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X-WR-CALNAME:Computational Optimisation Group
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X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20110101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120702T140000
DTEND;TZID=UTC:20120702T140000
DTSTAMP:20260418T124217
CREATED:20170124T102147Z
LAST-MODIFIED:20170124T102147Z
UID:601-1341237600-1341237600@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Time-Critical Cooperative Path-Following Control of Multiple UAVs
DESCRIPTION:Title: Time-Critical Cooperative Path-Following Control of Multiple UAVsSpeaker: Prof. Naira HovakimyanAffiliation: Department of Mechanical Science and Engineering at University of Illinois at Urbana-ChampaignLocation: Room 212 William PennyTime: 2:00pm \nAbstract. Worldwide\, there has been growing interest in the use of autonomous vehicles to execute cooperative missions of increasing complexity without constant supervision of human operators. Despite significant progress in the field of cooperative control\, several challenges need to be addressed to develop strategies capable of yielding robust performance of a fleet in the presence of complex vehicle dynamics\, communications constraints\, and partial vehicle failures. In this talk\, we will present a theoretical framework for the development of decentralized strategies for cooperative motion control of multiple vehicles that must meet stringent spatial and temporal constraints. The approach adopted applies to teams of heterogeneous systems\, and does not necessarily lead to swarming behavior. Flight test results of a coordinated road search mission involving multiple small tactical UAVs will be discussed to demonstrate the efficacy of the multi-vehicle cooperative control framework presented. \nAbout the speaker. Naira Hovakimyan received her M.S. in Theoretical Mechanics and Applied Mathematics in 1988 from Yerevan State University in Armenia. She received her Ph.D. in Physics and Mathematics in 1992\, in Moscow\, from the Institute of Applied Mathematics of Russian Academy of Sciences\, majoring in optimal control and differential games. In 1997 she was awarded a governmental postdoctoral scholarship to work in INRIA\, France. In 1998 she was invited to the School of Aerospace Engineering of Georgia Tech\, where she worked as a research faculty member until 2003. In 2003 she joined the Department of Aerospace and Ocean Engineering of Virginia Tech\, and in 2008 she moved to the University of Illinois at Urbana-Champaign\, where she is a professor and Schaller faculty scholar. She is the 2011 recipient of the AIAA Mechanics and Control of Flight Award. She has coauthored one book and more than 250 refereed publications. Her research interests are in the theory of robust adaptive control and estimation with an emphasis on aerospace applications\, control in the presence of limited information\, networks of autonomous systems and game theory. She is an associate fellow and life member of AIAA\, a Senior Member of IEEE\, and a member of AMS and ISDG.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-time-critical-cooperative-path-following-control-of-multiple-uavs/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120703T140000
DTEND;TZID=UTC:20120703T140000
DTSTAMP:20260418T124217
CREATED:20170124T102147Z
LAST-MODIFIED:20170124T102147Z
UID:600-1341324000-1341324000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Protection at All Levels: Probabilistic Envelope Constraints
DESCRIPTION:Title: Protection at All Levels: Probabilistic Envelope ConstraintsSpeaker: Dr. Xu HuanAffiliation: Department of Mechanical Engineering at National University of SingaporeLocation: CPSE Seminar room (C616 Roderic Hill)Time: 2:00pm \nAbstract. Optimization under chance constraints is a standard approach to ensure that bad events such as portfolio losses\, are unlikely to happen. They do nothing\, however\, to protect more against terrible events (e.g.\, deep portfolio losses\, or bankruptcy). In this talk\, we will propose a new decision concept\, termed "probabilistic envelop constraint"\, which extends the notion of chance constraints\, to a formulation that provides different probabilistic guarantees at each level of constraint violation. Thus\, we develop a notion of guarantee across the spectrum of disasters\, or rare events\, ensuring these levels of protection hold across the curve\, literally. We further show that the corresponding optimization problem can be reformulated as a semi-infinite optimization problem\, and provide conditions that guarantee its tractability. Interestingly\, the resulting formulation is what is known as a comprehensive robust optimization in literature. This work thus provides a new fundamental