In this talk, we describe a scalable and accurate quantum tomography (QT) recovery framework with approximation guarantees. Our objective is to accurately recover a d x d complex positive semi-definite (PSD) matrix X with a known rank r from dimensionality reducing measurements as y=A(X)+e, where A is a subsampled Pauli operator, and e is some bounded perturbation.

The QT problem has three salient aspects that set it apart from the existing low-rank recovery problems:

1. Standard nuclear norm minimization approaches are not directly applicable in QT since X is a PSD density matrix and must have a trace of 1.

2. The Pauli measurement operator A in QT creates a major scalability bottleneck as the range of its adjoint is fully dense and the size of X is exponential in the number of quantum bits q (qubits): d=2^q.

3. The Pauli measurement operator A has the rank restricted isometry property (RIP) from O(rdlog^6d) measurements.

We show how to achieve provable QT recovery in linear space and quadratic time (in d) and computationally demonstrate 16-qubit recovery from 4rd Pauli measurements. To this end, we derive new sparse simplex projections, leverage randomized SVD’s, and propose a new online subspace reweighting technique. We also describe how our algorithmic developments apply to seemingly different problems, where convex relaxations of the sparsity and rank appear as constraints, such as measure learning, Markowitz portfolio optimization, and metric learning problems.   

 "Shape and topology optimization of structural components: formulations and efficient solution algorithms"

prof. Pierre Duysinx (Department of Aerospace and Mechanical Engineering, ULg) 

ABSTRACT 

To face the challenge of higher performance and better energy efficiency, engineers call for efficient and rational tools to handle large and complex design problems. Combining computer aided design and mathematical programming algorithms provides an efficient and rationale approach to engineering design. Structural optimization has been developed now for more than 30 years with a growing success. Leading edge research is still pushing ahead the frontiers trying to solve larger scale problems, more general formulations, including more physics.

 Initiated by the pioneer work of Bendsøe and Kikuchi (1988), topology optimization is one of the most flexible and general approaches. The design problem is formulated as an optimum material distribution. Because of the bitmap description of the geometry, the formulation introduces a large number of design variables, so that a global performance function is generally preferred in the design problem statement. An interesting contribution consists in extending the scope of the minimum compliance problem to local design restrictions such as local material failure. We are also investigating strongly non-linear problems arising in MEMS design problems dominated by coupled electro mechanical behaviors.

 More recently, the (topology) optimal material description has been challenged by the novel level set description of the geometry. This implicit description of boundaries enables a flexible description of the geometry allowing a certain modification of the topology but with a smoother description of the shape. The level set description is naturally combined with the eXtended Finite Element Method (XFEM) to be able to relax the constraint of using conforming meshes, simplifying strongly the user interaction with the optimization process. The approach naturally enables a higher capability in handling various design constraints. For instance, level set description is applied to the shape optimization of flexible bodies subject to dynamics loading in complex multibody systems. Thus shape optimization using level set description provides a very nice complementary tool to topology optimization.

 Finally a major characteristic of the research approach that is developed lies in the great attention paid to tailor efficient solution algorithms coping with the larger and larger scale optimization problems. The approach relies on the so-called sequential convex programming approach: the implicit and non-linear optimization problem is replaced by a sequence of explicit approximations. Then the convex sub-problems are solved by resorting to efficient math programming algorithms, in particular the dual maximization optimizer.

(slides)

Abstract

Optimization plays an important role in many machine learning methods. However, the two areas have very different focuses. The gap has caused that, on the one hand, some machine learning tasks may not suitably use optimization techniques, and on the other hand, optimization researchers may wrongly consider irrelevant issues when applying their methodology to machine learning. In this talk I will discuss my experiences on kernel and linear classification. In particular, we discuss support vector machines (SVM), which involve some challenging  optimization problems. As a machine learning researcher with an optimization background, I will show lessons learned in the past and how we eventually construct some widely used machine learning software. 

Biographical Information

Chih-Jen Lin is a distinguished professor at the department of computer science and information engineering at the National Taiwan University. He studied mathematics at the National Taiwan University where he obtained his Bachelor degree in 1993. He received his Master degree and his PhD from the department of industrial and operations engineering  of the University of Michigan in 1996 and 1998 respectively.  After some time as a research associate in Argonne national laboratory, in 1998 he became a professor at the  National Taiwan University, where he is now a distinguished professor. Chih-Jen Lin has received numerous other awards for his research and is, among other, a fellow of the IEEE.

Prof. Chih-Jen Lin will also give 2 tutorials on January 15th and 16th. see 

"Support vector machines and kernel methods: status and challenges" (link)

"Large-scale Machine Learning in Distributed Environments" (link)

About the Lecture Series:

The "Simon Stevin Lecture Series on Optimization in Engineering" is set up in order to promote optimization in engineering. For this aim, every quarter of the year an outstanding international scholar is invited to report on latest progress in the development of optimization algorithms and their applications in engineering.
Simon Stevin (1548-1620) was a Flemish mathematician and engineer. Among other, he helped to advance the use of decimal fractions, was the first to explain the tides by the attraction of the moon, and discovered the hydrostatic paradox. He made numerous inventions, among them a wind propelled carriage with sails, the "land yacht", which once impressed Prince Maurice of Orange as it moved faster than horses, in around 1600 on the beach between Scheveningen and Petten. Simon Stevin was fond of promoting the use of science in daily life and in craftmanship, and translated various mathematical terms into dutch. Among other, he introduced the dutch word for mathematics, "wiskunde". 

24th Simon Stevin Lecture on Optimization in Engineering

"Shape Optimization in PDE Based Applications"

Volker Schulz,
Department of Mathematics,University of Trier,Germany

poster, flyer

slides

Abstract

Shape optimization is a challenging task, in particular in the context of PDE based applications. The talk will discuss recent approaches towards efficient optimization methods for applied shape optimization problems featuring a) a simultaneous optimization approach for the coupling of the treatment of the PDE with the optimization strategy and b) shape gradient and Hessian generation in the context of the shape calculus. Applications in the fields of aerodynamic, thermoelastic and acoustic applications will be presented. Furthermore, recent ideas towards an interpretation of the shape calculus in terms of optimization on the shape manifold will be discussed. These ideas have the potential to settle the annoying issue of the lack of symmetry of the standard shape Hessian and to enable a KKT point of view on shape optimization which has not been realizable so far.

Biographical Information

Volker Schulz is a full professor at the department of mathematics at the University of Trier, Germany.
He studied mathematics at the University of Augsburg where he obtained his diploma in 1990, and received his PhD and Habilitation from the University of Heidelberg in 1996 and 2000, respectively. After the PhD he was an associate researcher at the Institute for Computer Applications, University of Stuttgart, and in 1999, he became the head of the research group on Nonlinear Optimization and Inverse Problems at the Weierstrass Institute for Applied Analysis and Stochastics (WIAS) in Berlin. Since 2001 he is full professor in Trier, where he served, among other, head of the department, in 2002-2004. Volker Schulz' research interests are in several fields of numerical mathematics, in particular nonlinear large-scale optimization, partial differential equations, multigrid methods and inverse problems, with applications for example in robotics, chemical engineering, groundwater flow, and turbomachinery design. He is a member of the ediorial boards of the journals Computing and Visualization in Science, Acta Applicandae Matematicae, and the SIAM Journal on Scientific Computing, among other.

About the Lecture Series:

The "Simon Stevin Lecture Series on Optimization in Engineering" is set up in order to promote optimization in engineering. For this aim, every quarter of the year an outstanding international scholar is invited to report on latest progress in the development of optimization algorithms and their applications in engineering.
Simon Stevin (1548-1620) was a Flemish mathematician and engineer. Among other, he helped to advance the use of decimal fractions, was the first to explain the tides by the attraction of the moon, and discovered the hydrostatic paradox. He made numerous inventions, among them a wind propelled carriage with sails, the "land yacht", which once impressed Prince Maurice of Orange as it moved faster than horses, in around 1600 on the beach between Scheveningen and Petten. Simon Stevin was fond of promoting the use of science in daily life and in craftmanship, and translated various mathematical terms into dutch. Among other, he introduced the dutch word for mathematics, "wiskunde".

Participation is free but please register via the doodle:

http://www.doodle.com/d3fp3x84f98w83zt

Agenda:

14.00-14.30h: Presentation by Ingrid Lepot from Cenaero

14.30-14.45H: discussion and time for questions

14.45-15.00h: coffee break

Around 15h: end of the visit from Cenaero

15.15h: WG4 meeting continued (may start a bit earlier – depending on visit Cenaero)

15.15-15.45h: Overview of the two-slide presentations of the WG4 members (in preparation of the OPTEC retreat – cf. further below)

15.45-16.15h: Further planning of the OPTEC retreat – decision on agenda, possible presentations, …  

"Second-Order Multidimensional ICA: Theory and Methods"

Dana Lahat (Tel-Aviv University)

Abstract:

Independent component analysis (ICA) and blind source separation (BSS) deal with extracting a number of mutually independent elements from a set of observed linear mixtures. Motivated by various applications, this talk considers a more general and more flexible model: the sources can be partitioned into groups exhibiting dependence within a given group but independence between two different groups. We argue that this is tantamount to considering multidimensional components, as opposed to the standard ICA case which is restricted to one-dimensional components.

Multidimensional data may occur due to various complex relations within the dependent elements. The dimension of a dependent group may not always reflect the actual number of its underlying elements. As a result, in multidimensional models, there is not always a physically meaningful interpretation to separating the multidimensional components back into single-dimensional elements.

