Polynomial Interpolation: Practice Versus Theory

I have posted several times, that it is possible to pre-compute a matrix, such that to multiply a set of input-samples by this matrix, will result in the coefficients of a polynomial, and that next, a fractional position within the center-most interval of this polynomial can be computed – as a polynomial – to arrive at a smoothing function. There is a difference between how I represented this subject, and how it would be implemented.

I assumed non-negative values for the Time parameter, from 0 to 3 inclusively, such that the interval from 1 … 2 can be smoothed. This might work well for degrees up to 3, i.e. for orders up to 4. But in order to compute the matrices accurately, even using computers, when the degree of the polynomial is anything greater than 3, it makes sense to assume x-coordinates from -1 … +2 , or from -3 … +3 . Because, we can instruct a computer to divide by 3 to the 6th power more easily than by 6 to the 6th power.

And then in general, the evaluation of the polynomial will take place over the interval 0 … +1 .

The results can easily be shifted anywhere along the x-axis, as long as we only do the interpolation closest to the center. But the computation of the inverse matrix cannot.

My Example

Also, if it was our goal to illustrate the system to a reader who is not used to Math, then the hardest fact to prove, would be that the matrix of terms has a non-zero determinant, and is therefore invertible, when some of the terms are negative, as it was before.

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