Results 271 to 280 of about 71,359 (311)

Nonlinear Wavelet Approximation in BMO

Constructive Approximation, 2022
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Ivanov, Kamen G., Petrushev, Pencho
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Nonlinear Approximation of Random Functions

SIAM Journal on Applied Mathematics, 1997
Summary: Given an orthonormal basis and a certain class \(X\) of vectors in a Hilbert space \(H\), consider the following nonlinear approximation process: approach a vector \(x\in X\) by keeping only its \(N\) largest coordinates, and let \(N\) go to infinity.
Albert Cohen 0002, Jean-Pierre D'Ales
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An Analysis of Approximate Nonlinear Elimination

SIAM Journal on Scientific Computing, 1996
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Paul J. Lanzkron, Donald J. Rose, James T. Wilkes   +2 more
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Nonlinear Methods of Approximation

Foundations of Computational Mathematics, 2003
This extensive survey paper is, according to its author, complementary to the survey by \textit{R. A. DeVore} [Acta Numerica 7, 51--150 (1998; Zbl 0931.65007)]. The central concept is \(m\)-term approximation, that is, approximation of a given element \(f\) of a Banach space \(X\) by linear combinations of \(\leq m\) elements \(g_k\) taken from some ...
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Approximations for nonlinear functions

IEEE Transactions on Circuits and Systems I: Fundamental Theory and Applications, 1992
By applying a version of the Stone-Weierstrass theorem the author shows that a continuous real-valued function on a nonempty compact topological space can be uniformly approximated by a sum of the form \[ a_ 1 e^{\phi(x,p_ 1)}+\cdots+ a_ me^{\phi(x,p_ m)}. \] {}.
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Minimax nonlinear approximation by approximation on subsets

Communications of the ACM, 1972
A possible algorithm for minimax approximation on an infinite set X consists in choosing a sequence of finite point sets { X k } which fill out X and taking a limit of minimax approximations on X
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Nonlinear Estimation and Asymptotic Approximations

Econometrica, 1978
central objective of this paper is to present a series expansion of nonlinear estimators in order to facilitate an analysis of the distributions of such estimators. Where the estimator under consideration is a maximum likelihood estimator, the method provides somewhat more information, as well as higher order approximations to the distributions of the ...
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Approximation by nonlinear wavelet networks

[Proceedings] ICASSP 91: 1991 International Conference on Acoustics, Speech, and Signal Processing, 1991
By combining the class of feedforward neural networks and results from the wavelet theory, a class of networks call wavelet networks that can be used to approximate any nonlinear function is proposed. A stochastic gradient procedure for black-box identification of nonlinear static systems based on this class of networks is developed.
Qinghua Zhang, Albert Benveniste
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Nonlinear Nonnested Spline Approximation

Constructive Approximation, 2016
Linear and in particular non-linear spline approximation is a most useful tool in the approximation of for instance two-dimensional functions. Usually, piecewise polynomial splines with more and more refined knot-sequences are considered as elements of nested spaces spanned by splines. Generalising from this point of view, it is interesting to consider
Lind, Martin, Petrushev, Pencho
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Nonlinear approximation

Acta Numerica, 1998
This is a survey of nonlinear approximation, especially that part of the subject which is important in numerical computation. Nonlinear approximation means that the approximants do not come from linear spaces but rather from nonlinear manifolds. The central question to be studied is what, if any, are the advantages of nonlinear approximation over the ...
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