The core of this work is the statistical analysis of the blind separation of multidimensional components based on second-order statistics, in a piecewise-stationary model. We develop the likelihood and the associated estimating equations for the Gaussian case. We obtain closed-form expressions for the Fisher information matrix and the Cramér-Rao lower bound (CRLB) of the de-mixing parameters, as well as the mean square error (MSE) of the component estimates. For Gaussian data, our separation criterion achieves, up to higher-order terms, the CRLB, and is thus optimal in the MSE sense.

We present necessary and sufficient conditions for the model to be identifiable. These are also the sufficient and necessary conditions for joint block diagonalization (JBD) of any set of real positive-definite symmetric matrices to be unique.

We then turn to the case when the separation procedure is based on a one-dimensional model, followed by a clustering step, in which the one-dimensional output is assigned into groups, representing the multidimensional components. This attitude is common practice in various applications. We prove that for piecewise stationary data, and when only second-order statistics are used, this form of separation is suboptimal. In particular, we obtain a closed-form expression for the MSE of this separation procedure, an expression which is based only on the model parameters. By comparing this expression with the MSE when the correct model is used, one can obtain the exact expected gain directly from the model parameters, without resorting to numerical simulations or Monte-Carlo trials.

Our analysis is verified through numerical experiments. In addition, we demonstrate the theoretical gain in the accuracy of component recovery in the presence of multidimensional components for several dependence scenarios.

Data assimilation is a methodology for estimating the initial state of a dynamical system by combining the information from observational data and from a numerical prediction model that describes the evolution of the system. The most important fields of application of data assimilation are the ocean and weather forecasts. In this talk, we briefly survey the two main approaches used in data assimilation: the sequential one, based on the statistical estimation theory (Kalman filter) and the variational one, based on the optimal control theory. This last approach amounts to solve a very large weighted nonlinear least-squares problem called 4D-Var (four-dimensional variational problem). Focusing on the solution of the 4D-Var problem, we discuss two challenging issues in the context of large-scale operational data assimilation: preconditioning techniques, for accelerating the convergence, and derivative-free techniques, to avoid the computation of derivatives. 

Biographical Information

Annick Sartenaer is full professor at the Mathematics Department of the University of Namur (FUNDP) in Belgium, from which she received her PhD in 1991.  She was Senior Researcher at CERFACS (European Center for Research and Advanced Training in Scientific Computation) in France from 1997 to 1999 and Research Associate of the FNRS (Fond National de la Recherche Scientifique) from 1999 to 2002. She received her Habilitation (HDR) from the University Paul Sabatier in Toulouse in 1999.  Her research interests are in the area of nonlinear continuous optimization, with a special emphasis on large-scale issues, and in real-world applications such as weather forecast or energy distribution. 


About the Lecture Series:

The "Simon Stevin Lecture Series on Optimization in Engineering" is set up in order to promote optimization in engineering. For this aim, every quarter of the year an outstanding international scholar is invited to report on latest progress in the development of optimization algorithms and their applications in engineering.
Simon Stevin (1548-1620) was a Flemish mathematician and engineer. Among other, he helped to advance the use of decimal fractions, was the first to explain the tides by the attraction of the moon, and discovered the hydrostatic paradox. He made numerous inventions, among them a wind propelled carriage with sails, the "land yacht", which once impressed Prince Maurice of Orange as it moved faster than horses, in around 1600 on the beach between Scheveningen and Petten. Simon Stevin was fond of promoting the use of science in daily life and in craftmanship, and translated various mathematical terms into dutch. Among other, he introduced the dutch word for mathematics, "wiskunde". 

"Quarterly Meeting of OPTEC Working Group 4 on PDE- constrained optimization"

Proposed agenda

1. Brainstorm on connection between applied math and application: try to define simple problems for CS (connect to exercises? Simple paper?) & Continue discussion on flow problems (Burgers equation etc.). (40 min)

2. Possible visit & presentation by Axel Kroener in Autumn (question from Moritz) (10 min)

3. next dates WG4 meeting + brief discussion OPTEC retreat (10 min)

4. coffee (10 min)

5. Brainstorm on next year’s aims and objectives for WG4 (30 min)

6. planning next meeting (10 min)

 Program (see below for detailed abstracts)

May 2

Dept. of Computer Science, 200A02.112

 14h30-14h50: Lieven De Lathauwer, A Short Introduction to Tensor Decompositions

 14h50-15h45: Eugene Tyrtyshnikov, Kronecker Representation of TT Decompositions, New Wavelet Transforms, Fast Tensor Interpolation

 15h45-16h00: break

 16h00-16h30: Laurent Sorber, State of the Art Overview of Algorithms for Tensor Decompositions

 16h30-17h00: Nick Vannieuwenhoven, A New Truncation Strategy for the Higher-order Singular Value Decomposition

 May 3

Dept. of Electrical Engineering (ESAT), 00.62

14h30-15h30: Boris N. Khoromskij, Efficient Numerical Approximation of Multi-dimensional PDEs in Quantized Tensor Spaces

 15h30-15h45: Stefan Vandewalle, Overview Research NATW

 15h45-16h00: break

 16h00-16h30: Ignat Domanov, On the Uniqueness of the Canonical Polyadic Decomposition: New Sufficient Conditions Based on Properties of Khatri-Rao Products of Compound Matrices

 16h30-17h00: Mikael Sorensen, Canonical Polyadic Decomposition with Orthogonality Constraints

 17h00-17h30: Toon Van Waterschoot, Sensing Wave Fields in a Finite Element Framework

Lieven De Lathauwer, A Short Introduction to Tensor Decompositions

(slides)

Eugene Tyrtyshnikov, Kronecker Representation of TT Decompositions, New Wavelet Transforms, Fast Tensor Interpolation

(slides) 

 Laurent Sorber, State of the Art Overview of Algorithms for  Tensor Decompositions

(slides) 

The canonical polyadic and rank-(Lr,Lr,1) block term decomposition (CPD and BTD, respectively) are two closely related tensor decompositions. The CPD is an important tool in psychometrics, chemometrics, neuroscience and data mining, while the rank-(Lr,Lr,1) BTD is an emerging decomposition in signal processing and, recently, blind source separation. We present a decomposition that generalizes these two and give an overview of the state of the art in algorithms for these decompositions, including their drawbacks and benefits. Among these algorithms are recently developed (complex) optimization-based methods such as limited-memory BFGS and matrix-free nonlinear least squares techniques. In the latter, we exploit the structure of the Jacobian's Gramian by means of efficient expressions for its matrix-vector product. Combined with an effective preconditioner, numerical experiments confirm that these methods are among the most efficient and robust currently available for computing the CPD, rank-(Lr,Lr,1) BTD and their generalized decomposition.

Nick Vannieuwenhoven, A New Truncation Strategy for the Higher-order Singular Value Decomposition

(slides)

We present an alternative strategy to truncate the higher-order singular value decomposition (T-HOSVD), called the sequentially truncated HOSVD (ST-HOSVD). It requires less operations to compute and often improves the approximation error with respect to the T-HOSVD. Whenever a tensor is truncated to its multilinear rank, the basis computed by the T-HOSVD and ST-HOSVD coincide. In one experiment we performed, the results of a numerical simulation of a partial differential equation were compressed by T-HOSVD and ST-HOSVD. At the same truncation rank, the approximation errors were similar, and the bases computed by both algorithms were indistinguishable. The execution time, on the other hand, was reduced from 2h45 for T-HOSVD to one minute for ST-HOSVD, representing a speedup of 133. In another experiment, we compare T-HOSVD and ST-HOSVD for constructing a multilinear model for classifying handwritten digits.

Boris N. Khoromskij, Efficient Numerical Approximation of Multi-dimensional PDEs in Quantized Tensor Spaces

(slides)

 Modern methods of tensor-product approximation by separation of variables allow an efficient lowparametric calculus of functions and operators in higher dimensions. Most common separable representations combine the canonical, Tucker, tensor train (TT) and the quantized-TT (QTT) decompositions. The QTT tensor format makes it possible to represent (approximate) the multivariate functions, operators and dynamical systems in the quantized tensor spaces with the log-volume complexity scaling with respect to the size of full tensor [2]. This opens the way to the profound numerical simulation of high-dimensional PDEs getting rid of the “curse of dimensionality” and rigorous restrictions on the grid size. The numerical efficiency is justified by the remarkable QTT-approximation properties proven for the wide class of functions and operators [2, 3]. We focus on the approximation and complexity results in the quantized tensor formats applied to the solution of d-dimensional elliptic and parabolic equations [1] - [5]. Numerical tests indicate the logarithmic computational complexity of the QTT tensor approximation for the parametric elliptic PDEs, in computational quantum chemistry and in the multi-dimensional dynamics.

http://personal-homepages.mis.mpg.de/bokh

References

[1] I.V. Gavrilyuk, and B.N. Khoromskij. Quantized-TT-Cayley transform to compute dynamics and spectrum of high-dimensional Hamiltonians. Comp. Meth. in Applied Math., v.11 (2011), No. 3, 273-290.

[2] B.N. Khoromskij. O(d log N)-Quantics Approximation of N-d Tensors in High-Dimensional Numerical Modeling. J. Constr. Approx. v. 34(2), 257-289 (2011).

[3] B.N. Khoromskij. Tensors-structured Numerical Methods in Scientific Computing: Survey on Recent Advances. Chemometrics and Intellingent Laboratory Systems, 110 (2012), 1-19.

[4] B.N. Khoromskij, and Ch. Schwab. Tensor-Structured Galerkin Approximation of Parametric and Stochastic Elliptic PDEs. SIAM J. Sci. Comp., 33(1), 2011, 1-25.

[5] B.N. Khoromskij, and I. Oseledets. DMRG+QTT approach to the computation of ground state for the molecular Schrodinger operator. ¨ Preprint 68/2010, MPI MiS, Leipzig 2010 (Numer. Math., submitted).

