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X-ORIGINAL-URL:http://optimisation.doc.ic.ac.uk
X-WR-CALDESC:Events for Computational Optimisation Group
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DTSTART:20110101T000000
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DTSTART;TZID=UTC:20121108T150000
DTEND;TZID=UTC:20121108T150000
DTSTAMP:20260510T181459
CREATED:20170124T102146Z
LAST-MODIFIED:20170124T102146Z
UID:594-1352386800-1352386800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Reflections on robustness in stochastic programs with risk constraints
DESCRIPTION:Title: Reflections on robustness in stochastic programs with risk constraintsSpeaker: Dr. Milos KopaAffiliation: Department of Probability and Mathematical Statistics – Charles University in PragueLocation: Room 301 William PenneyTime: 3:00pm \nAbstract. This paper is a contribution to the robustness analysis for stochastic programs whose set of feasible solutions depends on the probability distribution P. For various reasons\, probability distribution P may not be precisely specified and we study robustness of results with respect to perturbations of P. The main tool is the contamination technique. For the optimal value\, local contamination bounds are derived and applied to robustness analysis of the optimal value of a portfolio performance under risk-shaping constraints. To illustrate the theoretical results\, numerical examples for several mean-risk models are presented. Finally\, under suitable conditions on the structure of the problem and for discrete distributions we shall suggest a new robust portfolio efficiency test with respect to the first (second) order stochastic dominance criterion and we shall exploit the contamination technique to analyze the resistance with respect to additional scenarios. \nAbout the speaker. Dr. Milos Kopa is an assistant professor at Charles University in Prague. He received his Ph.D. degree in Econometrics and Operations research in 2006 (supervisor: Prof. Jitka Dupacova\, Charles University in Prague).  He is a member of several scientific societies: Stochastic Programming Community\, EURO working group on financial modelling\, EUROPT. Recently he has become an elected member (and secretary) of Managerial Board of new EURO working group on stochastic programming.     He is vice-head of the Center of Excellence “Dynamic models in economics” that comprises about 40 leading researchers (in quantitative economics and finance) from Czech Republic.      The research of Milos Kopa is focused on: stochastic programming theory and applications\, especially financial applications. In recent years he has published several papers dealing with portfolio efficiency with respect to stochastic dominance criteria; data envelopment analysis and its relation to stochastic dominance; robustness (contamination) in stochastic programs with risk constraints\, etc.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-reflections-on-robustness-in-stochastic-programs-with-risk-constraints/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20121121T140000
DTEND;TZID=UTC:20121121T140000
DTSTAMP:20260510T181459
CREATED:20170124T102145Z
LAST-MODIFIED:20170124T102145Z
UID:593-1353506400-1353506400@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Algorithms and computer architectures for efficient real-time optimization and linear algebra solvers
DESCRIPTION:Title: Algorithms and computer architectures for efficient real-time optimization and linear algebra solversSpeaker: Eric C KerriganAffiliation: Department of Aeronautics and Department of Electrical and Electronic Engineering – Imperial College LondonLocation: Room 408 Electrical & Electronic Engineering DepartmentTime: 2:00pm \nAbstract. In many engineering applications where one would like to implement control and signal processing algorithms\, one needs to use the latest measurements to update and solve a sequence of numerical optimization or linear algebra problems. Solving these problems in a computationally efficient and numerically reliable fashion on an embedded computing system is a challenging task.  One of the key choices that an engineer has to make in order to determine the speed\, cost and power consumption of a microprocessor is the number representation that will be used in the arithmetic units. CPUs within modern desktop or laptop PCs provide hardware support for double precision floating-point arithmetic. However\, most microprocessors in embedded systems do not support double precision floating-point arithmetic; they often only support single-precision floating-point and/or fixed-point arithmetic. It is therefore possible that\, because of a significant decrease in precision or dynamic range\, a numerical algorithm that gives reliable results on the office PC or laptop might give completely different results when implemented on an embedded computing system.  We will present novel mathematical formulations\, computer architectures\, optimization solvers and linear algebra solvers to show that computational resources can be reduced significantly using very low precision arithmetic\, without sacrificing accuracy. We will also present new mathematical results that allow one to use fixed-point arithmetic to make impressive computational savings in iterative linear algebra solvers. Our theoretical results will be supported by implementations on a Field Programmable Gate Array (FPGA) and we will show that it is possible to exceed the peak theoretical performance of a 1TFLOP/s general-purpose GPU. \nAbout the speaker. Dr Kerrigan’s research includes the optimal and robust control of nonlinear\, constrained and distributed parameter systems. His research is focused on the development of efficient numerical methods and computational hardware architectures for solving the resulting problems and is applied to a variety of problems in aerospace and fluid dynamics.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-algorithms-and-computer-architectures-for-efficient-real-time-optimization-and-linear-algebra-solvers/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20121127T140000
