它如何做到这一点的本质在于杰拉德·惠特利的《应用数值分析》节选:

当你的软件程序要求计算机获取一个值时 或者，你有没有想过它是如何得到 如果它能计算的最强大的函数是多项式? 它不会在表中查找这些并进行插值!相反, 计算机逼近除多项式以外的所有函数 一个多项式，可以精确地给出值。

上面要提到的几点是，一些算法实际上是从表中插值的，尽管只是在前几次迭代中。还要注意它是如何提到计算机利用近似多项式而没有指定哪种类型的近似多项式。正如本文中其他人指出的那样，在这种情况下，切比雪夫多项式比泰勒多项式更有效。

它如何做到这一点的本质在于杰拉德·惠特利的《应用数值分析》节选:

当你的软件程序要求计算机获取一个值时 或者，你有没有想过它是如何得到 如果它能计算的最强大的函数是多项式? 它不会在表中查找这些并进行插值!相反, 计算机逼近除多项式以外的所有函数 一个多项式，可以精确地给出值。

上面要提到的几点是，一些算法实际上是从表中插值的，尽管只是在前几次迭代中。还要注意它是如何提到计算机利用近似多项式而没有指定哪种类型的近似多项式。正如本文中其他人指出的那样，在这种情况下，切比雪夫多项式比泰勒多项式更有效。

无论何时这样一个函数被求值，那么在某种程度上很可能有:

内插的值表(用于快速，不准确的应用程序-例如计算机图形) 收敛于期望值的级数的计算——可能不是泰勒级数，更可能是基于像克伦肖-柯蒂斯这样的奇异正交。

如果没有硬件支持，那么编译器可能会使用后一种方法，只发出汇编代码(没有调试符号)，而不是使用c库——这让您在调试器中跟踪实际代码变得很棘手。

我将尝试在一个C程序中回答sin()的情况，该程序用GCC的C编译器在当前的x86处理器(假设是Intel Core 2 Duo)上编译。

在C语言中，标准C库包含了一些常见的数学函数，而这些函数并不包含在语言本身中(例如pow, sin和cos分别表示幂，sin和cos)。它们的头文件包含在math.h中。

现在在GNU/Linux系统上，这些库函数是由glibc (GNU libc或GNU C库)提供的。但是GCC编译器希望您使用-lm编译器标志链接到数学库(libm.so)，以启用这些数学函数的使用。我不确定为什么它不是标准C库的一部分。这些将是浮点函数的软件版本，或“软浮动”。

题外话:将数学函数分开的原因由来已久，据我所知，可能是在共享库可用之前，它仅仅是为了在非常古老的Unix系统中减少可执行程序的大小。

Now the compiler may optimize the standard C library function sin() (provided by libm.so) to be replaced with an call to a native instruction to your CPU/FPU's built-in sin() function, which exists as an FPU instruction (FSIN for x86/x87) on newer processors like the Core 2 series (this is correct pretty much as far back as the i486DX). This would depend on optimization flags passed to the gcc compiler. If the compiler was told to write code that would execute on any i386 or newer processor, it would not make such an optimization. The -mcpu=486 flag would inform the compiler that it was safe to make such an optimization.

现在，如果程序执行sin()函数的软件版本，它将基于CORDIC(坐标旋转数字计算机)或BKM算法，或者更可能是现在通常用于计算此类超越函数的表格或幂级数计算。(Src: http://en.wikipedia.org/wiki/Cordic应用程序)

任何最新的gcc版本(大约2.9倍以来)也提供了内置的sin版本__builtin_sin()，作为优化，它将用于取代对C库版本的标准调用。

我相信这是非常清楚的，但希望给你更多的信息比你期望的，和许多出发点，以了解更多自己。

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));  
}

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。