 Ignat Domanov, On the Uniqueness of the Canonical Polyadic Decomposition: New Sufficient Conditions Based on Properties of Khatri-Rao Products of Compound Matrices

(slides)

 The decomposition of a tensor in a minimal linear combination of rank-1 tensors is called its Canonical Polyadic Decomposition (CPD). The decomposition is also known as Canonical / Parallel Factor Decomposition (CANDECOMP/PARAFAC). The most well-known result concerning CPD uniqueness is due to J. Kruskal: let A, B, C be the factor matrices in a CPD of a tensor T, and let kA+kB+kC≥2R+2,where kA, kB, kC denote the k-rank of A, B, C, respectively, then the CPD of T is unique. Other uniqueness conditions are due to T. Jiang and N.D. Sidiropoulos, and L. De Lathauwer. These conditions are on one hand more restrictive than Kruskal’s in the sense that one of the factor matrices, say C, needs to have full column rank, which implies that the rank R is bounded by the number of rows of C, i.e., by the third tensor dimension. On the other hand, the results are an order of magnitude more relaxed than Kruskal’s in terms of the conditions on A and B. The latter may be expressed as conditions on the Khatri-Rao product of the second compound matrices of A and B. In this talk we establish a theory for CPD uniqueness by working from the Jiang-Sidiropoulos-De Lathauwer results towards Kruskal’s result, thereby extending work of A. Stegeman. We relax the condition on C, no longer requiring that it has full column rank, while making the conditions on A and B more restrictive. The latter are conditions on the Khatri-Rao product of m-th compound matrices of A and B, where m > 2. In the other direction, we present a condition on A and B that ismore relaxed than the least restrictive Jiang-Sidiropoulos condition. Part of the study leads to new Kruskal-type conditions that are more relaxed than the original. For instance, we explain that, if kA+rB+rC≥2R+2, and rA+kB+rC≥2R+2, and rA+rB+kC≥2R+2, where rA, rB, rC denote the rank of A, B, C, respectively, then the CPD of T is unique. We discuss both deterministic and generic conditions. We show INDSCALvariants. We also present results concerning uniqueness of one factormatrix, regardless of overall uniqueness

Mikael Sorensen, Canonical Polyadic Decomposition with  Orthogonality Constraints

(slides) 

Canonical Polyadic Decomposition (CPD) of a higher-order tensor is an important tool in mathematical engineering. In several signal processing  applications  at least one of the matrix factors is constrained to be column-wise orthonormal.  If  the   tensor admits a CPD where one of the factors is constrained to be column-wise orthonormal, then it can be shown that it admits an algebraic solution under very mild conditions. However, in practical signal processing applications noise is always present and consequently  only a low-rank tensor approximation  is of interest.  Contrary to the unconstrained case, a  low-rank approximation  of a tensor by an orthogonality constrainted CPD always exists.  We also explain  that by taking the this constraint in account, more efficient and noise robust numerical algorithms for the computation of the constrained CPD can be obtained.  In particular, orthogonality-constrained versions of the  CPD  methods based on  simultaneous  matrix  diagonalization  and  alternating least squares  are presented.   

Toon Van Waterschoot, Sensing Wave Fields in a Finite Element Framewor

(slides)

Quarterly Meeting of OPTEC WG 4 on Shape and Topology Optimization

Proposed agenda

- Continue discussion on topology optimization: how to constraint complexity of the solution in a rigorous setting? (30 min – hope the topology adepts are willing to take the lead once more – to all others, please refresh your memory, cf. presentation in attachment)

- A PDE-constrained optimization problem – problem presentation by Joel Anderson (to be confirmed– 20 min)

- coffee break + informal discussion (15 min)

- Date for next meeting + agenda (10 min)

- Brainstorm on connection between applied math and application: try to define simple problems for CS (connect to exercises? Simple paper?). Setup discussion on possible joint project proposals. (2 times 15 min in smaller groups)

Optimization methods have been widely used in biology related fields with great success for example in systems biology, metabolic engineering, biochemical engineering amongst others. Most of the work has been focused on developing mathematical models trying to describe the steady-state and dynamical behaviour obtained from the observation of living organisms at the scale of single cell up to biochemical reactors. Optimization is then performed typically offline to obtain a better understanding of the organism under study from a design or operational standpoint.  Comparatively, fewer studies have been performed with the objective of optimizing a specified performance index on-line.

There are many problems specifically associated with online optimization compared to off-line. Model mismatch, model uncertainty, unmeasured disturbances have to be taken into account to make sure to optimize the actual system and not the model. The number of degrees of freedom is also much lower. Methods on how to address these problems will be given by studying several applications to biochemical systems with a focus on bioreactors. Perspectives for future research will be outlined.

Biographical Information

Prof. Michel Perrier is presently Head of the Department of Chemical Engineering at Polytechnique Montréal since Dec. 2009. He obtained his Ph.D. from McGill University. Before joining Polytechnique  as a professor in 1993, he worked in industry as a control engineer for a period of seven years at Shell Canada, the Pulp and Paper Research Institute of Canada and the Biotechnology Research Institute in Montréal. He has been a visiting professor at the Centre for Integrated Dynamics and Control in the department of Electrical Engineering at the University of Newcastle in Australia in 2001, at the Centre for Systems Engineering and Applied Mechanics at the Université Catholique de Louvain in Belgium in 2002, and at University Polytechnic of Catalunya in Spain in 2008-2009. His research interests are in the field of dynamics, control and optimization of biotechnological processes. He has been vice-chair and then chair of the technical committee on Biosystems and Bioprocesses of the International Federation of Automatic Control (IFAC) from 2001 to 2007. In 2008, he was awarded an honorary doctorate from Faculté Polytechnique de Mons in Belgium and in 2009 the D.G. Fisher Award from the Systems and Control Division of the Canadian Society for Chemical Engineering. In June 2010, he was inducted Fellow of the Canadian Academy of Engineering.

About the Lecture Series:

The "Simon Stevin Lecture Series on Optimization in Engineering" is set up in order to promote optimization in engineering. For this aim, every quarter of the year an outstanding international scholar is invited to report on latest progress in the development of optimization algorithms and their applications in engineering.
Simon Stevin (1548-1620) was a Flemish mathematician and engineer. Among other, he helped to advance the use of decimal fractions, was the first to explain the tides by the attraction of the moon, and discovered the hydrostatic paradox. He made numerous inventions, among them a wind propelled carriage with sails, the "land yacht", which once impressed Prince Maurice of Orange as it moved faster than horses, in around 1600 on the beach between Scheveningen and Petten. Simon Stevin was fond of promoting the use of science in daily life and in craftmanship, and translated various mathematical terms into dutch. Among other, he introduced the dutch word for mathematics, "wiskunde". 

"Efficient numerical methods for moving horizon estimation"

Niels Haverbeke (K.U. Leuven, ESAT-SCD)

Abstract: 

"EnerKite - Ways to cost-effective airborne wind energy converter"

Alexander Bormann

Abstract:

The topic of our talk will be "Ways to an cost-effective airborne wind turbine technology". The Presentation will show results of the preliminary EnerKite designs and reveal the importance of design principles, operational constraints and material selection.

Biographical Information
Alexander Bormann, CEO of aeroix, works in the field of wind engineering since 1993. He developed an optimized design method for the towers of multimegawatt turbines. Currently he and his company are developing technologies to make wind turbine towers useless. Aeroix is successful in kite automation since 2008. In addition to power generation Aeroix offers solutions for energy and material efficiency in aeronautics and architecture.

"MIXED-INTEGER DYNAMIC OPTIMIZATION IN SYSTEMS BIOLOGY"

Julio R. Banga, IIM-CSIC, Vigo, Spain

Abstract

Mathematical optimization aims to make a system or design as effective or
functional as possible, computing the quality of the different
alternatives using a mathematical model. Most models in systems biology
have a dynamic nature, usually described by sets of differential
equations. Dynamic optimization addresses this class of systems, seeking
the computation of the optimal time-varying conditions (control variables)
to minimize or maximize a certain performance index. Dynamic optimization
can solve many important problems in systems biology, including optimal
control for obtaining a desired biological performance, the analysis of
network designs and computer aided design of biological units.

In this talk I will describe our research in continuous and mixed-integer
dynamic optimization (MIDO) problems in the framework of computational
systems biology. I will also present results for
several challenging optimization problems related with bioreactor
optimization, optimal drug infusion
to a patient and the minimization of intracellular oscillations.

This interdisciplinary course is part of the thematic training of the Leuven Arenberg Doctoral School Training Programme and supported by IAP BCRYPT and LICT. The course is mainly aimed at Ph.D. students from all disciplines (either from the K.U.Leuven or from other universities,) but also open to undergraduate students, post-docs, people working in industry, or anyone else interested on the topic.

The course will provide an overview of various aspects of privacy from the technical, legal, economics, and social science perspectives. While the broad focus of the course is on privacy in electronic services, this year’s edition of the course will have a special focus on social networks.

When

·            Wednesday, June 23, from 09:30 to 17:30

·            Thursday, June 24, from 09:00 to 18:00

Where

Computer room at the Mediacentre (Faculty of Social Sciences)

Speakers

The course will last two days and consist of eight lectures. The lecturers include five speakers from different departments and faculties in K.U.Leuven and an invited speaker:

·            Prof. Alessandro Acquisti, (Carnegie Mellon University, USA)

·            Prof. Bettina Berendt, Computer Science (K.U.Leuven)

·            Dr. Claudia Diaz, Electrical Engineering (K.U.Leuven)

·            Dr. David Geerts, Faculty of Social Sciences (K.U.Leuven)

·            Seda Gürses, Electrical Engineering / Computer Science (K.U.Leuven)

·            Eleni Kosta, Faculty of Law (K.U.Leuven)

Registration

·            The course is free of charge, but attendees are required to register by sending an email to claudia.diaz@esat.kuleuven.be

·            The registration deadline is: Tuesday, June 15

If you have any questions or would like to know more information please send an email to claudia.diaz@esat.kuleuven.be.

