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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:20100101T000000
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BEGIN:VEVENT
DTSTART;TZID=UTC:20111124T163000
DTEND;TZID=UTC:20111124T163000
DTSTAMP:20260419T014328
CREATED:20170124T102153Z
LAST-MODIFIED:20170124T102153Z
UID:624-1322152200-1322152200@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Large Scale Numerical Shape Optimisation
DESCRIPTION:Title: Large Scale Numerical Shape Optimisation Speaker: Dr. Stephan Schmidt Affiliation: Location: CPSE Seminar room (C615 Roderic Hill)Time: 4:30pm \nAbstract. Shape Optimisation problems are a special sub-class of optimisation problems with PDE constraints\, where the domain in which the PDE is defined becomes the unknown to be found. The Hadamard Theorem states that given sufficient regularity\, one can always find a representation of the shape gradient that exists on the surface of the unknown domain alone\, thereby offering the potential to create very efficient numerical schemes. Unless the structure of a shape optimisation problem is exploited in some fashion\, the resulting need to include the deformation of the surrounding domain can potentially become prohibitively expensive. The desire to create higher order methods like SQP also necessitates studies about second order shape derivatives. The talk will also focus on applications in aerodynamic design and the shaping of wave emitters. \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-large-scale-numerical-shape-optimisation/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20111117T150000
DTEND;TZID=UTC:20111117T150000
DTSTAMP:20260419T014328
CREATED:20170124T102153Z
LAST-MODIFIED:20170124T102153Z
UID:625-1321542000-1321542000@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: LAROS: a convex optimization approach for feature extraction
DESCRIPTION:Title: LAROS: a convex optimization approach for feature extractionSpeaker: Dr. Xuan Vinh Doan Affiliation: Warwick Business School – University of WarwickLocation: Room 572A Huxley BuildingTime: 3:00pm \nAbstract. We propose a convex optimization formulation using nuclear norm and $ell_1$-norm to find a large approximately rank-one submatrix (LAROS) for a given matrix. We are able to characterize the low-rank and sparsity structure of the resulting solutions. We show that our model can recover low-rank submatrices for matrices with subgaussian random noises. We solve the proposed model using a proximal point algorithm and show that it can be applied to applications in feature extraction. This is joint work with Stephen Vavasis and Kim-Chuan Toh. \nAbout the speaker.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-laros-a-convex-optimization-approach-for-feature-extraction/
END:VEVENT
BEGIN:VEVENT
DTSTART;TZID=UTC:20111110T160000
DTEND;TZID=UTC:20111110T160000
DTSTAMP:20260419T014328
CREATED:20170124T102153Z
LAST-MODIFIED:20170124T102153Z
UID:626-1320940800-1320940800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Optimization under Uncertainty - Recent Advances in Multi-Parametric Mixed Integer Linear Programming and its Applications
DESCRIPTION:Title: Optimization under Uncertainty – Recent Advances in Multi-Parametric Mixed Integer Linear Programming and its Applications Speaker: Martina Wittmann-Hohlbein Affiliation: CPSE – Department of Chemical Engineering – Imperial College LondonLocation: 219A William Penney Lab (entrance from walkway)Time: 4:00pm \nAbstract. In optimization under uncertainty\, multi-parametric programming is a powerful tool to account for the presence of uncertainty in mathematical models. Its objective is to derive the optimal solution as a function of the parameters without exhaustively enumerating the parameter space. We consider the general multi-parametric mixed integer linear programming (mp-MILP) problem. The presence of uncertainty in mixed integer linear programming models employed in widespread application fields\, including planning/scheduling\, process synthesis and hybrid control\, significantly increases the complexity and computational effort in retrieving optimal solutions. A particular difficulty arises when uncertainty is simultaneously present in the coefficients of the objective function\, the constraint matrix and the constraint vector\, which transforms the original linear model into a nonlinear and non-convex one with respect to both the optimization variables and the parameters. Here\, we discuss two novel methods for the solution of the general mp-MILP problem. The first approach deals with the global solution of the general mp-MILP model. Exploiting the special structure of the problem\, we outline the steps of a parametric branch and bound procedure.  The second approach is a two-stage method for the approximate solution of the general mp-MILP problem which combines state-of-the-art robust optimization and multi-parametric programming techniques. The two-stage method is applied to short-term batch process scheduling under uncertainty and we demonstrate its potential in the construction of a pro-active scheduling policy. \nAbout the speaker. Martina Wittmann-Hohlbein obtained a Diploma in Mathematics and Economics at Martin-Luther-University Halle-Wittenberg\, Germany\, in 2007. She joined CPSE at Imperial College in 2009.
URL:https://optimisation.doc.ic.ac.uk/event/seminar-optimization-under-uncertainty-recent-advances-in-multi-parametric-mixed-integer-linear-programming-and-its-applications/
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