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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 ]
Even for proper assumptions about the function, the extrapolation can diverge severely from the function. The classic example is truncated power series representations of sin(x) and related trigonometric functions. For instance, taking only data from near the x = 0, we may estimate that the function behaves as sin(x) ~ x.
where A(x) is the volume of the new cell centered in x, and A(x i) is the volume of the intersection between the new cell centered in x and the old cell centered in x i. Natural neighbor interpolation with Laplace weights. The interface l(x i) between the cells linked to x and x i is in blue, while the distance d(x i) between x and x i is in red.
Furthermore, you only need to do O(n) extra work if an extra point is added to the data set, while for the other methods, you have to redo the whole computation. Another method is preferred when the aim is not to compute the coefficients of p ( x ), but only a single value p ( a ) at a point x = a not in the original data set.
The Whittaker–Shannon interpolation formula can be used if the number of data points is infinite or if the function to be interpolated has compact support. Sometimes, we know not only the value of the function that we want to interpolate, at some points, but also its derivative. This leads to Hermite interpolation problems.
The ability to chain formulas together is what gives a spreadsheet its power. Many problems can be broken down into a series of individual mathematical steps, and these can be assigned to individual formulas in cells. Some of these formulas can apply to ranges as well, like the SUM function that adds up all the numbers within a range.
The cubic Hermite spline article will remind you that (,,,) = () for some 4-vector () which is a function of x alone, where is the value at of the function to be interpolated. Rewrite this approximation as
A (1, 1) = Trapezoidal (f, tStart, tEnd, h, y0) % Each row of the matrix requires one call to Trapezoidal % This loops starts by filling the second row of the matrix, % since the first row was computed above for i = 1: maxRows-1 % Starting at i = 1, iterate at most maxRows - 1 times % Halve the previous value of h since this is the start of a ...
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