Programme

Wednesday, June 23

09:30 Introduction (Claudia Diaz)

10:30 Coffee break

11:00 Overview of Privacy Enhancing Technologies (PETs)  (Claudia Diaz)

12:30 Lunch break

14:00 Exploring European data protection: From social networks to cookies (Eleni Kosta)

15:30 Coffee break

16:00 Privacy and Web mining (Bettina Berendt)

17:30 End

Thursday, June 24 

09:00 To share or not to share - a user's perspective on privacy in social networks (David Geerts)

10:30 Coffee break

11:00 Privacy Concerns and Information Disclosure: An Illusion of Control Hypothesis (Alessandro Acquisti)

12:30 Lunch break

14:00 Privacy, Requirements Engineering and Online Social Network Services (Seda Gürses)

15:30 Coffee break

16:00 Predicting Social Security Numbers From Public Data (Alessandro Acquisti)

17:30 Discussion speakers and participants

18:00 end

ANNOUNCEMENT of  a K.U.Leuven Seminar, Campus ARENBERG,

Prof.  Ingrid DAUBECHIES
(Princeton university, USA)

“Surfing with Wavelets”

Tuesday May 11, 2010 from 11h00 to 12h00

Thermotechnisch Instituut, Kasteelpark Arenberg 41, 3001 HEVERLEE
Aula Van de Tweede Hoofdwet (TI 01.0002)

ABSTRACT:

Wavelets are used in the analysis of sounds and images, as well as in many other applications. The wavelet transform provides a mathematical analog to a music score: just as the score tells a musician which notes to play when, the wavelet analysis of a sound takes things apart into elementary units with a well defined frequency (which note?)  and at a well defined time (when?). For images wavelets allow you to first describe the coarse features with a broad brush, and then later to fill in details. This is similar to zooming in with a camera: first you can see that the scene is one of shrubs in a garden, then you concentrate on one shrub and see that it bears berries, then, by zooming in on one branch, you find that this is a raspberry bush. Because wavelets allow you to do a similar thing in more mathematical terms, the wavelet transform is sometimes called a "mathematical microscope".

Wavelets are used by many scientists for many different applications.  Outside science as well, wavelets are finding their uses: wavelet transforms are an integral part of the image compression standard JPEG2000.

The talk will start by explaining the basic principles of wavelets,  which are very simple. Then they will be illustrated with some examples, including an explanation of image compression.

BRIEF BIOGRAPHY:

Ingrid Daubechies received both her bachelor's and Ph.D. degrees (in 1975 and 1980) from the Free University in Brussels, Belgium.  She held a research position at the Free University until 1987. From 1987 to 1994 she was a member of the technical staff at AT&T Bell Laboratories, during which time she took leaves to spend six months (in 1990) at the University of Michigan, and two years (1991-93) at Rutgers University. She is now at the mathematics department and the Program in Applied and Computational Mathematics at Princeton University.  She was awarded a Leroy P. Steele prize for exposition in 1994 for her book Ten Lectures on Wavelets. From 1992 to 1997 she was a fellow of the John D. and Catherine T. MacArthur Foundation. She is a member of  the American Academy of Arts and Sciences, the American Mathematical Society, the Mathematical Association of America, the Society for Industrial and Applied Mathematics, and the Institute of Electrical and Electronical Engineers. She is married and has two children.

ORGANIZERS:

The seminar is organized by the Departments of Electrical Engineering, Mathematics and Computer Science on the occasion of the appointment of Prof. Daubechies as International Francqui Professor at the Free University in Brussels (January-June 2010).

Application areas: nonparametric statistics, system identification, machine learning and data mining

Short CV: László Györf. is professor at the Department of Computer Sciences and Information Theory at Budapest University of Technology and Economics and is the head of the Research Group for Informatics and Electronics of the Hungarian Academy of Sciences.
He obtained the diploma in Mathematics and Physics and two PhDs from the University of Budapest in 1970, 1974 and 1988. Since 1997 he is fellow of IEEE and since 2001 he is also Ordinary Member of the Hungarian Academy of Sciences. He is also the winner of numerous prizes such as Farkas Gyula Prize, Pollák - Virág Prize, Jacob Wolfowitz Prize and Széchenyi Prize. His main interests are stochastic approximation, pattern classification, nonparametric density, regression and entropy estimation, mathematical statistics, prediction of time series, multiple access communication, source coding and empirical portfolio selection.
He is the (co)-author of more than 100 papers in various fields of statistics and of several books such as Nonparametric density estimation: The L1 view, Nonparametric Curve Estimation from Time Series, A Probabilistic Theory of Pattern Recognition, A Distribution-Free Theory of Nonparametric Regression, Principles of Nonparametric Learning (ed.) and Multiple Access Channels: Theory and Practice (ed.).

Website: http://www.szit.bme.hu/~gyorfi/indexen.html

Abstract:

 This dissertation considers two main research topics. First, this thesis explores the applicability of Proper Orthogonal Decomposition (POD) and Galerkin projection in the design of model predictive control schemes for tubular chemical reactors. These processes pose very interesting control problems, since their behavior is modeled by highly nonlinear Partial Differential Equations (PDEs), and they require the satisfaction of both their input and state constraints. In this study, POD is used together with Garlerkin projection for reducing the high-dimensionality of the discretized systems used to approximate the PDEs that model the reactors. Then, based on the resulting reduced-order models, Kalman filters and predictive controllers are designed. Although a significant model order reduction can be obtained with POD and Galerkin projection, these techniques do not reduce the number of state constraints (linear inequality constraints) which is typically very large. In this thesis we propose two methods to tackle this problem. In the first method we use univariate polynomials to approximate part of the basis vectors derived with the POD technique, and then we apply the theory of positive polynomials to find good approximations of the state constraints by linear matrix inequalities. In the second method, we exploit the similarities between the coefficients of consecutive state constraints for developing a greedy algorithm that selects a small number of constraints from the complete set.

The second main research subject of this thesis is related to speeding up the evaluation of reduced-order models derived by POD from nonlinear high-dimensional systems. Unlike the linear time-invariant case, the model-order reduction by POD and Galerkin projection does not conduce to an important computational saving when the high-dimensional models under consideration are nonlinear. Therefore, this thesis introduces two methods for coping with this situation. The first method takes advantage of the input-output nonlinear mapping capabilities, and the fast on-line evaluation of multi-layer perceptrons for accelerating the evaluation of the POD models. The second method exploits the polynomial nature of POD models derived from input-affine high-dimensional systems with polynomial nonlinearities, in order to generate compact and efficient formulations that can be evaluated much faster. Moreover, in this study it is shown how the use of sequential feature selection algorithms can provide a significant boost in the computational saving. Although this method is not as general as the first one, it might be applied to models with non-polynomial nonlinearities, provided that the nonlinearities can be approximated by low degree polynomials. In addition, conditions for guaranteeing the local stability of these POD models with polynomial nonlinearities are discussed.

Promotors: Prof. B. De Moor, Prof. J. Espinosa (UNAL), Prof. J. Vandewalle

• study of partitioning methods for distributed and hierarchical control. Decomposition of a dynamical system in a number of weakly interacting sub-systems and representation of a dynamical system at different levels of abstraction for the design of decentralized/distributed or hierarchical control systems;
• design of distributed and/or hierarchical control structures for large scale systems, often composed by many interacting subsystems. Focusing on the design of model predictive control (MPC).
  

Exciting lecture by Prof Chua, Unversity of California, Berkeley, the inventor of the Memristor, the missing element in circuit theory:

Lecture: Auditorium of the Arenbergkasteel, Kasteelpark Arenberg, 1,
Katholieke Universiteit Leuven, 3001 Heverlee 

Title : "Memristors: From Proust to  HP"

Time: Wednesday August 12 2009 from 14u (till 16u)  (followed by a
coffee break in the salons)

Some background:
EVER had the feeling something is missing? If so, you're in good
company. Dmitri Mendeleev did in 1869 when he noticed four gaps in his
periodic table. They turned out to be the undiscovered elements
scandium, gallium, technetium and germanium. Paul Dirac did in 1929when he looked deep into the quantum-mechanical equation he had formulated to describe the electron. Besides the electron, he saw something else that looked rather like it, but different. It was only in 1932, when the electron's antimatter sibling, the positron, was sighted in cosmic rays that such a thing was found to exist. 

In 1971, Leon Chua had that feeling. A young electronics engineer with a penchant for mathematics at the University of California, Berkeley, he was fascinated by the fact that electronics had no rigorous mathematical foundation. So like any diligent scientist, he set about trying to derive one.
And he found something missing: a fourth basic circuit element besides the standard trio of resistor, capacitor and inductor. Chua dubbed it
the "memristor". The only problem was that as far as Chua or anyone else
could see, memristors did not actually exist. Except that they do. Within the past couple of years, memristors have morphed from obscure jargon into one of the hottest properties in physics. They've not only been made, but their unique capabilities might revolutionise consumer electronics. More than that, though, along with completing the jigsaw of electronics, they might solve the puzzle of how nature makes that most delicate and powerful of computers - the brain. That would be a fitting pay-off for a story which, in its beginnings, is a triumph of pure logic.

More information on the principle, relevance and the applications of
Memristors:

http://www.newscientist.com/article/dn13812-engineers-find-missing-link-of-electronics.html
http://www.newscientist.com/article/mg20327151.600-memristor-minds-the-future-of-artificial-intelligence.html?full=true
http://knol.google.com/k/anonymous/the-business-landscape-formemristor/23zgknsxnlchu/6#

(contact person for this event: Prof. Joos Vandewalle - K.U. Leuven ESAT-SCD)

This Stevin lecture is co-sponsored by ICCoS (Identification and Control of Complex Systems), a Scientific Research Network of the Research Foundation - Flanders (FWO-Vlaanderen).

