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X-ORIGINAL-URL:https://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
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TZOFFSETFROM:+0000
TZOFFSETTO:+0000
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DTSTART:20180101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191128T140000
DTEND;TZID=UTC:20191128T153000
DTSTAMP:20260417T092351
CREATED:20191025T170003Z
LAST-MODIFIED:20191025T170134Z
UID:1418-1574949600-1574955000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar with Prof. Greg Sorkin: Extremal Cuts and Isoperimetry in Random Cubic Graphs
DESCRIPTION:The minimum bisection width of random cubic graphs is of interest because it is one of the simplest questions imaginable in extremal combinatorics\, and also because the minimum bisection of (general) cubic graphs plays a role in the construction of efficient exponential-time algorithms\, and it seems likely that random cubic graphs are extremal. \nIt is known that a random cubic graph has a minimum bisection of size at most 1/6 times its order (indeed this is known for all cubic graphs)\, and we reduce this upper bound to below 1/7 (to 0.13993) by analyzing an algorithm with a couple of surprising features. We increase the corresponding lower bound on minimum bisection using the Hamilton cycle model of a random cubic graph. We use the same Hamilton cycle approach to decrease the upper bound on maximum cut. We will discuss some related conjectures.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-with-prof-greg-sorkin-extremal-cuts-and-isoperimetry-in-random-cubic-graphs/
LOCATION:Huxley 217
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191202T103000
DTEND;TZID=UTC:20191202T233000
DTSTAMP:20260417T092351
CREATED:20190723T135610Z
LAST-MODIFIED:20190916T101732Z
UID:1290-1575282600-1575329400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar by prof. Miguel Anjos
DESCRIPTION:Professor Miguel Anjos from the University of Edinburgh is giving a seminar on: Tight-and-Cheap Conic Relaxations for AC Optimal Power Flow and Optimal Reactive Power Dispatch \n  \nAbstract: The classical alternating current optimal power flow problem is nonconvex and generally hard to solve. We propose a new conic relaxation obtained by combining semidefinite optimization with RLT. The proposed relaxation is stronger than the second-order cone relaxation\, competitive with the recently proposed QC relaxation\, and up to one order of magnitude faster than for the semidefinite chordal approach on benchmarks with up to 6515 nodes\, with comparable global bounds. We extend the approach to optimal reactive power dispatch\, which requires the introduction of binary and integer variables\, and obtain with similar results and performance.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-by-prof-miguel-anjos/
LOCATION:Huxley 217
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BEGIN:VEVENT
DTSTART;TZID=UTC:20191206T140000
DTEND;TZID=UTC:20191206T153000
DTSTAMP:20260417T092351
CREATED:20191205T141856Z
LAST-MODIFIED:20191205T141940Z
UID:1432-1575640800-1575646200@optimisation.doc.ic.ac.uk
SUMMARY:Seminar by  Associate Professor Jakob Nordström
DESCRIPTION:TITLE:\nLearn to Relax: Integrating Integer Linear Programming with Conflict-Driven Search\n\n  \nABSTRACT:\nPseudo-Boolean (PB) solvers optimize 0-1 integer linear programs by\nextending the conflict-driven learning paradigm from SAT solving.\nThough PB solvers should be exponentially more efficient than SAT\nsolvers in theory\, in practice they can sometimes get hopelessly stuck\neven when the relaxed linear program (LP) is infeasible over the\nreals.  Inspired by mixed integer programming (MIP)\, we address this\nproblem by interleaving incremental LP solving with cut generation\nwithin the conflict-driven PB search.  This hybrid approach\, which for\nthe first time combines MIP techniques with full-blown conflict\nanalysis over linear inequalities using the cutting planes method\,\nsignificantly improves performance on a wide range of benchmarks\,\napproaching a “best of two worlds” scenario between SAT-style\nconflict-driven search and MIP-style branch-and-cut.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-by-associate-professor-jakob-nordstrom/
LOCATION:Huxley 217
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BEGIN:VEVENT
DTSTART;TZID=UTC:20200323T140000
DTEND;TZID=UTC:20200323T150000
DTSTAMP:20260417T092351
CREATED:20200127T124815Z
LAST-MODIFIED:20200317T114146Z
UID:1446-1584972000-1584975600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar by Prof. Oliver Stein (Cancelled due to the Covid-19 situation)
DESCRIPTION:Title: Pessimistic Bilevel Optimization \nPessimistic bilevel optimization problems\, as optimistic ones\, possess a structure involving\nthree interrelated optimization problems. Moreover\, their finite infima are only\nattained under strong conditions. We address these difficulties within a framework of moderate\nassumptions and a perturbation approach which allow us to approximate such finite\ninfima arbitrarily well by minimal values of a sequence of solvable single-level problems. \nTo this end\, as already done for optimistic problems\, we introduce the standard version of\nthe pessimistic bilevel problem. For its algorithmic treatment\, we reformulate it as a standard\noptimistic bilevel program with a two follower Nash game in the lower level. The latter lower level\ngame\, in turn\, is replaced by its Karush-Kuhn-Tucker conditions\, resulting in a single-level\nmathematical program with complementarity constraints. \nWe show that the perturbed pessimistic bilevel problem\, its standard version\, the\ntwo follower game as well as the mathematical program with complementarity constraints\nare equivalent with respect to their global minimal points. We also highlight the more intricate\nconnections between their local minimal points. As an illustration\, we consider a regulator problem\nfrom economics. \n  \nBio: \nOliver Stein is full professor at the Institute of Operations Research (IOR) at the Karlsruhe Institute of Technology (KIT).\nHe received his doctoral degree from the University of Trier in 1997\, and his venia legendi from RWTH Aachen University in 2002.\nHis research covers algorithms and their theoretical foundation for continuous and mixed-integer nonlinear optimization problems\,\nparametric optimization\, multi-leader-multi-follower games\, and multi-objective optimization. Oliver was fellow of the Friedrich-Ebert\nFoundation\, the Alexander-von-Humboldt Foundation\, and the German Research Foundation (Heisenberg followship)\, and received various\nteaching awards. Oliver is member of MOS\, SIAM\, GAMM\, GOR\, and DMV. Since 2015 he acts as Editor-in-Chief of MMOR.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-by-prof-oliver-stein/
LOCATION:CPSE lecture theatre
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20240119T140000
DTEND;TZID=UTC:20240119T150000
DTSTAMP:20260417T092351
CREATED:20240117T221445Z
LAST-MODIFIED:20240117T221445Z
UID:1630-1705672800-1705676400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Bayesian Optimization in Molecule Space: Challenges and Opportunities
DESCRIPTION:Title: Bayesian Optimization in Molecule Space: Challenges and Opportunities\nSpeaker: Austin Tripp\nAffiliation: University of Cambridge\nLocation: Huxley 315 \nAbstract. Rational design of experiments in chemistry is one of the most commonly mentioned applications of Bayesian optimization (BO). Therefore you might presume that existing BO algorithms for chemistry are well-developed. In this talk I explain how performing BO on the discrete\, structured space of molecules introduces extra complexity to BO which standard methods do not handle well. I will outline specific problems and potential avenues for solving them\, in addition to covering some recent work in this area. All are welcome\, but the target audience for this talk is optimization researchers interested in the fundamental algorithmic problems which chemistry applications present. \nBiography. Austin Tripp is a final-year PhD student at Cambridge researching ML methods for molecules. More info on his website austintripp.ca \n 
URL:https://optimisation.doc.ic.ac.uk/event/seminar-bayesian-optimization-in-molecule-space-challenges-and-opportunities/
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