Polynomial Approximation (polynomial + approximation)

Distribution by Scientific Domains


Selected Abstracts


Solving, Estimating, and Selecting Nonlinear Dynamic Models Without the Curse of Dimensionality

ECONOMETRICA, Issue 2 2010
Viktor Winschel
We present a comprehensive framework for Bayesian estimation of structural nonlinear dynamic economic models on sparse grids to overcome the curse of dimensionality for approximations. We apply sparse grids to a global polynomial approximation of the model solution, to the quadrature of integrals arising as rational expectations, and to three new nonlinear state space filters which speed up the sequential importance resampling particle filter. The posterior of the structural parameters is estimated by a new Metropolis,Hastings algorithm with mixing parallel sequences. The parallel extension improves the global maximization property of the algorithm, simplifies the parameterization for an appropriate acceptance ratio, and allows a simple implementation of the estimation on parallel computers. Finally, we provide all algorithms in the open source software JBendge for the solution and estimation of a general class of models. [source]


Comparative study of the least squares approximation of the modified Bessel function

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN BIOMEDICAL ENGINEERING, Issue 8 2008
Jianguo XinArticle first published online: 14 DEC 200
Abstract The least squares problem of the modified Bessel function of the second kind has been considered in this study with the Fourier series, Tchebycheff and Legendre approximation. Numerical evidence shows that the Gibbs phenomenon exists in the approximation with the truncated Fourier series, thus, giving poor convergence results compared with the other polynomial bases. For the latter two cases, the Legendre series perform better than Tchebycheff series in terms of the ,2 norm of the relative errors for each order of the polynomial approximation, and the ratio of the ,2 norm of the relative errors from the corresponding approximation seems to be a constant value of 1.3. Copyright © 2006 John Wiley & Sons, Ltd. [source]


Stability of linear time-periodic delay-differential equations via Chebyshev polynomials

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 7 2004
Eric A. Butcher
Abstract This paper presents a new technique for studying the stability properties of dynamic systems modeled by delay-differential equations (DDEs) with time-periodic parameters. By employing a shifted Chebyshev polynomial approximation in each time interval with length equal to the delay and parametric excitation period, the dynamic system can be reduced to a set of linear difference equations for the Chebyshev expansion coefficients of the state vector in the previous and current intervals. This defines a linear map which is the ,infinite-dimensional Floquet transition matrix U'. Two different formulas for the computation of the approximate U, whose size is determined by the number of polynomials employed, are given. The first one uses the direct integral form of the original system in state space form while the second uses a convolution integral (variation of parameters) formulation. Additionally, a variation on the former method for direct application to second-order systems is also shown. An error analysis is presented which allows the number of polynomials employed in the approximation to be selected in advance for a desired tolerance. An extension of the method to the case where the delay and parametric periods are commensurate is also shown. Stability charts are produced for several examples of time-periodic DDEs, including the delayed Mathieu equation and a model for regenerative chatter in impedance-modulated turning. The results indicate that this method is an effective way to study the stability of time-periodic DDEs. Copyright © 2004 John Wiley & Sons, Ltd. [source]


