BEGIN:VCALENDAR
PRODID:-//Ben Fortuna//iCal4j 1.0//EN
VERSION:2.0
CALSCALE:GREGORIAN
X-WR-CALNAME:Seminar\, Optimization and systems theory
BEGIN:VEVENT
DTSTAMP:20211017T033257Z
SUMMARY:Jan Kronqvist: (Convex) mixed-integer optimization: some algorith
ms and modeling techniques
LOCATION:Zoom
DTSTART:20210611T090000Z
DTEND:20210611T100000Z
UID:53911f93-26b5-4515-b3fe-5f399136533f
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20211017T033257Z
SUMMARY:Justin Pearson: The Essence of Constraint Programming
DESCRIPTION:Constraint Programming (CP) is a relatively young paradigm\,
geared\ntowards the elegant modelling and efficient solving of combinato
rial\nproblems\, which are so ubiquitous and important in management\,\n
engineering\, and science. CP works in a way orthogonal and\ncomplement
ary to other optimisation technologies\, such as integer\nprogramming (I
P)\, Boolean satisfiability (SAT)\, and answer-set\nprogramming (ASP).
CP has become the technology of choice in some\nareas\, such as scheduli
ng and configuration.\n\nI will present the essential principles of CP a
nd combinatorial\noptimisation\, and present some our research group's\n
(http://www.it.uu.se/research/group/optimisation research activities\nwi
thin CP and optimisation.\n
LOCATION:3418\, https://www.kth.se/places/room/id/774d2e00-417d-4642-b644
-f0c6b761e491
DTSTART:20210917T090000Z
DTEND:20210917T100000Z
UID:43e44585-ab83-4246-9955-014ce353675b
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20211017T033257Z
SUMMARY:Julian Hall\, "HiGHS: Theory\, software and Impact"
DESCRIPTION:Abstract: Since Dantzig formulated the simplex algorithm in
1947\, the widespread need to solve linear optimization problems drove
the development of algorithmic and computational techniques for decades\
, yielding several high performance commercial and open source software
systems. This talk will focus on the Edinburgh-based work on solving lar
ge scale sparse linear programming problems that underpins the high perf
ormance open source linear optimization software\, HiGHS\, the challenge
s of developing such software\, and the Impact that it has achieved.\n
LOCATION:Seminar room 3418\, via zoom. (We show the presentation using th
e projector)
DTSTART:20211001T090000Z
DTEND:20211001T100000Z
UID:8dfa7f4c-f64d-49ff-b0f3-3c556a9c0076
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20211017T033257Z
SUMMARY:Yura Malitsky: Adaptive Gradient Descent without Descent
DESCRIPTION:Abstract: In this talk I will present some recent results for
the most classical optimization method — gradient descent. We will show
that a simple zero cost rule is sufficient to completely automate gradi
ent descent. The method adapts to the local geometry\, with convergence
guarantees depending only on the smoothness in a neighborhood of a solut
ion. The presentation is based on a joint work with K. Mishchenko\, see
https://arxiv.org/abs/1910.09529.\n
LOCATION:Seminar room 3721
DTSTART:20211015T090000Z
DTEND:20211015T100000Z
UID:8a4f7bac-651e-4f1c-ba40-98f9a412662d
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20211017T033257Z
SUMMARY:Gonzalo Muñoz: TBA
DTSTART:20211022T120000Z
DTEND:20211022T130000Z
UID:d7d3e8ba-368a-4791-b6c2-9833536d4f03
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20211017T033257Z
SUMMARY:Santany Dey: TBA
DTSTART:20211105T130000Z
DTEND:20211105T140000Z
UID:b32aedc4-d14a-42a7-a09e-d32e0d7a0bd6
END:VEVENT
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