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DTSTART;TZID=Europe/Berlin:20170609T103000
DTEND;TZID=Europe/Berlin:20170609T120000
DTSTAMP:20170428T121722Z
CREATED:20170323T102447Z
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UID:1108-1497004200-1497009600@alop.uni-trier.de
SUMMARY:ALOP Colloquium with Prof. Lorenz T. (Larry) Biegler
DESCRIPTION:On Friday\, June 9 2017\,  Prof. Lorenz T. (Larry) Biegler\,  will join the ALOP-Colloquium and present his recent work.  \nLorenz T. (Larry) Biegler is currently the Head and Bayer University Professor of Chemical Engineering at Carnegie Mellon University. His research interests lie in computer aided process engineering (CAPE) and include flowsheet optimization\, optimization of systems of differential and algebraic equations\, reactor network synthesis and algorithms for constrained\, nonlinear process control. Projects in these areas include analysis and development of nonlinear programming algorithms\, optimization software design and application to real-world chemical processes and energy systems. He has authored or co-authored over 400 archival publications and two textbooks\, edited nine technical books and given numerous invited presentations at national and international conferences. \n  \nNonlinear Programming Properties and Formulations for Nonlinear and Economic Model Predictive Control \n  \nNonlinear Model Predictive Control (NMPC) is a powerful approach for on-line optimization of nonlinear\, dynamic systems. This talk explores nonlinear programming (NLPs) formulations that ensure the stability and robustness of NMPC strategies.  In particular\, NLPs that satisfy familiar second order and regularity conditions have solutions that are continuous and differentiable with respect to perturbations of the problem data\, and are important prerequisites for nominal and ISS stability of NMPC controllers. Moreover\, ensuring these properties is not difficult through reformulation of the NLP subproblem and addition of exact penalty and barrier terms.  This framework is also essential for the extension of NMPC to sensitivity-based NMPC and economic NMPC\, thus leading to efficient\, on-line optimization strategies that guarantee stable and robust performance. The results of this analysis have been implemented within an optimization modeling platform and are demonstrated on challenging case studies drawn from chemical processes. \n  \nThe presentation will take place in HS 10. \nWe will meet for coffee at 10:00 in E10.
URL:https://alop.uni-trier.de/event/alop-colloquium-with-prof-lorenz-t-larry-biegler/
LOCATION:Trier University E Building\, Universitätsring 15\, Trier\, 54296\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="Research Training Group ALOP at Trier University":MAILTO:OptimizationDays@uni-trier.de
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DTSTART;TZID=Europe/Berlin:20170612T160000
DTEND;TZID=Europe/Berlin:20170612T180000
DTSTAMP:20170608T140235Z
CREATED:20170607T115606Z
LAST-MODIFIED:20170608T140235Z
UID:1316-1497283200-1497290400@alop.uni-trier.de
SUMMARY:ALOP Colloquium with Prof. Gabriel Wittum
DESCRIPTION:On Monday\, June 12\, 2017\,  Prof. Gabriel Wittum of King Abdullah University of Science and Technology  will join the ALOP-Colloquium and present his recent work entitled \nSimulation of Neuronal Signal Processing \nThe crucial feature of neuronal ensembles is their high complexity and variability. This makes modelling and computation very difficult\, in particular for detailed models based on first principles. The problem starts with modelling geometry\, which has to extract the essential features from those highly complex and variable phenotypes and at the same time has to take in to account the stochastic variability. Moreover\, models of the highly complex processes which are living on these geometries are far from being well established\, since those are highly complex too and couple on a hierarchy of scales in space and time. Simulating such systems always puts the whole approach to test\, including modeling\, numerical methods and software implementations. In combination with validation based on experimental data\, all components have to be enhanced to reach a reliable solving strategy. \nTo handle problems of this complexity\, new mathematical methods and software tools are required. In recent years\, new approaches such as parallel adaptive multigrid methods and corresponding software tools have been developed allowing to treat problems of huge complexity. In the lecture we present a three dimensional model for the simulation of signal processing in neurons. Part of this approach is a method to reconstruct the geometric structure of neurons from data measured by 2-photon microscopy. Being able to reconstruct neural geometries and network connectivities from measured data is the basis of understanding coding of motoric perceptions and long term plasticity which is one of the main topics of neuroscience. Other issues are compartment models and upscaling.   \nThis presentation will take place in HS 9 \nPlease join us for coffee at 15:45 in E10.
URL:https://alop.uni-trier.de/event/alop-colloquium-with-prof-gabriel-wittum/
LOCATION:Trier University E Building\, Universitätsring 15\, Trier\, 54296\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="Research Training Group ALOP at Trier University":MAILTO:OptimizationDays@uni-trier.de
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DTSTART;TZID=Europe/Berlin:20170626T160000
DTEND;TZID=Europe/Berlin:20170626T180000
DTSTAMP:20170619T104934Z
CREATED:20170323T103244Z
LAST-MODIFIED:20170619T104934Z
UID:1111-1498492800-1498500000@alop.uni-trier.de
SUMMARY:ALOP Colloquium with Dr. Giorgio Ferrari
DESCRIPTION:On Monday\, June 26 2017\,  Dr. Giorgio Ferrari with Bielefeld University will join the ALOP-Colloquium and present his recent work entitled  \nOn the Optimal Management of Public Debt: a Singular Stochastic Control Problem \n  \nConsider the problem of a government that wants to reduce the debt-to-GDP (gross domestic product) ratio of a country. The government aims at choosing a debt reduction policy which minimises the total expected cost of having debt\, plus the total expected cost of interventions on the debt ratio. We model this problem as a singular stochastic control problem over an infinite time-horizon. In a general not necessarily Markovian framework\, we first show by probabilistic arguments that the optimal debt reduction policy can be expressed in terms of the optimal stopping rule of an auxiliary optimal stopping problem. We then exploit such link to characterise the optimal control in a two-dimensional Markovian setting in which the state variables are the level of the debt-to-GDP ratio and the current inflation rate of the country. The latter follows uncontrolled Ornstein-Uhlenbeck dynamics and affects the growth rate of the debt ratio. We show that it is optimal for the government to adopt a policy that keeps the debt-to-GDP ratio under an inflation-dependent ceiling. This curve is given in terms of the solution of a nonlinear integral equation arising in the study of a fully two-dimensional optimal stopping problem. \n  \nThe presentation will take place in HS9. \nPlease join us for coffee at 15:45 in E10.
URL:https://alop.uni-trier.de/event/alop-colloquium-with-giorgio-ferrari/
LOCATION:Trier University E Building\, Universitätsring 15\, Trier\, 54296\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="Research Training Group ALOP at Trier University":MAILTO:OptimizationDays@uni-trier.de
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