DTEND;TZID=UTC:20121127T140000
DTSTAMP:20260510T181459
CREATED:20170124T102145Z
LAST-MODIFIED:20170124T102145Z
UID:592-1354024800-1354024800@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: Matrix Learning Problems and First-Order Optimization
DESCRIPTION:Title: Matrix Learning Problems and First-Order OptimizationSpeaker: Dr. Andreas Argyriou Affiliation: Toyota Technological Institute at ChicagoLocation: Room 217 Huxley BuildingTime: 2:00pm \nAbstract. In the past few years\, there has been significant interest in nonsmooth convex optimization problems involving matrices\, especially in the areas of machine learning\, statistics and control. Instances of such problems are multitask learning and matrix completion\, robust PCA\, sparse inverse covariance selection etc. I will present PRISMA\, a new optimization algorithm for minimizing a convex objective which decomposes into three parts: a smooth part\, a simple non-smooth Lipschitz part\, and a simple nonsmooth non-Lipschitz part. Our algorithm combines the methodologies of smoothing and accelerated proximal methods. Moreover\, our convergence result removes the assumption of bounded domain required by Nesterov's smoothing methods. I will show how PRISMA can be applied to the problems of max-norm regularized matrix completion and clustering\, robust PCA and sparse inverse covariance selection\, and compare to state of the art methods.  \nAbout the speaker. Andreas Argyriou has received degrees in Computer Science from MIT and a PhD in Computer Science from UCL. The topic of his PhD work has been on machine learning methodologies integrating different tasks and data sources. He has held postdoctoral and research faculty positions at UCL\, TTI Chicago\, KU Leuven and is currently in Ecole Centrale Paris with an RBUCE-UP fellowship. His current interests are in the areas of machine learning with big and complex data\, compressed sensing and convex optimization methods.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-matrix-learning-problems-and-first-order-optimization/
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BEGIN:VEVENT
DTSTART;TZID=UTC:20121129T150000
DTEND;TZID=UTC:20121129T150000
DTSTAMP:20260510T181459
CREATED:20170124T102145Z
LAST-MODIFIED:20170124T102145Z
UID:591-1354201200-1354201200@optimisation.doc.ic.ac.uk
SUMMARY:Seminar: The Branch-and-Sandwich Algorithm for Mixed-Integer Nonlinear Bilevel Programming Problems
DESCRIPTION:Title: The Branch-and-Sandwich Algorithm for Mixed-Integer Nonlinear Bilevel Programming ProblemsSpeaker: Dr. Polyxeni-Margarita KleniatiAffiliation: Centre for Process Systems Engineering at Imperial College LondonLocation: Room 144 HuxleyTime: 3:00pm \nAbstract. We extend our recently introduced algorithm for general bilevel programming problems\, Branch-and-Sandwich (Kleniati and Adjiman\, J. Global Optim.\, 2012)\, to the class of mixed-integer nonlinear bilevel problems.  As in the original algorithm\, auxiliary inner lower and upper bounding problems are constructed in order to bound the inner value function and provide constant bound cuts for the outer upper and outer lower bounding problems. The KKT-based relaxations\, originally proposed for the inner upper bounding and the outer lower bounding problems\, are applicable with respect to the lower-level continuous variables based on appropriate constraint qualifications\, but are no longer required. In the extension that we present here\, a robust counterpart approach is employed to formulate the inner upper bounding problem and the resulting bound cut may be the only constraint added to the proposed outer lower bounding problem. The branching framework with auxiliary lists of nodes\, as developed for the original Branch-and-Sandwich\, is also applied to the discrete case. The algorithm is used to solve successfully ten literature problems. \nAbout the speaker. Dr. Kleniati is undertaking her second postdoctoral research position with Prof. Adjiman at the Chemical Engineering department of Imperial College London. She received her PhD in Computing and Optimisation research in 2010 under the supervision of Prof. Rustem at the department of Computing in Imperial College London.  The research of Polyxeni Kleniati is currently focused on the global optimisation of bilevel programming problems with applications to chemical engineering.
URL:http://optimisation.doc.ic.ac.uk/event/seminar-the-branch-and-sandwich-algorithm-for-mixed-integer-nonlinear-bilevel-programming-problems/
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