[eh_why_not]

Anyone experienced with the Remez algorithm mentioned at the end of the article?

The degree-9 polynomial, said to be a thousand times better than the original Taylor approximation in maximum error, also appears to be very close to the Taylor series in the first place.

Rounding the Taylor coefficients to 6 digits after the decimal:

1/3! = 0.166667

1/5! = 0.008333

1/7! = 0.000198

1/9! = 0.000027(56)

The first 2 are exact, the third is 5 digits only (so 0.000190), and the fourth is more different starting from the 6th digit (0.000026019).

The delta in the 9-th order is expected if you were to truncate the Taylor series starting from the 11th order to infinity (+ x^11 / 11! - x^13/13! ...).

[olooney]

I don't think the polynomial given in the article was calculated via Remez. Perhaps the author merely meant it as an illustration. Here is Wolfram Alpha plotting the error of equation from the article:

https://www.wolframalpha.com/input?i=plot+sin%28x%29+-+P%28x....

You'll note that the error is small near zero, and large near pi/4, which is characteristic of a Taylor series around zero, and not the characteristic "level" oscillation characteristic of Remez approximations[1]. Note also that the polynomial only includes odd terms, which is not something I would expect from Remez unless it was run on the symmetric interval [-pi/4, pi/4].

I ran Remez for the problem and after 4 iterations obtained a degree 8 polynomial with error less than 1e-9, but it didn't look anything like the polynomial given in the article.

    1.7209863008943345e-05x^8 -0.00024575124459624625x^7 +7.194649190849227e-05x^6 +0.008268794893899754x^5 +3.425379759410762e-05x^4 -0.16667692317020713x^3 +1.5422400957890642e-06x^2 +0.9999999106213322x +8.526477071446453e-10

Although of course the first few digits of the low-order matching terms will be very similar - any polynomial approximation method will agree there because they are fitting the same function, after all. But by the time we reach x^5 or x^7 the agreement is very loose, really only the first couple digits are the same.

[1]: https://en.wikipedia.org/wiki/Approximation_theory

[eh_why_not]

> Perhaps the author merely meant it as an illustration.

Yes I too was expecting oscillations (since I saw Chebyshev polynomials mentioned on the wiki page). I'm beginning to think that the word "might" in their statement "One way to calculate is Remez’s algorithm. The results might look like this:" is doing a lot of heavy lifting.

A couple of notes about your results:

- The interval only needs to be [0, pi/16] and not [0, pi/4]; adjusting that in your wolframalpha query yields a similar bound to what the author lists: 3.5 e-9. (Although same observation about the change of behavior at the bounds as you made).

- The polynomial you obtained actually looks pretty close to the Taylor one: the even coefficients are small, and rounding to 5 or 6 digits might yield a close one, maybe if we look for a degree 9 polynomial instead of degree 8. (I don't have an implementation of Remez handy right now to try it myself).

> Although of course the first few digits of the low-order matching terms will be very similar

Makes sense.

[stephencanon]

https://en.wikipedia.org/wiki/Remez_algorithm

It’s a very simple iterative algorithm, essentially the dumbest thing that could possibly work (like most good algorithms). It fails to converge for functions that have poles nearby unless you have a very good initial guess (the Chebyshev or Carathéodory-Fejér approximants are ~always good starting points and easily computed). In practice you want to optimize a weighted L-inf norm rather than absolute, because floating-point errors are measured in a relative norm.

[djmips]

Robin Green goes into this in this GDC 2002 article.

https://basesandframes.files.wordpress.com/2016/05/rgreenfas...