link between two main methodologies in optimization under uncertainty: stochastic optimization and robust optimization. This is a joint work with Constantine Caramanis (UT-Austin) and Shie Mannor (Technion). \nAbout the speaker. Huan Xu has been an assistant professor of Mechanical Engineering of National University of Singapore since 2011. He obtained his Ph. D. degree in ECE from McGill University\, Canada\, in 2009\, and was a postdoctral research fellow of the University of Texas at Austin prior to joining NUS. His current research interest focuses on learning and decision-making in large-scale complex systems\, including machine learning\, high-dimensional statistics\, robust and adaptable optimization\, robust sequential decision making\, and applications to large-scale systems. He has published in leading operations research and machine learning journals including Operations Research\, Math. Oper. Res.\, IEEE Info. Theory\, JMLR\, and conferences including ICML\, NIPS\, and COLT.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-protection-at-all-levels-probabilistic-envelope-constraints/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120705T160000
DTEND;TZID=UTC:20120705T160000
DTSTAMP:20260418T124217
CREATED:20170124T102147Z
LAST-MODIFIED:20170124T102147Z
UID:599-1341504000-1341504000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: A decision rule approach to medium-term hydropower scheduling under uncertainty
DESCRIPTION:Title: A decision rule approach to medium-term hydropower scheduling under uncertaintySpeaker: Paula RochaAffiliation: Department of Computing – Imperial College LondonLocation: CPSE seminar room (C615 Roderic Hill)Time: 4:00pm \nAbstract. We present a multistage stochastic optimisation model for the medium-term scheduling of a cascaded hydropower system. Electricity spot prices change on a much shorter time scale than the hydrological dynamics of the reservoirs in the cascade. We exploit this property to reduce computational complexity: we partition the planning horizon into hydrological macroperiods\, and we account for intra-stage price variability by using price duration curves. Moreover\, we restrict the space of recourse decisions to those affine in the observable data\, thereby obtaining a tractable approximate problem. \nAbout the speaker.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-a-decision-rule-approach-to-medium-term-hydropower-scheduling-under-uncertainty/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20120705T163000
DTEND;TZID=UTC:20120705T163000
DTSTAMP:20260418T124217
CREATED:20170124T102146Z
LAST-MODIFIED:20170124T102146Z
UID:598-1341505800-1341505800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Robust Pricing of Monopolistic Cloud Computing Services with Service Level Agreements
DESCRIPTION:Title: Robust Pricing of Monopolistic Cloud Computing Services with Service Level AgreementsSpeaker: Vladimir RoitchAffiliation: Department of Computing – Imperial College LondonLocation: CPSE seminar room (C615 Roderic Hill)Time: 4:30pm \nAbstract. Cloud Computing is a new computing paradigm that gives end-users on-demand access to computing resources of companies that maintain large data centres. Here\, we address the optimal pricing of cloud computing services from the perspective of a monopolistic service provider that needs to manage demand responsiveness and uncertainty. We formulate the pricing problem for on-demand services as a multi-stage stochastic program and model service level agreements via chance constraints. Under weak assumptions about the demand uncertainty we show that the resulting model can be reduced to an equivalent two-stage stochastic program. As cloud computing is only just emerging\, it is impossible to reliably estimate demand distributions from historical data. Indeed\, such data may even be difficult to collect. We address this type of model uncertainty by adopting a distributionally robust approach\, assuming that only information about the location\, size and support (but not the shape) of the demand distribution is available. We show that the arising robust model can be reformulated as a second-order cone program\, and we analytically derive the worst-case distributions. Several extensions of the basic model are discussed. First\, we study generalized models in which higher-order moments of the demand distribution are known. Next\, we include multiple products and account for different product qualities. Finally\, we investigate the possibility of selling unused capacity (if any) on a spot market. \nAbout the speaker.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-robust-pricing-of-monopolistic-cloud-computing-services-with-service-level-agreements/
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