LA09 Abstracts - LA09 Abstracts 39 inner product spaces. Christian Mehl Technische Universitaet Berlin

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  • 38 LA09 Abstracts


    Preconditioning for PDE-constrained Optimization

    For many partial differential equations (PDEs) and sys- tems of PDEs efficient preconditioning strategies now exist which enable effective iterative solution of the linear or linearised equations which result from discretization. Few would now argue for the use of Krylov subspace methods for most such PDE problems without effective precondi- tioners. There are many situations, however, where it is not the solution of a PDE which is actually required, but rather the PDE expresses a physical constraint in an Opti- mization problem: a typical example would be in the con- text of the design of aerodynamic structures where PDEs describe the external flow around a body whilst the goal is to select its shape to minimize drag. We will briefly de- scribe the mathematical structure of such PDE-constrained Optimization problems and show how they lead to large scale saddle-point systems. The main focus of the talk will be to present new approaches to preconditioning for such Optimization problems. We will show numerical results in particular for problems where the PDE constraints are provided by the Stokes equations for incompressible viscous flow.

    Andy Wathen Oxford University Computing Laboratory


    Exploiting Sparsity in Matrix Functions, with Ap- plications to Electronic Structure Calculations

    In this talk, some general results on analytic decay bounds for the off-diagonal entries of functions of large, sparse (in particular, banded) Hermitian matrices will be pre- sented. These bounds will be specialized to various ap- proximations to density matrices arising in Hartree-Fock and Density Functional Theory (assuming localized basis functions are used), proving exponential decay (i.e., ‘near- sightedness’) for gapped systems at zero electronic tem- perature and thus providing a rigorous justification for the possibility of linear scaling methods in electronic structure calculations for non-metallic systems. The case of density matrices for disordered systems (Anderson model) and for metallic systems at finite temperature will also be consid- ered. Additional applications, possible extensions to the non-Hermitian case, and some open problems will conclude the talk. This is joint work with Paola Boito and Nader Razouk.

    Michele Benzi

    DepartmentõfM̃athematics and Computer Science Emory University


    Matrix Factoriazations and Total Positivity

    The concept of a totally positive matrix grew out of three separate applications: Vibrating systems, interpolation, and probability. Since them, this class of matrices con- tinues to garner interest from both applied and theoretical mathematicians. In 1953, a short paper appeared by Anne Whitney in which she established a ”reduction” result that may be one of the most powerful results on this topic. My plan for this presentation is to survey the relevant history from Anne’s paper to present day, and to discus the result-

    ing impact of her reduction result. Particular attention will be paid to bidiagonal factorizations of totally positive matrices, along with a number of related applications.

    Shaun M. Fallat University of Regina Canada


    Model Reduction for Uncertainty Quantification and Optimization of Large-scale Systems

    For many engineering systems, decision-making under uncertainty is an essential component of achieving im- proved system performance; however, uncertainty quan- tification and stochastic optimization approaches are gen- erally computationally intractable for large-scale systems, such as those resulting from discretization of partial dif- ferential equations. This talk presents recent advances in model reduction methods — which aim to generate low- dimensional, efficient models that retain predictive fidelity of high-resolution simulations — that make possible the characterization, quantification and control of uncertainty in the large-scale setting.

    Karen Willcox Massachusetts Institute of Technology


    Recent Advances in the Numerical Solution of Quadratic Eigenvalue Problems

    A wide variety of applications require the solution of a quadratic eigenvalue problem (QEP). The most common way of solving the QEP is to convert it into a linear problem of twice the dimension and solve the linear problem by the QZ algorithm or a Krylov method. Much work has been done to understand the linearization process and the effects of scaling. Structure preserving linearizations have been studied, along with algorithms that preserve the spectral properties in finite precision arithmetic. We present a new class of transformations that preserves the block structure of widely used linearizations and hence does not require computations with matrices of twice the original size. We show how to use these structure preserving transformations to decouple symmetric and nonsymmetric quadratic matrix polynomials.

    Francoise Tisseur University of Manchester Department of Mathematics


    Definite versus Indefinite Linear Algebra

    Matrices with symmetries with respect to indefinite inner products arise in many applications, e.g., Hamiltonian and symplectic matrices in control theory and/or the theory of algebraic Riccati equations. In this talk, we give an overview on latest developments in the theory of matrices in indefinite inner product spaces and focus on the sim- ilarities and differences to the theory of Euclidean inner product spaces. Topics include normality, polar decom- positions, and singular value decompositions in indefinite

  • LA09 Abstracts 39

    inner product spaces.

    Christian Mehl Technische Universitaet Berlin Institut für Mathematik


    Beyond the Link-Graph: Analyzing the Content of Pages to Better Understand their Meaning

    In recent years the Web link graph has attracted much at- tention. However once we start analyzing the content Web pages many other fascinating graphs reveal themselves. In my talk I will discuss some of the information structures that we can construct from different Web content analysis sources, such as: anchor text, query and click logs, social tagging, natural language processing of text, and user pro- vided semantic data.

    Hugo Zaragoza Yahoo! Research Barcelona


    Numerical Linear Algebra, Data Mining and Image Processing

    In this talk, I will present two applications in data mining and image processing. The first application is related to the problem where an instance can be assigned to multi- ple classes. For example, in text categorization tasks, each document may be assigned to multiple predefined topics, such as sports and entertainment; in automatic image or video annotation tasks, each image or video may belong to several semantic classes, such as urban, building, road, etc; in bioinformatics, each gene may be associated with a set of functional classes. The second application is related to multiple class image segmentation problem. For ex- ample, foreground-background segmentation has wide ap- plications in computer vision (scene analysis), computer graphics (image editing) and medical imaging (organ seg- mentation). Both applications share the concept of semi- supervised learning and involve several numerical linear al- gebra issues. Finally, I will present some matrix compu- tation methods for solving these numerical issues. Exper- imental results are also given to demonstrate the applica- tions and computation methods.

    Michael Ng Hong Kong Baptist University Department of Mathematics


    Confidence and Misplaced Confidence in Image Re- construction

    Forming the image from a CAT scan and taking the blur out of vacation pictures are problems that are ill-posed. By definition, small changes in the data to an ill-posed problem make arbitrarily large changes in the solution. How can we hope to solve such problems using noisy data and inexact computer arithmetic? In this talk we discuss the use of side conditions and bias constraints to improve the quality of solutions. We discuss their impact on solution algorithms and show their effect on our confidence in the results.

    Dianne P. O’Leary

    University of Maryland, College Park Department of Computer Science


    Sparse Optimization: Algorithms and Applications

    Many signal- and image-processing applications seek sparse solutions to large underdetermined linear systems. The basis-pursuit approach minimizes the 1-norm of the solu- tion, and the basis-pursuit denoising approach balances it against the least-squares fit. The resulting problems can be recast as conventional linear and quadratic pro- grams. Useful generalizations of 1-norm regularization, however, involve optimization over matrices, and often lead to considerably more difficult problems. Two recent exam- ples: problems with multiple right-hand sides require joint- sparsity patterns among the solutions and involve mini- mizing the sum of row norms; matrix-completion problems require low-rank matrix approximations, and involve min- imizing the sum of singular values. I will survey some of the most successful and promising approaches for sparse optimization, and will show examples from a variety of ap- plications.

    Michael P. Friedlander Dept of Computer Science University of British Columbia


    Using Combinatorics to Solve Systems in M- matrices in Nearly-linear Time

    Symmetric M-matrices arise in areas as diverse as Sci- entific Computin