Linear Algebra, Rational Approximation and Orthogonal Polynomials
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Contents:
Journal of Mathematical Analysis and Applications, Mason, J.
Adhemar Bultheel
Olshevsky, V. Numerical Linear Algebra with Applications, Wu, H. User Username Password Remember me. Journal Help. Notifications View Subscribe.
Open Journal Systems. It is known to be important to solve a large scale of problems in numerical analysis, linear system theory, stochas Du kanske gillar. Ladda ned.
Spara som favorit. Moreover, the aim of this session is to provide an opportunity for researchers to discuss recent progress in the field. Abstract: Spectral methods are a class of numerical techniques used to solve differential and integral equations by expressing the solution as an expansion in a global basis such as trigonometric or orthogonal polynomials.
The speakers in this minisymposium will present recent developments in fast transforms, infinite-dimensional linear algebra, multivariate orthogonal polynomials, non-local problems, rational approximation and domain decomposition methods that are allowing spectral methods to tackle problems previously viewed as outside their reach. Abstract: Wave propagation appears in many physics, engineering and military applications. The study of wave propagation often requires solving partial differential equations in unbounded domains, on which the computational cost is very large.
Purchase Linear Algebra, Rational Approximation and Orthogonal Polynomials, Volume 6 - 1st Edition. Print Book & E-Book. ISBN In the related areas of linear algebra, orthogonal. polynomials and Pad e approximation, these block cases are underdeveloped. to almost nonexisting at all.
Various techniques have been developed to reduce the computation to a boundary domain, including artificial boundary conditions, boundary integral equations, and perfectly matched layers. This mini-symposium aims at bringing both leading experts and young researchers working on these problems, to discuss the latest results and to exchange new ideas, approaches, possible applications and emerging computational techniques.
Generalizations of orthogonal polynomials ⋆ (2003)
Abstract: Model-driven optimization and control is at the core of a myriad of modern scientific and societal challenges including the control and estimation of collective behaviour phenomena, the design of protocols for autonomous vehicles, and the study of data transmission over communication networks, to name a few. Furthermore, in an applicative framework a fundamental step to study real problems is represented by the introduction of stochastic parameters reflecting the natural lack of information due to a wide range of phenomena spanning form possible external actions to behavioral forces.
Therefore, the quantification of the influence of uncertainties is of paramount importance to build more realistic models and to design efficient automation strategies. This minisymposium addresses the analysis of novel computational techniques for robust control synthesis in the presence of parametric and structural uncertainties. It aims at promoting interactions between researchers working on computational methods for Hamilton-Jacobi and Fokker-Planck type equations, multiscale interacting particle systems and kinetic equations, PDE-constrained optimization, and applications in collective dynamics.
Skip to main content. Join our mailing list. Symmetric Functions and Orthogonal Polynomials. Institutional Subscription. The following distinguished researchers have accepted invitations to deliver plenary lectures at the conference:. Ismail and James A.
Abstract: We discuss recent advances in the numerical approximation of problems in fluid mechanics. The emphasis will be on finite element methods for Stokes and Navier-Stokes equations, but contributions in related areas such as discontinuous Galerkin, virtual elements or finite volume methods will also be welcome. In addition, contributions related to adaptive methods for one or several of the above topics will also be welcome.
Abstract: There is a strong relationship between network science and linear algebra, as complex networks can be represented and manipulated using matrices. Some popular tasks in network science, such as ranking nodes, identifying hidden structures, or classifying and labelling components in networks, can be tackled by exploiting the matrix representation of the data.
In this minisymposium we sample some recent contributions that build on an algebraic representation of standard and higher-order networks to design models and algorithms to address a diverse range of network problems, including but not limited to core-periphery detection and centrality.
Abstract: Optimisation problems over continuous variables are ubiquitous across quantitative disciplines, and the development of efficient numerical methods to solve such problems is an important topic in numerical analysis. This minisymposium will present recent work on a diverse array of continuous optimisation problems, including linear and nonlinear programming, and global and stochastic optimisation. Abstract: The theoretical and algorithmic aspects of rational approximation have experienced a surge of interest in recent years, motivated in part by applications of rational approximation in numerical linear algebra.
This minisymposium aims to bring together researchers working on these diverse aspects of rational approximation. Abstract: The mini-symposium focuses of the fundamental challenges of understanding uncertainties in numerical analysis. Addressing these challenges requires synergies across mathematical disciplines including statistical sciences as well as traditional numerical analysis.
Linear Algebra, Rational Approximation and Orthogonal Polynomials
The mini-symposium aims at presenting possible approaches to form this synergy by discussing, motivating, and highlighting approaches enabling the coherent propagation of probability measures through the numerical computation and inference. Recognizing a potential possibility of statistical view on the data employed in numerical schemes gives rise to a new understanding of the notion of dimensionality and important properties it entails, in particular in the case when dimension of the vector space to which the original data belongs is high.
Revealed intra-disciplinary connections between traditional and more recent approaches in numerical analysis enables linking of vast relevant accumulated expertise in numerical analysis to the vibrant community of Machine Learning and Artificial Intelligence. Abstract: Real life problems require expensive approximations and when such approximations are combined with other numerical methods the cost can become prohibitive.
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