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PRODID:-//Ben Fortuna//iCal4j 1.0//EN
VERSION:2.0
CALSCALE:GREGORIAN
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BEGIN:VEVENT
DTSTAMP:20210623T045003Z
SUMMARY:Gianpiero Canessa: Static risk averse models and applications
DESCRIPTION:Abstract:\nStochastic optimization allows the modeler to inco
rporate risk into his decision making process. Different applications of
this optimization methodology will be presented\, showing in each case
the innovation incorporated into a classical deterministic model and tra
nsforming it into the stochastic version.\n\nThe main challenge of stoch
astic optimization is that one often generates models that cannot be sol
ved directly: we need to transform it to a tractable deterministic equiv
alent problem (DEP) and solve it using any of the commercial solvers ava
ilable. Naive reformulations into DEPs can\, and often will\, result in
complex and/or large DEPs that current solvers may not be able to solve
in an adequate amount of time\, or even load in memory due to its size.
This work is centered in showing how we can circumvent these difficultie
s and obtain results that are equivalent to those that would be obtained
in the original stochastic formulation.\n
LOCATION:F11
DTSTART:20190913T090000Z
DTEND:20190913T100000Z
UID:33b16184-6bf3-4325-9bd8-0303a4603417
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20210623T045003Z
SUMMARY:Scott Mason: Statistical mechanics and the Ising model
LOCATION:Room 3418\, Lindstedtsvägen 25\, 4th floor\, Department of Mathe
matics\, KTH
DTSTART:20190913T111500Z
DTEND:20190913T120000Z
UID:491012c3-b7fd-4ce5-a4b5-5ea40949df23
END:VEVENT
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