没有什么比点击源代码，看看人们是如何在常用的库中实际完成它的了;让我们特别看看一个C库实现。我选择了uLibC。

这是sin函数:

http://git.uclibc.org/uClibc/tree/libm/s_sin.c

看起来它处理了一些特殊情况，然后执行一些参数约简，将输入映射到范围[-pi/4,pi/4]，(将参数分成两部分，一个大的部分和一个尾巴)，然后调用

http://git.uclibc.org/uClibc/tree/libm/k_sin.c

然后作用于这两个部分。 如果没有尾巴，则使用13次多项式生成近似答案。 如果有尾巴，根据sin(x+y) = sin(x) + sin'(x')y的原理，你会得到一个小的修正

计算正弦/余弦/正切其实很容易通过代码使用泰勒级数来实现。自己写一个只需5秒钟。

整个过程可以用这个方程来概括:

下面是我为C语言写的一些例程:

double _pow(double a, double b) {
    double c = 1;
    for (int i=0; i<b; i++)
        c *= a;
    return c;
}

double _fact(double x) {
    double ret = 1;
    for (int i=1; i<=x; i++) 
        ret *= i;
    return ret;
}

double _sin(double x) {
    double y = x;
    double s = -1;
    for (int i=3; i<=100; i+=2) {
        y+=s*(_pow(x,i)/_fact(i));
        s *= -1;
    }  
    return y;
}
double _cos(double x) {
    double y = 1;
    double s = -1;
    for (int i=2; i<=100; i+=2) {
        y+=s*(_pow(x,i)/_fact(i));
        s *= -1;
    }  
    return y;
}
double _tan(double x) {
     return (_sin(x)/_cos(x));  
}

如果您想查看这些函数在C语言中的实际GNU实现，请查看glibc的最新主干。参见GNU C库。

OK kiddies, time for the pros.... This is one of my biggest complaints with inexperienced software engineers. They come in calculating transcendental functions from scratch (using Taylor's series) as if nobody had ever done these calculations before in their lives. Not true. This is a well defined problem and has been approached thousands of times by very clever software and hardware engineers and has a well defined solution. Basically, most of the transcendental functions use Chebyshev Polynomials to calculate them. As to which polynomials are used depends on the circumstances. First, the bible on this matter is a book called "Computer Approximations" by Hart and Cheney. In that book, you can decide if you have a hardware adder, multiplier, divider, etc, and decide which operations are fastest. e.g. If you had a really fast divider, the fastest way to calculate sine might be P1(x)/P2(x) where P1, P2 are Chebyshev polynomials. Without the fast divider, it might be just P(x), where P has much more terms than P1 or P2....so it'd be slower. So, first step is to determine your hardware and what it can do. Then you choose the appropriate combination of Chebyshev polynomials (is usually of the form cos(ax) = aP(x) for cosine for example, again where P is a Chebyshev polynomial). Then you decide what decimal precision you want. e.g. if you want 7 digits precision, you look that up in the appropriate table in the book I mentioned, and it will give you (for precision = 7.33) a number N = 4 and a polynomial number 3502. N is the order of the polynomial (so it's p4.x^4 + p3.x^3 + p2.x^2 + p1.x + p0), because N=4. Then you look up the actual value of the p4,p3,p2,p1,p0 values in the back of the book under 3502 (they'll be in floating point). Then you implement your algorithm in software in the form: (((p4.x + p3).x + p2).x + p1).x + p0 ....and this is how you'd calculate cosine to 7 decimal places on that hardware.

请注意，在FPU中大多数硬件实现的超越操作通常涉及一些微码和类似的操作(取决于硬件)。 切比雪夫多项式用于大多数先验多项式，但不是全部。例:使用Newton raphson方法的两次迭代，首先使用查询表，使用平方根更快。 同样，《计算机逼近》这本书会告诉你。

If you plan on implmementing these functions, I'd recommend to anyone that they get a copy of that book. It really is the bible for these kinds of algorithms. Note that there are bunches of alternative means for calculating these values like cordics, etc, but these tend to be best for specific algorithms where you only need low precision. To guarantee the precision every time, the chebyshev polynomials are the way to go. Like I said, well defined problem. Has been solved for 50 years now.....and thats how it's done.

