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  2. Order of approximation - Wikipedia

    en.wikipedia.org/wiki/Order_of_approximation

    In the zeroth-order example above, the quantity "a few" was given, but in the first-order example, the number "4" is given. A first-order approximation of a function (that is, mathematically determining a formula to fit multiple data points) will be a linear approximation, straight line with a slope: a polynomial of degree 1. For example:

  3. Padé approximant - Wikipedia

    en.wikipedia.org/wiki/Padé_approximant

    As a result, at the point , where the accuracy of the approximation may be the worst in the ordinary Padé approximation, good accuracy of the 2-point Padé approximant is guaranteed. Therefore, the 2-point Padé approximant can be a method that gives a good approximation globally for x = 0 ∼ ∞ {\displaystyle x=0\sim \infty } .

  4. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    The effect of averaging out questionable data points in a sample, rather than distorting the curve to fit them exactly, may be desirable. Runge's phenomenon: high order polynomials can be highly oscillatory. If a curve runs through two points A and B, it would be expected that the curve would run somewhat near the midpoint of A and B, as well

  5. Order of accuracy - Wikipedia

    en.wikipedia.org/wiki/Order_of_accuracy

    In numerical analysis, order of accuracy quantifies the rate of convergence of a numerical approximation of a differential equation to the exact solution. Consider u {\displaystyle u} , the exact solution to a differential equation in an appropriate normed space ( V , | | | | ) {\displaystyle (V,||\ ||)} .

  6. Muller's method - Wikipedia

    en.wikipedia.org/wiki/Muller's_method

    The next approximation x k is now one of the roots of the p k,m, i.e. one of the solutions of p k,m (x)=0. Taking m =1 we obtain the secant method whereas m =2 gives Muller's method. Muller calculated that the sequence { x k } generated this way converges to the root ξ with an order μ m where μ m is the positive solution of x m + 1 − x m ...

  7. Linearization - Wikipedia

    en.wikipedia.org/wiki/Linearization

    The linear approximation of a function is the first order Taylor expansion around the point of interest. In the study of dynamical systems , linearization is a method for assessing the local stability of an equilibrium point of a system of nonlinear differential equations or discrete dynamical systems . [ 1 ]

  8. Function approximation - Wikipedia

    en.wikipedia.org/wiki/Function_approximation

    Several progressively more accurate approximations of the step function. An asymmetrical Gaussian function fit to a noisy curve using regression.. In general, a function approximation problem asks us to select a function among a well-defined class [citation needed] [clarification needed] that closely matches ("approximates") a target function [citation needed] in a task-specific way.

  9. Approximation - Wikipedia

    en.wikipedia.org/wiki/Approximation

    Approximation is a key word generally employed within the title of a directive, for example the Trade Marks Directive of 16 December 2015 serves "to approximate the laws of the Member States relating to trade marks". [11] The European Commission describes approximation of law as "a unique obligation of membership in the European Union". [10]