This Stevin lecture is co-sponsored by ICCoS (Identification and Control of Complex Systems), a Scientific Research Network of the Research Foundation - Flanders (FWO-Vlaanderen).

• Didier Henrion (LAAS-CNRS, France and Czech Technical University in Prague, Czech Republic)
• Katja Mombaur (University of Heidelberg and LAAS-CNRS, Toulouse, France)
• Dominikus Noll (Universite Paul Sabatier, Toulouse, France)
• Michael Overton (New York University, USA)

This Stevin lecture is co-sponsored by ICCoS (Identification and Control of Complex Systems), a Scientific Research Network of the Research Foundation - Flanders (FWO-Vlaanderen).

• Prof.dr.ir. B. De Moor, promotor
• Prof.dr.ir. J.C. Willems, copromotor

• 9:00 - 9:50 Tube-based Robust Nonlinear Model Predictive Control - E. Kerrigan (Imperial College, UK)
• 9:50 - 10:40 Efficient Robust Model Predictive Control - M. Cannon (University of Oxford, UK)
• 10:40 - 11:10 Coffee Break
• 11:10 - 12:00 Invariant Polyhedral Sets for Output Feedback Control of Constrained Linear Systems - C. E. T. Dorea (UFBA, Brazil. K.U.Leuven guest until Feb 2009)
• 12:00 - 12:30 Robust Nonlinear Model Predictive Control for Power Generating Kites – B. Houska (K.U.Leuven)
• 12:30 - 14:00 Sandwich Lunch
• 14:00 - 14:30 Trajectory Based Suboptimality Estimates for Receding Horizon Controllers - J.Pannek (Universität Bayreuth, Germany )
• 14:30 - 15:20 Approximate Dynamic Programming for Robust Model Predictive Control - M. Diehl (K.U. Leuven)
• 15:20 - 15:50 Coffee break
• 15:50 – 16:40 Robust Optimization Methods for Predictive Control - P. Goulart (Imperial College, UK)

• 9:00 Welcome and Introduction
• 9:00-13:00   Deterministic MPC
•            9:00 - 10:00 MPC Problem Formulation - Mark
•            10:00 - 10:30 Coffee Break
•            10:30 - 11:30 Stability Analysis and Closed Loop Properties - Mark
•            11:45 - 12.45 Dynamic Programming - Moritz
• 13:00-14:00  Sandwich Lunch
• 14:00-17:00  Numerics and Robust MPC
•            14:00 - 15:00 Numerical Implementation - Paul
•            15:00 - 15:30 Coffee Break
•            15:30 - 16:30 Integral Action and Constant Disturbances - Eric
• 16:30 - 17:00 Discussion

Systems biology aims to discover how function arises from dynamic interactions in living systems. Its ultimate objective should be to explain how the components within a cell interact dynamically, and then how cells interact, resulting in the observed structure, organization and function. Systems biology offers unique opportunities for applying systems engineering methods, which are based on mathematical models. Correspondingly, new types of biological problems are motivating new research in theoretical systems engineering.

Optimization aims to make a system or design as effective or functional as possible. Mathematical optimization methods are widely used in engineering, economics and science. This talk will be focused on applications of mathematical optimization in computational systems biology. Examples are given illustrating the use of optimization methods in topics including optimal model building, optimality in biochemical metabolic networks, optimization of metabolic engineering and synthetic biology. Finally, several perspectives for future research are outlined.

This Stevin lecture is co-sponsored by ICCoS (Identification and Control of Complex Systems), a Scientific Research Network of the Research Foundation - Flanders (FWO-Vlaanderen).

Agenda

• 9:30 Introduction Joos Vandewalle and Moritz Diehl
•   10:00 WP 3 - Control System Design: Ilse Smets. Posters by  Diederik Verscheure, Bart Saerens, Boris Houska, Lieboud Van den Broeck, Goele Pipeleers.
•    10:30 WP 4 - Design optimization: Jan Swevers. Posters by Trent McConaghy, Filip Logist, Bram Demeulenaere
•   11:00 WP 1 - Conceptual and theoretical challenges: M. Diehl. Posters by Julian Bonilla, Mauricio Agudelo, Ion Necoara, Samuel Xavier-de-Souza, Michel Baes
•   11:30 WP 2 - System identification and approximation: J.  Suykens. Posters by Marcelo Espinoza, Marco Signoretto, Tillmann Falck, Vanya Van Belle, Fabian Ojeda, Peter Karsmakers, Friedl de Groote
•    12:00 WP 5 - Numerical algorithms and software: S. Vandewalle. Posters by Bart Vandereycken, Iurie Caraus, Joris Vanbiervliet, Carlos Alzate, Toon van Waterschoot
•    12:30 Findings of mini-SAB
•    13:00 end

• 10:30-12:00  Adminstrative Overview of OPTEC (room 00.147, who likes may join)
• 12:00-13:00  Sandwich Lunch in Hall (for all participants, reserve with Ida Tassens with subject SAB-LUNCH before Wed March 12)
• Plenary Poster Presentations (Auditorium)
  

• 13:00-13:25  "Robust Stability Optimization with the Smoothed Spectral Abscissa", Joris Vanbiervliet
• 13:25-13:50  "Optimal Performanc Trade-Offs in Repetitive Control", Goele Pipeleers
  
• 13:50-14:15  "Electric Load Forecasting: Using Kernel-Based Modeling for Nonlinear system Identification", Marcelo Espinoza
  

• 14:15-15:10  Poster Session No. 1 + coffee, GROUP PICTURE
• Plenary Poster Presentations (Auditorium)
  

• 15:10-15:35  "Positive Polynomial Constraints for POD-based Predictive Controllers", Michel Baes , with Mauricio Agudelo.
  
• 15:35-16:00  "Time-optimal path tracking for robots: a convex optimization approach", Diederik Verscheure.
  

• 16:00-17:00   Poster Session 2 + coffee
• 17:00-17:30   SAB internal discussion (room 00.147)
• 17:30-18:00   Communication of SAB Findings to OPTEC Professors and Postdocs (room 00.147)

The topics include:

•   modeling cell population dynamics (e.g. age-structured models),
•   parameter identification of distributed parameter systems (partial differential equations),
•   flow cytometry based cell turnover experimental studies.

This workshop will also present the results of the project 'Modeling and identification of distributed parameter systems arising in immunology' (Flemish-Russian bilateral scientific cooperation).

• Introduction to MPC
• Linear MPC (stability, different norms, .)
• Explicit approach
• Hybrid systems
• Software developed at ETHZ

• the maximal inscription of a gemstone into a rough stone to minimize the wasted material,
•  the computation of lower bounds for the volume of complicated container sets by inscription of balls, like in the manoeuvrability problem in robotics, and
•  the determination of “innermost points” of sets in order to stay away from their boundaries, like in quality control of production processes.
  

• 9:00 Welcome by M. Diehl
• 9:10-9:25 M. Wittmann, DLR: Revenue-optimal Scheduling of Solar  Thermal Power Plants with integrated Thermal Storage"
• 9:25-9:45 Fjo De Ridder, VITO: Controlling underground heat storage"
• 9:45-10:00 Coffee break
• 10:00-10:30 Discussion on solving optimal storage control problems
• 10:30-11:15 Prof. R. Pitz - Paal, DLR: Solar Power from the Deserts-Technologies, Markets, and Visions".
• 11:15-11:45  Prof. B. Hoffschmidt, FH Aachen: The Solar Tower Project in Juelich - A Milestone to Commercialisation of Solar Thermal Power Generation,"

"Distributed Constrained Optimal Control of Large-Scale Interconnected Systems"
Tamas Keviczky (T.U.Delft)

Large-scale engineering systems composed of interconnected components can be found in a broad spectrum of applications ranging from networks of independently actuated vehicles in robotics and formation flight to industrial process control and civil engineering. The presentation will focus on distributed control design methods for these systems using constrained optimal control techniques. Interest in such problems is usually motivated by the necessity of avoiding centralized design when this becomes computationally prohibitive or would require unrealistic expectations regarding information exchange. In particular, a distributed predictive control scheme will be described with a discussion on stability and feasibility related questions. Recent results in distributed design for a special class of large-scale systems using LQR controllers will also be summarized.

Applicability of the proposed framework will be demonstrated using the formation control problem of multiple Unmanned Air Vehicles (UAVs) as a motivating example. This particular application problem has a wide range of envisioned applications including distributed sensing and monitoring, which appear to be the most promising ones. The challenge in UAV formation flight is to formulate simple local problems and design individual controllers which would enable the UAVs to meet certain maneuvering challenges while maintaining relative positions and safe distances between each vehicle. This objective is achieved by developing distributed control laws for cooperative multi-vehicle groups using constrained optimal control theory and their demonstration on high-fidelity models of an actual unmanned hovering vehicle platform.

Fifth Simon Stevin Lecture on Optimization in Engineering

Program:

16:00 Simon Stevin Lecture on Optimization in Engineering by David Mayne
17:00 Short Coffee Break
17:15 OPTEC Christmas lecture by Guido Vanden Berghe
18:15 Reception in the salons of the Arenberg castle

Flyer ,   Poster

Simon Stevin Lecture on Optimization in Engineering

"Optimization: highways and byways"
David Mayne (Imperial College London)

Abstract:
I commenced my research in optimal control and optimization based design in 1959 so I have seen great changes in our subject over nearly 50 years.  In this talk, I outline some areas of research that I have been engaged in, some of which developed into major activities (the highways) and some of which remained dormant (byways). Both are interesting since even the byways may suggest fruitful areas for future research. The topics that I discuss include algorithms for optimal control (differential dynamic programming and strong variation algorithms), Monte Carlo procedures for stochastic control problems and filtering (particle filters), design problems involving infinite dimensional constraints such as the tuning problem in circuit design, and model predictive control. My aim is to present the relatively simple concepts that motivated research in these areas.

