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  2. Bulirsch–Stoer algorithm - Wikipedia

    en.wikipedia.org/wiki/Bulirsch–Stoer_algorithm

    In numerical analysis, the Bulirsch–Stoer algorithm is a method for the numerical solution of ordinary differential equations which combines three powerful ideas: Richardson extrapolation, the use of rational function extrapolation in Richardson-type applications, and the modified midpoint method, [1] to obtain numerical solutions to ordinary ...

  3. Interpolation - Wikipedia

    en.wikipedia.org/wiki/Interpolation

    The term extrapolation is used to find data points outside the range of known data points. In curve fitting problems, the constraint that the interpolant has to go exactly through the data points is relaxed. It is only required to approach the data points as closely as possible (within some other constraints).

  4. Extrapolation - Wikipedia

    en.wikipedia.org/wiki/Extrapolation

    A sound choice of which extrapolation method to apply relies on a priori knowledge of the process that created the existing data points. Some experts have proposed the use of causal forces in the evaluation of extrapolation methods. [2] Crucial questions are, for example, if the data can be assumed to be continuous, smooth, possibly periodic, etc.

  5. Nearest-neighbor interpolation - Wikipedia

    en.wikipedia.org/wiki/Nearest-neighbor_interpolation

    For a given set of points in space, a Voronoi diagram is a decomposition of space into cells, one for each given point, so that anywhere in space, the closest given point is inside the cell. This is equivalent to nearest neighbor interpolation, by assigning the function value at the given point to all the points inside the cell. [ 3 ]

  6. Gekko (optimization software) - Wikipedia

    en.wikipedia.org/wiki/Gekko_(optimization_software)

    In the example above, the hyperbolic tangent activation function (hidden layer 2) could be replaced with a sine or cosine function to improve extrapolation. The final part of the script displays the neural network model, the original function, and the sampled data points used for fitting.

  7. Curve fitting - Wikipedia

    en.wikipedia.org/wiki/Curve_fitting

    Fitting of a noisy curve by an asymmetrical peak model, with an iterative process (Gauss–Newton algorithm with variable damping factor α).Curve fitting [1] [2] is the process of constructing a curve, or mathematical function, that has the best fit to a series of data points, [3] possibly subject to constraints.

  8. List of algorithms - Wikipedia

    en.wikipedia.org/wiki/List_of_algorithms

    An algorithm is fundamentally a set of rules or defined procedures that is typically designed and used to solve a specific problem or a broad set of problems.. Broadly, algorithms define process(es), sets of rules, or methodologies that are to be followed in calculations, data processing, data mining, pattern recognition, automated reasoning or other problem-solving operations.

  9. Polynomial interpolation - Wikipedia

    en.wikipedia.org/wiki/Polynomial_interpolation

    For example, given a = f(x) = a 0 x 0 + a 1 x 1 + ··· and b = g(x) = b 0 x 0 + b 1 x 1 + ···, the product ab is a specific value of W(x) = f(x)g(x). One may easily find points along W(x) at small values of x, and interpolation based on those points will yield the terms of W(x) and the specific product ab. As fomulated in Karatsuba ...