Oct 3, 2024 · In this study, we blend numerical analysis and statistics to introduce a stable and fast O(N\log N) algorithm called NoisyChebtrunc based on the Chebyshev ...
Oct 3, 2024 · This method is based on truncating the Chebyshev interpolant at an appropriate degree and corresponds to solving a weighted least-squares ...
Oct 6, 2024 · Abstract. Approximating a univariate function on the interval [ − 1 , 1 ] [-1,1] with a polynomial is among the most classical problems in ...
View recent discussion. Abstract: Approximating a univariate function on the interval $[-1,1]$ with a polynomial is among the most classical problems in ...
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This function provides a simple algorithm to estimate the SNR in a data set. The algorithm subdivides the data set into sections with a length determined by the ...
Jan 16, 2022 · I am trying to better understand the meaning of "noise" with regards to function optimization - specifically, why "Noisy" functions are more difficult to ...
Show polynomial approximation of noisy polynomial data a = polyfit(n,s2+noise,2); s2pf = polyval(a, n); figure(1) clf plot(n, s2 + noise,
This paper are to estimate the polynomial degree and the noise power from data coming from an underlying polynomial with additive Gaussian noise, using an AR ...
Dec 11, 2013 · I have a function f:[a,b]→R that I can observe through some noise, ie I can only directly measure f(x)+η where η is some random noise with mean 0.
A basic question in any model of computation is how to reliably compute a given function when its inputs are subject to noise.