Bibliographical Information:
David Mayne received the B.Sc. and M.Sc degrees in Engineering from the University of the Witwatersrand, the Ph.D. and D.Sc degrees from the University of London, and the degree of Doctor of Technology, honoris causa, from the University of Lund, Sweden. He has held posts at the University of the Witwatersrand, the British Thomson Houston Company, University of California, Davis and Imperial College London where he is now Senior Research Fellow. He has been awarded Fellowships from the Royal Society, Royal Academy of Engineering, IEEE, IFAC, IEE and Imperial college. He has held numerous visiting professorship appointments. 

David Mayne left traces in nearly all areas of control. His research interests include optimization, optimization based design, nonlinear control, model predictive control, and adaptive control.

"Simon Stevin (1548-1620) Mathematician, physicist, engineer . . . , Uomo universale"
Guido Vanden Berghe (Universiteit Gent)
Guido.VandenBerghe@UGent.be

Abstract:
In this talk we shall give in first instance attention to the family and the life of Simon Stevin. Born in Bruges his grandparents were original wealthy inhabitants of Ypres and Veurne. In the second place we shall present a comprehensive picture of the activities and the creative heritage of Simon Stevin, who made outstanding contributions to various fields of science in particular, physics and mathematics and many more. Among the striking spectrum of his ingenious achievements, it is worth emphasizing, that Simon Stevin is rightly considered as the father of the system of decimal fractions as it is in use today. Stevin also urged the universal use of decimal fractions along with standardization in coinage, measures and weights. This was a most visionary proposal.
Stevin was the first since Archimedes to make a significant new contribution to statics and hydrostatics. His activities as an engineer will be discussed; in particular the construction of fortifications,windmills and the famous sailing chariot will be illustrated. He truly was an "uomo universale".

References
(1) J.T. Devreese en Guido Vanden Berghe, Wonder en is gheen wonder , De geniale wereld van Simon Stevin. 1548-1620, Davidsfonds, Leuven, 2003.
(2) J.T. Devreese and Guido Vanden Berghe, Magic is No Magic: The Wonderful World of Simon Stevin , WITpress Southampton (UK), 2007 .

About the Lecture Series:
The "Simon Stevin Lecture Series on Optimization in Engineering" is set up in order to promote optimization in engineering. For this aim, every quarter of the year an outstanding international scholar is invited to report on latest progress in the development of optimization algorithms and their applications in engineering.
Simon Stevin (1548-1620) was a Flemish mathematician and engineer. Among other, he helped to advance the use of decimal fractions, was the first to explain the tides by the attraction of the moon, and discovered the hydrostatic paradox. He made numerous inventions, among them a wind propelled carriage with sails, the "land yacht", which once impressed Prince Maurice of Orange as it moved faster than horses, in around 1600 on the beach between Scheveningen and Petten. Simon Stevin was fond of promoting the use of science in daily life and in craftmanship, and translated various mathematical terms into dutch. Among other, he introduced the dutch word for mathematics, "wiskunde".

Directly after this winter's Simon Stevin Lecture, a little reception will be given at 18:45 in the salons of Arenberg Castle, to which all attendants of the lecture are most warmly welcome!

***** REGISTRATION ENCOURAGED *****
Please send an e-mail with the subject "STEVIN" to Ida.Tassens@esat.kuleuven.be if you intend to participate in the event. No obligation, just to help us getting an idea how many people plan to come.

This Stevin lecture is co-sponsored by ICCoS (Identification and Control of Complex Systems), a Scientific Research Network of the Research Foundation - Flanders (FWO-Vlaanderen).

Convex Optimization 3 Day Mini Course
by Michel Baes

Date:  Dec 10, 12 and 18,
Time:  10:35-12:35,
Place: ESAT 00.91 (PC Pool)

 In this two session lecture with exercises, we deal exclusively with convex optimization  problems. We fist show how every convex optimization problem can be put into a so-called conic format, which enables us to use the powerful tools of duality theory to solve it. We show how several practical optimization problems can be formulated as instances of conic programming, and how we can solve most of them extremely efficiently using the software Sedumi. We will also describe briefly the main ideas of the primal-dual algorithm used in Sedumi. Several examples will be set up by the participants. 
The exercise-lectures are part of the master course on "Numerical 
Optimization" by Moritz Diehl but open to some extra participants "Convex conic programming"

"Duality Theory" (11/12/2007)

Duality originates from the very simple idea that one can obtain
useful information on an optimization problem practically for free by
combining its constraints. Yet, this simple idea has an impressive
number of consequences, in a theoretical level with a.o. the development
of the Lagrangean framework, and the foundations of an elegant
differentiability theory, in a practical level with a.o. the economic
interpretation of dual variables and the development of extremely
efficient primal-dual algorithm (this last topic will be sketched in the
next lecture).

"Convex conic programming" (12/12/2007)

In this lecture, we deal exclusively with convex optimization problems.
We first show how every convex optimization problem can be put in a
so-called conic format, which enables us to use the powerful tools of
duality theory to solve it. We show how several practical optimization
problems can be formulated as instances of conic programming, and how we
can solve most of them extremely efficiently using the software Sedumi.
We will also describe briefly the main ideas of the primal-dual
algorithm used in Sedumi.

Mysteries and Conundra in the
Use of Physical Dimensions

Physical dimensions are the glue that binds mathematics to physical reality. We use these quantities so routinely that we scarcely give them any thought. Nevertheless, there are a few aspects of physical dimensions that require careful attention and that are not fully settled. In this talk I will first discuss basic concepts relating to physical dimensions, including a proof of the cornerstone (and only) result in the field, namely, the Buckingham Pi theorem. Next, I will discuss various philosophical and technical subtleties that arise in the use of dimensions. For example:

Which approach is better: a) nondimensionalizing all physical quantities, b) working with dimensioned quantities directly, or c) ignoring dimensions completely (as in control theory)?

Why do dimensions matter when all real measurements are in volts?

Are physical dimensions relevant when we run a Matlab code with billions of operations on numbers?

If radians are dimensionless, why don’t we just ignore them completely?

Are all dimensionless quantities equivalent? Is there a difference between radian and Reynolds number?

Don’t energy and moment have the same dimensions, namely, length times distance? If so, what does it mean for a moment to do work?

Can a matrix with dimensioned entries have a determinant? Can it have eigenvalues and eigenvectors? If so, what are their dimensions?

Is it legal for control theorists to write V = xdot^2 + x^2?

Based on joint work with Harish Palanthandalam-Madapusi and Ravinder Venugopal.

This is a 5 day course within the Graduate school in Systems, Optimization, Control and Networks (SOCN)

NUMERICAL METHODS FOR NONLINEAR OPTIMAL CONTROL PROBLEMS

Dates : November 26, 29, and December 03, 06, 07, 2007
Schedule : 9h15 - 12 h45

More information on the course website

Announcement: SOCN website

Lecturer : M. DIEHL

The course develops numerical solution strategies for optimization problems with underlying differential equation models. After a brief overview over sequential and simultaneous approaches to optimal control, we focus on the latter class, which solve both the model equations and the optimality conditions in a "one-shot' approach. Here, exploitation of sparsity in the linear solvers is of crucial importance. Special topics include the treatment of distributed inequalities by interior point methods, periodic problems. Finally, we discuss real world applications from chemical and mechanical engineering.

Contents
1. Optimal Control: Introduction and Overview
2. Dynamic Programming and Indirect Approaches (Pontryagin)
3. Direct Transcription Methods (ODE Sensititivity, Single Shooting)
4. Direct Collocation, Treatment of Sparsity
5. Direct Multiple Shooting
6. Applications and Extensions: Multi-Stage Processes, Periodicity

Prerequisites
Participants are assumed to have a knowledge of linear algebra and calculus. Basic knowledge in optimisation and numerical simulation of dynamic systems is advantageous.

The course will take place at Esat in room 00.62, except on  26/11 it will be in room 02.58.

 REGISTER NOW FOR FREE! 
 

 Nov 22, 9:30-16:30, Brussels: DYSCO (Dynamical Systems, Control, and Optimization) Study Day :
http://www.inma.ucl.ac.be/DYSCO/StudyDays/2007/November22.htm

"Adaptive cubic overestimation for unconstrained optimization"
Philippe Toint

 http://www.fundp.ac.be/facultes/sciences/departements/mathematique/agenda/evenement.2007-11-06.8872619258

ATHENS course "Dynamic Process and System Optimization" November 19-23
teachers: Moritz Diehl, Bart Saerens, Dang Doan, Niels Haverbeke

Program and room information can be found on the course website.

"Some graph optimization problems in data mining"
Paul Van Dooren, UCL

Graph-theoretic ideas have become very useful in uderstanding modern large-scale datamining techniques. We show in this talk that ideas from optimization are also quite useful to better understand the numerical behaviour of the corresponding algorithms. We illustrate this claim by looking at two specific graph theoretic problems and their application in datamining.
The first problem is that of reputation systems where the reputation of objects and voters on the web are estimated; the second problem is that of estimating the similarity of nodes of large graphs. These two problems are also illustrated using concrete applications in datamining.

