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In mathematics, least squares function approximation applies the principle of least squares to function approximation, by means of a weighted sum of other functions.The best approximation can be defined as that which minimizes the difference between the original function and the approximation; for a least-squares approach the quality of the approximation is measured in terms of the squared ...
What is meant by best and simpler will depend on the application. A closely related topic is the approximation of functions by generalized Fourier series, that is, approximations based upon summation of a series of terms based upon orthogonal polynomials.
The polynomial of best approximation within a given subspace is defined to be the one that minimizes the maximum absolute difference between the polynomial and the function. In this case, the form of the solution is precised by the equioscillation theorem .
The mean value theorem ensures that if there is a root of f in X k, then it is also in X k + 1. Moreover, the hypothesis on F′ ensures that X k + 1 is at most half the size of X k when m is the midpoint of Y, so this sequence converges towards [x*, x*], where x* is the root of f in X.
The conventional Padé approximation is determined to reproduce the Maclaurin expansion up to a given order. Therefore, the approximation at the value apart from the expansion point may be poor. This is avoided by the 2-point Padé approximation, which is a type of multipoint summation method. [ 9 ]
The result of fitting a set of data points with a quadratic function Conic fitting a set of points using least-squares approximation. In regression analysis, least squares is a parameter estimation method based on minimizing the sum of the squares of the residuals (a residual being the difference between an observed value and the fitted value provided by a model) made in the results of each ...
In mathematics, a linear approximation is an approximation of a general function using a linear function (more precisely, an affine function). They are widely used in the method of finite differences to produce first order methods for solving or approximating solutions to equations.
It is clear that the sequence of polynomials of best approximation () converges to f(x) uniformly (due to the Weierstrass approximation theorem). Now we have only to show that each p n ∗ ( x ) {\displaystyle p_{n}^{*}(x)} may be obtained by means of interpolation on certain nodes.