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One good solution is linear interpolation, which draws a line between the two points in the table on either side of the value and locates the answer on that line. This is still quick to compute, and much more accurate for smooth functions such as the sine function. Here is an example using linear interpolation:
So for a 45 year old female, using Table 2 you would look down the first column to find 45 and then across to the 0% column which gives a figure of 43.93. In cases where the age is not a whole number, i.e. female who is 45.75 years, then you use the figure for 45 years (43.93) and the figure for 46 years (42.87) and interpolate between the two ...
A description of linear interpolation can be found in the ancient Chinese mathematical text called The Nine Chapters on the Mathematical Art (九章算術), [1] dated from 200 BC to AD 100 and the Almagest (2nd century AD) by Ptolemy. The basic operation of linear interpolation between two values is commonly used in computer graphics.
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 mathematics, divided differences is an algorithm, historically used for computing tables of logarithms and trigonometric functions. [citation needed] Charles Babbage's difference engine, an early mechanical calculator, was designed to use this algorithm in its operation.
There are several useful properties of natural neighbor interpolation: [4] The method is an exact interpolator, in that the original data values are retained at the reference data points. The method creates a smooth surface free from any discontinuities.
Barnes interpolation; Bilinear interpolation; Bicubic interpolation; Bézier surface; Lanczos resampling; Delaunay triangulation; Bitmap resampling is the application of 2D multivariate interpolation in image processing. Three of the methods applied on the same dataset, from 25 values located at the black dots. The colours represent the ...
Runge's phenomenon shows that for high values of n, the interpolation polynomial may oscillate wildly between the data points. This problem is commonly resolved by the use of spline interpolation . Here, the interpolant is not a polynomial but a spline : a chain of several polynomials of a lower degree.