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Polynomial interpolation also forms the basis for algorithms in numerical quadrature (Simpson's rule) and numerical ordinary differential equations (multigrid methods). In computer graphics, polynomials can be used to approximate complicated plane curves given a few specified points, for example the shapes of letters in typography.
One derivation replaces the integrand () by the quadratic polynomial (i.e. parabola) () that takes the same values as () at the end points and and the midpoint +, where = /.
Muller's method — based on quadratic interpolation at last three iterates; Sidi's generalized secant method — higher-order variants of secant method; Inverse quadratic interpolation — similar to Muller's method, but interpolates the inverse; Brent's method — combines bisection method, secant method and inverse quadratic interpolation
The simplest interpolation method is to locate the nearest data value, and assign the same value. In simple problems, this method is unlikely to be used, as linear interpolation (see below) is almost as easy, but in higher-dimensional multivariate interpolation, this could be a favourable choice for its speed and simplicity.
In numerical analysis, inverse quadratic interpolation is a root-finding algorithm, meaning that it is an algorithm for solving equations of the form f(x) = 0. The idea is to use quadratic interpolation to approximate the inverse of f. This algorithm is rarely used on its own, but it is important because it forms part of the popular Brent's method.
In the sixth iteration, we cannot use inverse quadratic interpolation because b 5 = b 4. Hence, we use linear interpolation between (a 5, f(a 5)) = (−3.35724, −6.78239) and (b 5, f(b 5)) = (−2.71449, 3.93934). The result is s = −2.95064, which satisfies all the conditions. But since the iterate did not change in the previous step, we ...
One class of examples is the staggered geometric progressions that get closer to their limits only every other step or every several steps, for instance the example () =,, /, /, /, /, …, / ⌊ ⌋, … detailed below (where ⌊ ⌋ is the floor function applied to ). The defining Q-linear convergence limits do not exist for this sequence ...
Lagrange and other interpolation at equally spaced points, as in the example above, yield a polynomial oscillating above and below the true function. This behaviour tends to grow with the number of points, leading to a divergence known as Runge's phenomenon ; the problem may be eliminated by choosing interpolation points at Chebyshev nodes .