in 2007. His main research interests are in (i) reformulations in mathematical programming\, (i) mixed-integer nonlinear programming (MINLP)\, global and combinatorial optimization\, (iii) distance geometry and bioinformatics\, and (iv) complex industrial systems and sustainable development.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-random-projections-in-mathematical-programming/
END:VEVENT
END:VCALENDAR