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  2. Spline (mathematics) - Wikipedia

    en.wikipedia.org/wiki/Spline_(mathematics)

    A common spline is the natural cubic spline. A cubic spline has degree 3 with continuity C 2, i.e. the values and first and second derivatives are continuous. Natural means that the second derivatives of the spline polynomials are zero at the endpoints of the interval of interpolation.

  3. Spline interpolation - Wikipedia

    en.wikipedia.org/wiki/Spline_interpolation

    This can only be achieved if polynomials of degree 3 (cubic polynomials) or higher are used. The classical approach is to use polynomials of exactly degree 3 — cubic splines. In addition to the three conditions above, a natural cubic spline has the condition that ″ = ″ =.

  4. Bicubic interpolation - Wikipedia

    en.wikipedia.org/wiki/Bicubic_interpolation

    Bicubic interpolation can be accomplished using either Lagrange polynomials, cubic splines, or cubic convolution algorithm. In image processing, bicubic interpolation is often chosen over bilinear or nearest-neighbor interpolation in image resampling, when speed is not an issue.

  5. Smoothing spline - Wikipedia

    en.wikipedia.org/wiki/Smoothing_spline

    The most familiar example is the cubic smoothing spline, but there are many other possibilities, including for the case where is a vector quantity. Cubic spline definition [ edit ]

  6. Monotone cubic interpolation - Wikipedia

    en.wikipedia.org/wiki/Monotone_cubic_interpolation

    Example showing non-monotone cubic interpolation (in red) and monotone cubic interpolation (in blue) of a monotone data set. Monotone interpolation can be accomplished using cubic Hermite spline with the tangents m i {\displaystyle m_{i}} modified to ensure the monotonicity of the resulting Hermite spline.

  7. Lagrange polynomial - Wikipedia

    en.wikipedia.org/wiki/Lagrange_polynomial

    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. [5]

  8. 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.

  9. Cubic Hermite spline - Wikipedia

    en.wikipedia.org/wiki/Cubic_Hermite_spline

    Cubic polynomial splines are extensively used in computer graphics and geometric modeling to obtain curves or motion trajectories that pass through specified points of the plane or three-dimensional space. In these applications, each coordinate of the plane or space is separately interpolated by a cubic spline function of a separate parameter t.