Slides

"Linear programs for optimal motion generation in linear dynamic systems: numerical issues"
Bram Demeulenaere (K.U.Leuven Mech-PMA)

Abstract:
Since 2005, a linear-programming based optimization framework has been developed at KUL/PMA to design dynamically optimal, polynomial splines for motion generation. While the framework was originally developed for rigid-body mechanical systems, it has recently been extended to flexible systems, described by linear ODEs subject to parametric uncertainty. For the latter class of systems, also so-called input shaping techniques have been considered, whereby it was shown in a 2006-2007 master thesis that input shapers can also be designed based on linear programming.

The present talk gives a short and high-level introduction to both the polynomial spline and input shaper optimization, and subsequently focuses on the main problems encountered so far, which are predominantly numerical in nature. The main idea of the talk is (i) to get some preliminary feedback of specialists in the field of spline optimization, linear algebra/programming and general scientific computing, and (ii) to investigate whether a collaboration concerning this topic can be set up.

Note:
Bram Demeulenaere is a member of OPTEC and an expert on the application of convex optimization techniques to mechanical systems.  He works in particular on counterweight balancing of linkages, spline-based cam design, dynamically compensated cams, and inverse dynamic musculo-skeletal simulation, including muscle physiology.

Slides

"Workshop on Nonlinear State Estimation"
November 8, 2007
Katholieke Universiteit Leuven

More information on the workshop website

Aim:

State and parameter estimation of nonlinear dynamic systems is an important prerequisite for their control and optimization. It is an integral part in such diverse applications as process monitoring, fault detection, process optimization, and model predictive control.

This OPTEC topical workshop will focus on the question of how to estimate the state of a dynamic system accurately and fast, given online measurements and a possibly nonlinear model. It is intended as a forum that shall bring young researchers from within the OPTEC environment together that work on nonlinear state estimation.
It is opened by a lecture by Johannes P. Schloeder from the IWR in Heidelberg, who reviews Numerical Methods for Parameter and State Estimation in Nonlinear Differential Equation Systems.

Confirmed speakers:

• Johannes Schloeder, IWR University of Heidelberg, Germany
• Friedl DeGroote, K.U.Leuven - PMA
• Tinne De Laet, K.U.Leuven - PMA
• Niels Haverbeke, K.U.Leuven - SCD
• Jeroen Boets, K.U.Leuven - SCD
• Wolfgang Mauntz, University of Dortmund, Germany
• Geert Gins, K.U.Leuven - BIOTEC

Conference website

 At this conference also Mike Powell will give a talk with the title "The development of algorithms for nonlinear optimization".

Mike Powell is (emeritus) Professor at Cambridge university and one of the most important and influential nonlinear optimizers worldwide.
He left traces in several fields of optimization, among other with the invention of the now widespread SQP algorithm 30 years ago, up to efficient derivative free nonlinear optimization algorithms in recent years.

"Energy Management in Conventional and Hybrid Vehicles as an Optimization Problem"
Mark Eifert

 Mechanical power is obtained through a combustion process in an engine, and a part of that power may be converted to electrical power with an electric machine. The electrical power may either be buffered in an energy storage device for future use or used immediately by electrical consumers or, in the case of a full or mild hybrid system, by an electric traction motor. The control of the generation of electrical power, the control of an electrical machine as a motor and the scheduling of electrical consumers are all objectives of an energy management strategy. Such strategies generally have the goals of maintaining enough stored electrical energy to guarantee the ability to start the vehicle and always provide requested electrical power and to lower fuel consumption and emissions. They are developed with system models, whose fidelity and accuracy indirectly determine the approach and complexity of the strategy itself. A number of different approaches are possible when designing an energy management strategy. They include predictive control, which requires a prediction of vehicle states over a time horizon, or game theory, which poses the optimal control problem as a game between the controller and the environment, where neither player knows what the other will do next. In my presentation, I would like to discuss the development of energy management strategies for conventional and hybrid vehicles. In it, I will touch on basic vehicular power supply and hybrid architectures, components and their modeling. I will then discuss the optimization problem and some strategy design approaches.

Slides

"Parametric Linear Complementarity Problems in Control"
Colin Jones (ETH, Zurich)

Abstract:
Constrained finite time optimal control problems can be expressed as
mathematical programs parameterized by the current state of the
system: the so-called multi-parametric programs. These problems have
received a great deal of attention in the control community during the
last few years because solving the parametric program is equivalent to
synthesizing the optimal state-feedback controller. For many cases of
interest, the resulting synthesized controllers are simple
piecewise-affine functions, which enables receding horizon control to
be used not only in slowly sampled systems requiring powerful
computers but now also in high-speed embedded applications running at
many kilohertz or megahertz.

In this talk, we introduce the parametric linear complementarity
problem (pLCP), which unifies and generalizes linear and quadratic
programs and bimatrix games. This problem allows the synthesis of
constrained receding horizon controllers for linear systems, based on
the optimization of quadratic or piecewise-linear costs. We also find
that many fundamental algorithms of computational geometry that are of
interest in various areas of constrained linear control can be posed
as an equivalent convex parametric LCP.

We present a new computational method for solving this important class
of problems. The method is shown to be polynomial time in the output
size when the problem is in general position. Furthermore, we describe
how the symbolic technique of lexicographic perturbation can be
applied to simulate general position and thus extend the algorithm to all
convex degenerate LCPs.

Slides

Fourth Simon Stevin Lecture on Optimization in Engineering

"Control of Hybrid Systems: Theory, Computation and Applications"
Manfred Morari (ETH Zurich)

Abstract:    slides      poster

Theory, computation and applications define the evolution of the field of control. This premise is illustrated with the emerging area of hybrid systems, which can be viewed, loosely speaking, as dynamical systems with switches. Many practical problems can be formulated in the hybrid system framework. Power electronics are hybrid systems by their very nature, systems with hard bounds and/or friction can be described in this manner and problems from other domains, as diverse as driver assistance systems, anesthesia and active vibration control can be put in this form.

I will describe the theoretical basis of some of the tools that have been proposed to synthesize the controllers for hybrid systems. Parametric programming has received a lot of attention in the control literature in the past few years because model predictive controllers (MPC) can be posed in a parametric framework and hence pre-solved offline, resulting in a significant decrease in on-line computation effort. I will describe recent work on parametric linear programming (pLP) from the point of view of the control engineer. I will survey various types of algorithms, and identify a new standard convex hull approach that offers significant potential for approximation of pLPs for the purpose of control. The resulting algorithm, based on the beneath/beyond paradigm, computes low-complexity approximate controllers that guarantee stability and feasibility.

Many industrial applications will serve to highlight the theoretical developments and the extensive software that helps to bring the theory to bear on the practical examples.

 joint work with Colin Jones, Miroslav Baric and Melanie Zeilinger.

Bibliographical Information:
Manfred Morari was appointed head of the Automatic Control Laboratory at ETH Zurich in 1994. Before that he was the McCollum-Corcoran Professor of Chemical Engineering and Executive Officer for Control and Dynamical Systems at the California Institute of Technology. He obtained the diploma from ETH Zurich and the Ph.D. from the University of Minnesota, both in chemical engineering. His interests are in hybrid systems and the control of biomedical systems. In recognition of his research contributions, he received numerous awards, among them the Donald P. Eckman Award and the John Ragazzini Award of the Automatic Control Council, the Allan P. Colburn Award and the Professional Progress Award of the AIChE, the Curtis W. McGraw Research Award of the ASEE, Doctor Honoris Causa from Babes-Bolyai University, Fellow of IEEE, the IEEE Control Systems (Technical Field) Award, and was elected to the National Academy of Engineering (U.S.). Professor Morari has held appointments with Exxon and ICI plc and serves on the technical advisory board of several major corporations.

About the Lecture Series:
The "Simon Stevin Lecture Series on Optimization in Engineering" is set up in order to promote optimization in engineering. For this aim, every quarter of the year an outstanding international scholar is invited to report on latest progress in the development of optimization algorithms and their applications in engineering.
Simon Stevin (1548-1620) was a Flemish mathematician and engineer. Among other, he helped to advance the use of decimal fractions, was the first to explain the tides by the attraction of the moon, and discovered the hydrostatic paradox. He made numerous inventions, among them a wind propelled carriage with sails, the "land yacht", which once impressed Prince Maurice of Orange as it moved faster than horses, in around 1600 on the beach between Scheveningen and Petten. Simon Stevin was fond of promoting the use of science in daily life and in craftmanship, and translated various mathematical terms into dutch. Among other, he introduced the dutch word for mathematics, "wiskunde".

Directly after this autumn's Simon Stevin Lecture, a little reception will be given at 18:00 in the salons of Arenberg Castle, to which all attendants of the lecture are most warmly welcome!

***** REGISTRATION ENCOURAGED *****
Please send an e-mail with the subject "STEVIN" to Ida.Tassens@esat.kuleuven.be if you intend to participate in the event. No obligation, just to help us getting an idea how many people plan to come.

This Stevin lecture is co-sponsored by ICCoS (Identification and Control of Complex Systems), a Scientific Research Network of the Research Foundation - Flanders (FWO-Vlaanderen).

