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X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20150101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20161003T150000
DTEND;TZID=UTC:20161003T150000
DTSTAMP:20260513T065607
CREATED:20170124T101756Z
LAST-MODIFIED:20170124T101756Z
UID:529-1475506800-1475506800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Pooling Problems: Advances in Theory and Applications
DESCRIPTION:Title: Pooling Problems: Advances in Theory and ApplicationsSpeaker: Fabian RigterinkAffiliation: School of Mathematical and Physical Sciences – The University of Newcastle  AustraliaLocation: Room 554 Huxley BuildingTime: 3:00pm \nAbstract. The pooling problem is a nonconvex nonlinear programming problem with important applications. The nonlinearities of the problem arise from bilinear constraints that capture the blending of raw materials. In this talk\, we summarise our recent contributions to the problem\, which fall into the following categories:  Formulations: we propose new multi-commodity flow formulations based on output\, input and output and (input\, output) commodities\, and evaluate their performance computationally. Complexity: we show that the pooling problem with one pool and a bounded number of inputs can be solved in polynomial time. Bounding the gap between the McCormick relaxation and the convex hull: we show that the so-called McCormick relaxation can be arbitrarily worse than the convex hull.  Convex hulls of bilinear functions: Padberg introduced new classes of inequalities that can significantly strengthen the McCormick relaxation. We study classes of bilinear functions where some of the Padberg inequalities characterise the convex hull\, and evaluate computationally which of the inequalities are strongest. We conclude the talk by studying an application of particular interest to Novocastrians: optimising coal blending operations at the port of Newcastle; the world’s largest coal export port. This is joint work with my PhD supervisors\, Dr Thomas Kalinowski\, Prof Natashia Boland\, and Prof Martin Savelsbergh. \nAbout the speaker. Fabian Rigterink is a PhD candidate at the University of Newcastle\, Australia. He is supervised by Dr Thomas Kalinowski\, Prof Natashia Boland\, and Prof Martin Savelsbergh. Prior to commencing his PhD\, Fabian received his BSc and MSc in Industrial Engineering and Management from Karlsruhe Institute of Technology\, Germany.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-pooling-problems-advances-in-theory-and-applications/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20161025T140000
DTEND;TZID=UTC:20161025T170000
DTSTAMP:20260513T065607
CREATED:20170116T145907Z
LAST-MODIFIED:20170116T145907Z
UID:432-1477404000-1477414800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Optimisation with occasionally accurate data
DESCRIPTION:Title: Optimisation with occasionally accurate data\nSpeaker: Coralia Cartis\nAffiliation: Mathematical Institute – Oxford and Balliol College\nLocation: Huxley building\nTime: 2:00pm (1 hour) \nAbstract. We present global rates of convergence for a general class of methods for nonconvex smooth optimization that include linesearch\, trust-region and regularisation strategies\, but that allow inaccurate problem information. Namely\, we assume the local (first- or second-order) models of our function are only sufficiently accurate with a certain probability\, and they can be arbitrarily poor otherwise. This framework subsumes certain stochastic gradient analyses and derivative-free techniques based on random sampling of function values. It can also be viewed as a robustness assessment of deterministic methods and their resilience to inaccurate derivative computation such as due to processor failure in a distribute framework. We show that in terms of the order of the accuracy\, the evaluation complexity of such methods is the same as their counterparts that use deterministic accurate models; the use of probabilistic models only increases the complexity by a constant\, which depends on the probability of the models being good. Time permitting\, we also discuss the case of inaccurate\, probabilistic function value information\, that arises in stochastic optimization. This work is joint with Katya Scheinberg (Lehigh University\, USA). \nAbout the speaker. Coralia Cartis (BSc Mathematics\, Babesh-Bolyai University\, Romania; PhD Mathematics\, University of Cambridge (2005)) has joined the Mathematical Institute at Oxford and Balliol College in 2013 as Associate Professor in Numerical Optimization. Previously\, she worked as a research scientist at Rutherford Appleton Laboratory and as a postdoctoral researcher at Oxford University. Between 2007-2013\, she was a (permanent) lecturer and senior lecturer in the School of Mathematics\, University of Edinburgh. Her research interests address the development\, analysis and implementation of algorithms for linear and nonlinear non-convex optimization problems\, suitable for large-scale problems. A particular focus of her recent research has been the complexity analysis/global rates of convergence of nonconvex optimization algorithms. Some of her methods have been included in GALAHAD optimization software library. She has also worked on applications of optimization in compressed sensing\, signal processing and for parameter estimation in climate modelling.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-optimisation-with-occasionally-accurate-data/
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