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DTSTART;TZID=Europe/Berlin:20200129T140000
DTEND;TZID=Europe/Berlin:20200218T171500
DTSTAMP:20191017T124620Z
CREATED:20191017T123456Z
LAST-MODIFIED:20191017T124620Z
UID:3138-1580306400-1582046100@alop.uni-trier.de
SUMMARY:Short course on the Introduction to Mixed-Integer Nonlinear Optimization
DESCRIPTION:Prof. Dr. Martin Schmidt\, a Principal Investigator of the Research Training Group on Algorithmic Optimization\, is offering a short course on the Introduction to Mixed-Integer Nonlinear Optimization. \nThe course will consist of 4 x 2  lectures of 90 minutes each on the following dates and times: \nWednesday\,                     21 January 2020                     14:00 – 17:15                                              HS 10 \nWednesday\,                      5 February 2020                    14:00 – 17:15                                              HS 10 \nWednesday\,                     12 February 2020                   14:00 – 17:15                                              HS 10 \nTuesday\,                           18 February 2020                   14:00 – 17:15                                              HS 10 \nCourse Abstract: \nMixed-integer nonlinear optimization problems (MINLPs) are of great importance in practice because they allow for two crucial modeling aspects. First\, using integer variables makes it possible to model decision-making. Second\, accurate modeling of real-world phenomena often leads to nonlinearities like in physics or in models of economies of scale. However\, the combination of integer variables and nonlinearities also makes these problems extremely hard to solve for large-scale instances of real-world applications. \n  \nIn this compact course\, we introduce the class of convex and nonconvex MINLPs\, discuss some MINLP-specific modeling tricks\, and study the basic algorithms for solving MINLPs. \nFor a printout of this information\, please click here. \nCourse outline: \nDay 1: Introduction to the problem class of MINLPs\n* Definition of problem class\n* Convex vs. nonconvex MINLP\n* Modeling examples\n* Modeling techniques\n* Good and bad formulations\n* General algorithmic techniques for solving MINLPs \nDay 2: Algorithmic techniques\n* Nonlinear branch-and-bound\n* Kelley’s cutting plane method\n* Outer approximation\n* LP-/NLP-based branch-and-bound \nDay 3: Getting rid of what makes the problem hard\n* MIP-based solution techniques\n* NLP-based solution techniques \nDay 4: Nonconvex MINLPs and Software\n* Under- and overestimators\n* expression trees\n* Generic relaxation strategies for nonconvex MINLPs\n* Spatial branch-and-bound\n* Modeling software (GAMS\, AMPL\, Pyomo)\n* Solvers \n 
URL:https://alop.uni-trier.de/event/short-course-on-the-introduction-to-mixed-integer-nonlinear-optimization/
CATEGORIES:Short Course
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DTSTART;TZID=Europe/Berlin:20200206T130000
DTEND;TZID=Europe/Berlin:20200206T160000
DTSTAMP:20191114T122557Z
CREATED:20191114T122557Z
LAST-MODIFIED:20191114T122557Z
UID:3227-1580994000-1581004800@alop.uni-trier.de
SUMMARY:Mathematical Writing Course with Mirjam Duer
DESCRIPTION:Prof. Dr. Mirjam Dür with Augsburg University will teach a Mathematical Writing Course on Thursday\, February 6\, 2020 from 1 to 4 pm in Room E 10.
URL:https://alop.uni-trier.de/event/mathematical-writing-course-with-mirjam-duer/
LOCATION:Trier University E Building\, Universitätsring 15\, Trier\, 54296\, Germany
CATEGORIES:Workshop
ORGANIZER;CN="RTG ALOP at Trier University":MAILTO:ALOP@uni-trier.de
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=Europe/Berlin:20200210T160000
DTEND;TZID=Europe/Berlin:20200210T180000
DTSTAMP:20200203T155305Z
CREATED:20200113T160241Z
LAST-MODIFIED:20200203T155305Z
UID:3315-1581350400-1581357600@alop.uni-trier.de
SUMMARY:CANCELLED - ALOP Colloquium with Shengfeng Zhu
DESCRIPTION:CANCELLED DUE TO THE CURRENT TRAVEL RESTRICTIONS IN CHINA\nThis presentation will be rescheduled at a later time. \nOn Monday\, February 10 2020 at 16:00 c.t.  Dr. Shengfeng Zhu of East China Normal University  will present his recent work \nentitled Finite element approximations of shape gradients with applications in shape optimization  \nAbstract: \nShape optimization has many practical applications in science and engineering. Boundary type Eulerian derivative has been widely used in shape gradient algorithms. The distributed Eulerian derivative is seldom noticed. For model problems of eigenvalue optimization and shape design in flows\, we present two types of discrete finite element schemes for shape gradients contained in distributed and boundary types of Eulerian derivatives. Our a prior error estimates show that the discrete shape gradient associated with the distributed Eulerian derivative on a fixed domain has higher convergence rate and better accuracy. Furthermore\, we report numerical evidence that the distributed shape gradient algorithm can have better numerical performance during deformations.  \n\n  \nThe presentation will take place in HS 9. \nPlease join us for coffee at 15:45 in E10. \nCANCELLED DUE TO THE CURRENT TRAVEL RESTRICTIONS IN CHINA
URL:https://alop.uni-trier.de/event/alop-colloquium-with-dr-shengfeng-zhu/
LOCATION:Trier University E Building\, Universitätsring 15\, Trier\, 54296\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="RTG ALOP at Trier University":MAILTO:ALOP@uni-trier.de
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DTSTART;TZID=Europe/Berlin:20200224T160000
DTEND;TZID=Europe/Berlin:20200224T180000
DTSTAMP:20191108T111752Z
CREATED:20191016T115717Z
LAST-MODIFIED:20191108T111752Z
UID:3122-1582560000-1582567200@alop.uni-trier.de
SUMMARY:ALOP Colloquium with Marianna de Santis
DESCRIPTION:On Monday\, February 24 2020 at 16:00 c.t.  Dr. Marianna de Santis\, Università de Roma\, DIAG  will present her recent work \nentitled Branch-and-bound Algorithms for structured Mixed Integer Nonlinear Programming Problems \nAbstract: \nMixed Integer Nonlinear Programming (MINLP) is the area of optimization  that addresses nonlinear problems with continuous and integer variables.  MINLP has proven to be a powerful tool for modeling and it combines challenges from both combinatorial and nonlinear optimization. \nIn this talk\, we deal with the computation of dual bounds for different classes of structured MINLPs. In particular\, we will focus on problems having quadratic objective function. \nThe branch-and-bound algorithms presented generalize the approach for unconstrained convex quadratic integer programming proposed by Buchheim\, Caprara and Lodi [Math.Progr.\, 135\, pp.369-395 (2012)] to the presence of linear constraints and nonconvex objective function. The main feature of the latter approach consists of a sophisticated preprocessing phase\, leading to a fast enumeration on the branch-and-bound nodes. \nExperimental results on randomly generated instances show that the approach significantly outperforms the MIQP solver of CPLEX12.6 for instances with a small number of constraints. Hints on how to extend the key ideas to more general contexts will be given. \n  \nThe 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-marianna-de-santis/
LOCATION:Trier University E Building\, Universitätsring 15\, Trier\, 54296\, Germany
CATEGORIES:Colloquium
ORGANIZER;CN="RTG ALOP at Trier University":MAILTO:ALOP@uni-trier.de
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