"MPC for Diesel engine air path control"
Peter Ortner, Roland Bergmann

"Fast implementation of MPC using Set Membership approximation methodologies"
Lorenzo Fagiano (Politecnico di Torino)

Model Predictive Control (MPC) (see e.g. the survey [1]) is a model based control technique where the control action is computed by solving at each sampling time an optimization problem which uses the current system state as the initial condition. In general the control move ut at time t, for time invariant systems, is a nonlinear static function of the system state x_t, i.e. u_t = f(x_t). One of the main limitations in using MPC techniques is the presence of fast plant dynamics which require small sampling periods that do not allow to perform the optimization problem online: this motivates the research efforts devoted to develop computationally tractable MPC solutions, or suitable approximations of MPC control laws, by exploiting the properties of f(x). These approaches include explicit piece-wise linear solutions ([2]), neural approximations ([3]) and piece-wise linear approximations ([4]) of f(x). An alternative approach is presented here using Set Membership (SM) methodologies for nonlinear function estimation as firstly introduced in [5]. Under this context, no assumption on the functional form of f(x) is made and only assumptions on its regularity properties, given by bounds on its value set and on its gradient, are considered. The use of such methodology enables to compute an approximation of a given predictive control law which guarantees to fulfill input constraints. Moreover, it is possible to compute an upper bound of the approximation error which holds for every state value in the considered set. In particular, a key feature of this approach is the possibility of tuning the approximation error in order to guarantee satisfaction of state constraints. This way, as the control computation is simply reduced to the evaluation of a static nonlinear function, the computational time is significantly reduced leading to a “Fast” Model Predictive Control implementation (FMPC). Two different SM methodologies are presented for the approximation of the nominal MPC controller. Both methodologies are based on the off-line computation of a certain number N of exact MPC control moves. The first method derives an “optimal” approximating function which minimizes, for a given N, the guaranteed accuracy level. However, its computational time grows with N. The second one gives lower guaranteed accuracy for a given N, but its computational time is lower and it is approximately constant with N. Thus, at the cost of increasing the number of off-line computations, any desired level of guaranteed accuracy can be obtained without increasing the on-line computation effort. Two numerical examples are given to show the effectiveness of the presented results.

REFERENCES
[1] D. Q. Mayne, J. B. Rawlings, C. V. Rao and P.O.M. Scokaert,
“Constrained model predictive control: Stability and optimality”,
Automatica, 36, 789–814, 2000.
[2] A. Bemporad, M. Morari, V. Dua and E.N. Pistikopoulos “The explicit
linear quadratic regulator for constrained systems”, Automatica
38, 3–20, 2002.
[3] T. Parisini and R. Zoppoli, “A receding-horizon regulator for
nonlinear systems and a neural approximation”. Automatica 31(10), 1443–
1451, 1995.
[4] T. A. Johansen, “Approximate explicit receding horizon control of
constrained nonlinear systems”. Automatica 40, 293–300, 2004.
[5] M. Canale and M. Milanese, “FMPC: a fast implementation of model
predictive control”, in 16th IFAC World Congress, Prague, Czech
Republic, July 2005.

Slides

Jury:

Prof. dr. ir. J. Berlamont, chairman
Prof. dr. ir. B. De Moor, promoter
Dr. ir. J. Dehaene, copromoter
Prof. dr. M. Fannes
Prof. dr. ir. J. Vandewalle
Prof. dr. ir. F. Verstraete (Universität Wien)
Prof. dr. ir. K. Audenaert (University of London)

"OPTEX: Workshop on Optimal Experimental design in engineering"
October 8-9, 2007
Katholieke Universiteit Leuven

More information on the workshop website.

Aim:
This workshop wants to bring researchers with interests in optimal experimental design (OED) together, that work in various fields of engineering including chemical, biochemical, and mechanical engineering. The workshop is organized in two days.

The first day is a tutorial day introducing the principles of optimal experimental design for nonlinear model identification using a series of examples and software exercises (guest teacher: Stefan Koerkel, Humboldt University Berlin). This day is an excellent opportunity to get acquainted with OED for newcomers, and to learn how to use state-of-the-art software for nonlinear parameter estimation and OED.

The second day will be a scientific day. Several invited speakers will present their work on OED, and there is the possibility to present your own work in form of a talk (limited places) or a poster. In case you are interested, please submit a title and short abstract (plain text, max. 250 words) to Christian Hoffmann.

Confirmed international speakers:

• Stefan Körkel, Institut of Mathematics, Humboldt University Berlin
  
• Hergen Schultze, BASF AG Ludwigshafen, Germany 
  
• Samuel Bandara, Chemical and Systems Biology, Stanford University, USA.

“Optimal Control of Mixing”

Ian Couchman (Imperial College London, UK)
E.C. Kerrigan, J.C. Vassilicos

Motivated by the problem of laminar fluid mixing, this presentation will consider the problem of optimal control of advective mixing in a 2D Navier-Stokesfluid flow. The type of flow that is studied here was first introduced in [1]; a charge-carrying flow is induced by a spatially distributed force field, the strength of which can be scaled arbitrarily with a time-varying current. Though the flow is laminar, it has turbulent-like statistics and is well suited to mixing [1].

The control objective that will be considered in this presentation, is how to compute the current profile that will maximize the mixing in the flow for a given amount of input energy. The contribution of this presentation is two-fold:

1. The degree of mixing of a flow will be quantified by considering a number of different cost functions. A new measure, based on the idea that mixing can be quantified by the frequency with which parts of the flow come together, will be introduced and justified.

2. The optimal control problem, which contains a dynamic model for the evolution of the degree of mixing of the fluid, will be defined in detail.

The presentation will conclude with specific research questions regarding the efficiency and reliability of the numerical solution of the optimal control problem.

References

[1] L. Rossi, J.C. Vassilicos, and Y. Hardalupas. Electromagnetically controlled

multi-scale flows. J. Fluid Mech, 2006.

Slides

Lectures on "Numerical Optimization" given by Moritz Diehl taking place first on Sept 26 (until Dec 11.)
every tuesday (room Celestijnenlaan 200 C 01.05, Aud. C) and
wednesday from 10:35-12:35(room Celestijnenlaan 300 - 00.81, Aud. D).

Announcement

 Contents of the Course

Part I: Introduction
1.   Fundamental Concepts of Optimization
2.   Types of Optimization Problems

Part II:  Unconstrained Optimization
3.   Optimality Conditions and Convexity
4.   Iterative Descent Methods
5.   Newton Type Optimization Methods
6.   Calculating Derivatives
7.   Estimation and Fitting Problems
8.   Trust Region vs. Line Search Methods

Part III: Constrained Optimization
9.   Karush-Kuhn-Tucker Optimality Conditions
10. Quadratic Programming
11. Sequential Quadratic Programming
12. Globalisation Strategies
13. Interior Point Methods
14. Optimal Control Problems
15. Summary of the Lecture
16. Duality
17. Special Topics in Convex Optimization

The course is partly based on the book "Numerical Optimization" by
J. Nocedal and S. Wright, Springer Verlag.

Lecture:         Moritz Diehl,
Exercises:       Hans-Joachim Ferreau
Special Topics:  Michel Baes,

 Please register by sending an email with the subject NUMOPT
to Ida Tassens

“Interplanetary Spacecraft Trajectory Optimization”

Marnix Volckaert (T.U.Delft)

In order to learn more about the origin of our solar system, it is crucial to send spacecraft to other planets and their moons. Furthermore, the possibility of sending humans to another planet, such as Mars, requires the technology to transfer a spacecraft from the Earth to the other planet. Such a complex undertaking can only be achieved if the trajectory followed by the spacecraft is highly optimized, for example in terms of the required propellant mass.

Finding the trajectory that uses the least amount of propellant is difficult. The search space is quasi unlimited, and there are multiple local optima. The motion of the spacecraft is described by complex astrodynamical concepts, that can be used to the advantage, for example during a gravity assist maneuver.

Trajectory optimization techniques can be divided into analytical methods and numerical methods. The analytical methods are generally much faster but can be inaccurate because of their simplifying assumptions. The numerical methods can be more accurate, but are usually slower, because they rely on numerical integration of the spacecraft's state.

In this presentation an overview will be given of different trajectory optimization methods, both analytical and numerical, including their application to real-life and concept missions.

"A worst-case-estimate for the method of conjugate gradients"
Thomas Davi (University of Duesseldorf)

The cg-algorithm is used for quadratic functions whose appropriate symmetric nxn matrix is positive definite. This demand ensures that the minimum of the function is found within n steps (theoretically - because of rounding errors in general n steps are not enough in practice). For a given dimension n and a condition number K>1 we deal with the maximum possible distance between the n-1. iterate of the cg-algorithm and the minimum of any quadratic function whose symmetric postive definite matrix has the condition number K. To measure the distance we construct a function F which contains n-1steps of the cg-algorithm. The input of F is a quadratic function and a starting vector. We analyze the function F and try to find candidates for the maximum of it by a trust-region-method.

“Real-time optimization in automotive control applications”

Ralf Beck, Dirk Abel (IRT, Aachen)

The need for more fuel efficient vehicles is a key driver for innovations in automotive engineering. This goal gives rise to sophisticated combustion engine concepts like Controlled Auto Ignition (CAI) and driveline architectures like Hybrid Electric Vehicles (HEV) which in turn, due to their inherent complexity, require advanced control strategies. This favors the introduction of optimization-based control schemes like Model Predictive Control to these fast applications, which is addressed by this talk.

Besides the control of driveline components, the overall drivetrain energy management gains importance in order to maximize the vehicle’s efficiency and lower th fuel consumption. This naturally leads to an optimization problem. The talk will exemplify this topic by the energy management of hybrid electric vehicles.

Slides

 "Optimization in the Modeling and Control of Walking Robots "
Pierre-Brice Wieber (INRIA, Grenoble)

The modeling of walking articulated systems introduces two  fundamental questions that deal with the unilateral contact between  the feet of the system and the ground. The first question is about  the global behavior of the system that results from this unilateral  contact, its general stability. The second question focuses more  specifically on the changes in the support phases and their general  analysis. These two topics raise many serious theoretical  mathematical modeling problems, and they deeply impact the control  laws that we would like to implement on these systems to make them  realize stable and robust walking movements. Far from the very  impressive demos of humanoid robots that have been proposed by  japanese industrials during the last years, these are in fact serious  problems to which no satisfying answers exist today. We will propose  in this talk a quick tour of all these questions and some of the  possible answers, with a special emphasis on where, why and how the  tools from the theory of Optimization are a major help.

Slides