Polynomial Approximation of Piecewise Analytic Functions on Quasi-Smooth Arcs

Polynomial operators for spectral approximation of piecewise analytic functions

We construct a sequence of globally defined polynomial valued operators, using linear combinations of either the coefficients in the orthogonal polynomial expansion of the target function with respect to a very general mass distribution μ or the values of the function at suitable points on [−1, 1], so that the degree of approximation by these operators globally is commensurate with the degree o...

Approximation and Compression of Piecewise Smooth Functions

Wavelet or subband coding has been quite successful in compression applications, and this success can be attributed in part to the good approximation properties of wavelets. In this paper, we revisit rate-distortion bounds for wavelet approximation of piecewise smooth functions, in particular the piecewise polynomial case. We contrast these results with rate-distortion bounds achievable using a...

Remarks on Piecewise Polynomial Approximation

The theoretical and experimental background for the approximation of "real-world" functions is reviewed to motivate the use of piecewise polynomial approximations. It is possible in practice ~o achieve the results suggested by the theory for piecewise polynomials. A convergent adaptive algorithm is outlined which effectively and efficiently computes smooth piecewise polynomial approxImations to...

Quasi-analytic Vectors and Quasi-analytic Functions

Neural Networks for Optimal Approximation of Smooth and Analytic Functions

We prove that neural networks with a single hidden layer are capable of providing an optimal order of approximation for functions assumed to possess a given number of derivatives, if the activation function evaluated by each principal element satisfies certain technical conditions. Under these conditions, it is also possible to construct networks that provide a geometric order of approximation ...