Now, that being said, there are techniques whereby the Chebyshev polynomials can be used to get a single precision result with a low degree polynomial (like the example for cosine above). Then, there are other techniques to interpolate between values to increase the accuracy without having to go to a much larger polynomial, such as "Gal's Accurate Tables Method". This latter technique is what the post referring to the ACM literature is referring to. But ultimately, the Chebyshev Polynomials are what are used to get 90% of the way there.

享受。

不要用泰勒级数。切比雪夫多项式更快更准确，正如上面几个人指出的那样。下面是一个实现(最初来自ZX Spectrum ROM): https://albertveli.wordpress.com/2015/01/10/zx-sine/

切比雪夫多项式，正如在另一个答案中提到的，是函数和多项式之间的最大差异尽可能小的多项式。这是一个很好的开始。

在某些情况下，最大误差不是你感兴趣的，而是最大相对误差。例如，对于正弦函数，x = 0附近的误差应该比较大的值小得多;你想要一个小的相对误差。所以你可以计算sinx / x的切比雪夫多项式，然后把这个多项式乘以x。

Next you have to figure out how to evaluate the polynomial. You want to evaluate it in such a way that the intermediate values are small and therefore rounding errors are small. Otherwise the rounding errors might become a lot larger than errors in the polynomial. And with functions like the sine function, if you are careless then it may be possible that the result that you calculate for sin x is greater than the result for sin y even when x < y. So careful choice of the calculation order and calculation of upper bounds for the rounding error are needed.

例如，sinx = x - x^3/6 + x^5 / 120 - x^7 / 5040…如果你天真地计算sinx = x * (1 - x^2/6 + x^4/120 - x^6/5040…)，那么括号中的函数是递减的，如果y是x的下一个大的数字，那么有时siny会小于sinx。相反，计算sinx = x - x^3 * (1/6 - x^2/ 120 + x^4/5040…)，这是不可能发生的。

例如，在计算切比雪夫多项式时，通常需要将系数四舍五入到双倍精度。但是，虽然切比雪夫多项式是最优的，但系数舍入为双精度的切比雪夫多项式并不是具有双精度系数的最优多项式!

For example for sin (x), where you need coefficients for x, x^3, x^5, x^7 etc. you do the following: Calculate the best approximation of sin x with a polynomial (ax + bx^3 + cx^5 + dx^7) with higher than double precision, then round a to double precision, giving A. The difference between a and A would be quite large. Now calculate the best approximation of (sin x - Ax) with a polynomial (b x^3 + cx^5 + dx^7). You get different coefficients, because they adapt to the difference between a and A. Round b to double precision B. Then approximate (sin x - Ax - Bx^3) with a polynomial cx^5 + dx^7 and so on. You will get a polynomial that is almost as good as the original Chebyshev polynomial, but much better than Chebyshev rounded to double precision.

Next you should take into account the rounding errors in the choice of polynomial. You found a polynomial with minimum error in the polynomial ignoring rounding error, but you want to optimise polynomial plus rounding error. Once you have the Chebyshev polynomial, you can calculate bounds for the rounding error. Say f (x) is your function, P (x) is the polynomial, and E (x) is the rounding error. You don't want to optimise | f (x) - P (x) |, you want to optimise | f (x) - P (x) +/- E (x) |. You will get a slightly different polynomial that tries to keep the polynomial errors down where the rounding error is large, and relaxes the polynomial errors a bit where the rounding error is small.

所有这些将使您轻松地获得最多0.55倍于最后一位的舍入误差，其中+，-，*，/的舍入误差最多为0.50倍于最后一位。