## Evaluating polynomials

05/05/2020

Evaluating polynomials is not a thing I do very often. When I do, it’s for interpolation and splines; and traditionally those are done with relatively low degree polynomials—cubic at most. There are a few rather simple tricks you can use to evaluate them efficiently, and we’ll have a look at them.

## Horner’s Method

25/10/2016

It’s not like evaluating polynomial is something that comes up that frequently in the average programmer’s day. In fact, almost never, but it brings up a number of interesting questions, such as, how do we evaluate it very efficiently and how much of a simplification in the computation is actually a simplification?

The generic polynomial has the form

$a_0+a_1x+a_2x^2+a_3x^3+\cdots+a_nx^n$.

If we evaluate this naïvely, we end up doing $O(n^2)$ multiply and $O(n)$ additions. Even using the fast exponentiation algorithm, we still use $O(n \lg n)$ multiplies. But we can, very easily, get down to $O(n)$.

## Trigonometric Tables Reconsidered

28/02/2012

A couple of months ago (already!) 0xjfdube produced an excellent piece on table-based trigonometric function computations. The basic idea was that you can look-up the value of a trigonometric function rather than actually computing it, on the premise that computing such functions directly is inherently slow. Turns out that’s actually the case, even on fancy CPUs. He discusses the precision of the estimate based on the size of the table and shows that you needn’t a terrible number of entries to get decent precision. He muses over the possibility of using interpolation to augment precision, speculating that it might be slow anyway.

I started thinking about how to interpolate efficiently between table entries but then I realized that it’s not fundamentally more complicated to compute a polynomial approximation of the functions than to search the table then use polynomial interpolation between entries.