Spatially adaptive color filter array interpolation for noiseless and noisy data

INTERNATIONAL JOURNAL OF IMAGING SYSTEMS AND TECHNOLOGY, Issue 3 2007
Dmitriy Paliy
Abstract Conventional single-chip digital cameras use color filter arrays (CFA) to sample different spectral components. Demosaicing algorithms interpolate these data to complete red, green, and blue values for each image pixel, to produce an RGB image. In this article, we propose a novel demosaicing algorithm for the Bayer CFA. For the algorithm design, we assume that, following the concept proposed in (Zhang and Wu, IEEE Trans Image Process 14 (2005), 2167,2178), the initial interpolation estimates of color channels contain two additive components: the true values of color intensities and the errors that are considered as an additive noise. A specially designed signal-adaptive filter is used to remove this so-called demosaicing noise. This filter is based on the local polynomial approximation (LPA) and the paradigm of the intersection of confidence intervals applied to select varying scales of LPA. This technique is nonlinear and spatially-adaptive with respect to the smoothness and irregularities of the image. The presented CFA interpolation (CFAI) technique takes significant advantage from assuming that the original data is noise-free. Nevertheless, in many applications, the observed data is noisy, where the noise is treated as an important intrinsic degradation of the data. We develop an adaptation of the proposed CFAI for noisy data, integrating the denoising and CFAI into a single procedure. It is assumed that the data is given according to the Bayer pattern and corrupted by signal-dependant noise common for charge-coupled device and complementary-symmetry/metal-oxide semiconductor sensors. The efficiency of the proposed approach is demonstrated by experimental results with simulated and real data. © 2007 Wiley Periodicals, Inc. Int J Imaging Syst Technol, 17, 105,122, 2007 [source]


Nonlinear functionals of the periodogram

JOURNAL OF TIME SERIES ANALYSIS, Issue 5 2002
GILLES FAY
A central limit theorem is stated for a wide class of triangular arrays of nonlinear functionals of the periodogram of a stationary linear sequence. Those functionals may be singular and not-bounded. The proof of this result is based on Bartlett decomposition and an existing counterpart result for the periodogram of an independent and identically distributed sequence, here taken to be the driving noise. The main contribution of this paper is to prove the asymptotic negligibility of the remainder term from Bartlett decomposition, feasible under short dependence assumption. As it is highlighted by applications (to estimation of nonlinear functionals of the spectral density, robust spectral estimation, local polynomial approximation and log-periodogram regression), this extends may results until then tied to Gaussian assumption. [source]


Analysis and implementation issues for the numerical approximation of parabolic equations with random coefficients

INTERNATIONAL JOURNAL FOR NUMERICAL METHODS IN ENGINEERING, Issue 6-7 2009
F. Nobile
Abstract We consider the problem of numerically approximating statistical moments of the solution of a time-dependent linear parabolic partial differential equation (PDE), whose coefficients and/or forcing terms are spatially correlated random fields. The stochastic coefficients of the PDE are approximated by truncated Karhunen,Loève expansions driven by a finite number of uncorrelated random variables. After approximating the stochastic coefficients, the original stochastic PDE turns into a new deterministic parametric PDE of the same type, the dimension of the parameter set being equal to the number of random variables introduced. After proving that the solution of the parametric PDE problem is analytic with respect to the parameters, we consider global polynomial approximations based on tensor product, total degree or sparse polynomial spaces and constructed by either a Stochastic Galerkin or a Stochastic Collocation approach. We derive convergence rates for the different cases and present numerical results that show how these approaches are a valid alternative to the more traditional Monte Carlo Method for this class of problems. Copyright © 2009 John Wiley & Sons, Ltd. [source]


Blended kernel approximation in the ,-matrix techniques

NUMERICAL LINEAR ALGEBRA WITH APPLICATIONS, Issue 4 2002
W. Hackbusch
Abstract Several types of ,-matrices were shown to provide a data-sparse approximation of non-local (integral) operators in FEM and BEM applications. The general construction is applied to the operators with asymptotically smooth kernel function provided that the Galerkin ansatz space has a hierarchical structure. The new class of ,-matrices is based on the so-called blended FE and polynomial approximations of the kernel function and leads to matrix blocks with a tensor-product of block-Toeplitz (block-circulant) and rank- k matrices. This requires the translation (rotation) invariance of the kernel combined with the corresponding tensor-product grids. The approach allows for the fast evaluation of volume/boundary integral operators with possibly non-smooth kernels defined on canonical domains/manifolds in the FEM/BEM applications. (Here and in the following, we call domains canonical if they are obtained by translation or rotation of one of their parts, e.g. parallelepiped, cylinder, sphere, etc.) In particular, we provide the error and complexity analysis for blended expansions to the Helmholtz kernel. Copyright © 2002 John Wiley & Sons, Ltd. [source]