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大学生网站的设计风格,站长工具麻豆,电子商务网站建设参考文献,做网站用什么开源算法描述： (1). 先做自变量x的范围检查，不能出现负数和0. 自己使用时，如果能通过其它途径保证自变量为正，那么可以省略这两个判断，提高速度。 (2). 根据IEEE 754浮点数的格式，，则 ln(x)kln(2)ln…

算法描述：
(1). 先做自变量x的范围检查，不能出现负数和0. 自己使用时，如果能通过其它途径保证自变量为正，那么可以省略这两个判断，提高速度。
(2). 根据IEEE 754浮点数的格式， $x=2^k \cdot m,\ m\in(1,2)$ ，则 ln(x)=kln(2)+ln(m)，可以通过位运算方便快速地获取k和m .
(3). 把 ln(1+x) 和 ln(1-x) 在 x=0 处的泰勒级数相减，

$\ln(1+x) =x-\frac{x^2}{2}+\frac{x^3}{3}-\frac{x^4}{4}+\cdots$

$\ln(1-x) =-x-\frac{x^2}{2}-\frac{x^3}{3}-\frac{x^4}{4}-\cdots$

$\ln\frac{1+x}{1-x}=2x+\frac{2x^3}{3}+\frac{2x^5}{5}+\cdots$

因为m的范围是(1, 2)，不够接近1，如果直接令m=(1+x)/(1-x)，那么x不够接近0，代入上面的泰勒级数，则精度不够高，所以要对m进行变换，常见的做法是乘上sqrt(2)/2，即

$\ln(x)=k\ln(2)+\ln(m) \\ =k\ln(2)+\ln(\sqrt{2})+\ln(\frac{\sqrt{2}}{2}m) \\ =(k+\frac{1}{2})\ln(2)+\ln(\frac{\sqrt{2}}{2}m)$

$\frac{\sqrt{2}}{2}m\in(\frac{\sqrt{2}}{2}, \sqrt{2}),\quad x=\frac{\frac{\sqrt{2}}{2}m-1}{\frac{\sqrt{2}}{2}m+1} \in(-(3-2\sqrt{2}),3-2\sqrt{2})$

如果改为乘以 2/3，则

$\frac{2}{3}m\in(\frac{2}{3}, \frac{4}{3}),\quad x=\frac{\frac{2}{3}m-1}{\frac{2}{3}m+1} \in(-\frac{1}{5},\frac{1}{7})$

这个区间长度为12/35=0.34285714，比区间 $(-(3-2\sqrt{2}),3-2\sqrt{2})$ 的长度 $6-4\sqrt{2}$ =0.34314575更短，代入泰勒级数后的精度更高一些。

$\ln(x)=k\ln(2)+\ln(m) \\ =k\ln(2)+\ln(\frac{3}{2})+\ln(\frac{2}{3}m)$
多项式求值采用秦九韶算法，同时还使用fmadd指令加速运算(融合乘加，intel _mm_fmadd_sd)

计算机如何计算对数函数_数值计算】求对数函数值,输入实数x>0 ,输出x对应的对数函数值ln(x)(使用双精度dou-CSDN博客更详细地解释了如何利用IEEE 754浮点数的格式获取k和m.

标准库的算法可参考：glibc/sysdeps/ieee754/dbl-64/s_log1p.c at master · bminor/glibc · GitHub

最终的效果是，精度与标准库互有胜负(以windows的计算器以及Wolfram|Alpha: Computational Intelligence作为参考值)，如果不对自变量为NAN的情况进行处理，速度稍快于标准库。

C++代码如下：

#include<stdio.h>
#include<math.h>
#include<time.h>
#include<immintrin.h>#define FMADD
constexpr double ln2 = 0.6931471805599453;
constexpr double ln3_2 = 0.40546510810816438;   // ln(3/2)
constexpr double sqrt2_2 = 0.7071067811865475;  // sqrt(2)/2
constexpr unsigned long long x000F = 0x000FFFFFFFFFFFFF;
constexpr unsigned long long x3FF0 = 0x3FF0000000000000;__m128d c17 = _mm_set_sd(2.0 / 17.0);
__m128d c15 = _mm_set_sd(2.0 / 15.0);
__m128d c13 = _mm_set_sd(2.0 / 13.0);
__m128d c11 = _mm_set_sd(2.0 / 11.0);
__m128d c9 = _mm_set_sd(2.0 / 9.0);
__m128d c7 = _mm_set_sd(2.0 / 7.0);
__m128d c5 = _mm_set_sd(2.0 / 5.0);
__m128d c3 = _mm_set_sd(2.0 / 3.0);
__m128d c1 = _mm_set_sd(2.0);inline double myln(double x) {if (x < 0) {return NAN;}if (x == 0) {return -INFINITY;}unsigned long long llx = *reinterpret_cast<unsigned long long*>(&x);short k = (llx >> 52) - 1023;        //  x = 2^k * munsigned long long llm = (llx & x000F) | x3FF0;double m = *reinterpret_cast<double*>(&llm);m *= 0.66666666666666666;    //m *= sqrt2_2;x = (m - 1.0) / (m + 1.0);double x2 = x * x;
#ifdef FMADD__m128d x128 = _mm_set_sd(x);__m128d x2_128 = _mm_set_sd(x2);__m128d t128 = c17;t128 = _mm_fmadd_sd(t128, x2_128, c15);t128 = _mm_fmadd_sd(t128, x2_128, c13);t128 = _mm_fmadd_sd(t128, x2_128, c11);t128 = _mm_fmadd_sd(t128, x2_128, c9);t128 = _mm_fmadd_sd(t128, x2_128, c7);t128 = _mm_fmadd_sd(t128, x2_128, c5);t128 = _mm_fmadd_sd(t128, x2_128, c3);t128 = _mm_fmadd_sd(t128, x2_128, c1);t128 = _mm_mul_sd(t128, x128);m = _mm_cvtsd_f64(t128);
#elsem = 2.0 / 17.0;m = m * x2 + 2.0 / 15.0;m = m * x2 + 2.0 / 13.0;m = m * x2 + 2.0 / 11.0;m = m * x2 + 2.0 / 9.0;m = m * x2 + 2.0 / 7.0;m = m * x2 + 2.0 / 5.0;m = m * x2 + 2.0 / 3.0;m = m * x2 + 2.0;m *= x;
#endifreturn k * ln2 + ln3_2 + m;  // return (k + 0.5) * ln2 + m; //如果前面 m *= sqrt2_2，那就需要用这一行return
}int main() {printf("double, 精度测试\n");for (double x = 0.1; x < 3; x += 0.1) {printf("myln(%2.1f)=%18.16lf\n  ln(%2.1f)=%18.16lf\n-------\n", x, myln(x), x, log(x));}printf("速度测试，编译器优化设为/O2，CPU：Core i7-11800H \n");clock_t start = clock();double sum = 0;double x1 = 0.01, x2 =1000, dx = 1e-6;for (double x = x1; x < x2; x += dx) {sum += myln(x) / x;}printf("sum=%lf, myln_Time: %fs\n", sum, (double)(clock() - start) / CLOCKS_PER_SEC);start = clock();sum = 0;for (double x = x1; x < x2; x += dx) {sum += log(x) / x;}printf("sum=%lf,   ln_Time: %fs\n", sum, (double)(clock() - start) / CLOCKS_PER_SEC);
}

运行结果如下：

double, 精度测试
myln(0.1)=-2.3025850929940459ln(0.1)=-2.3025850929940455
-------
myln(0.2)=-1.6094379124341003ln(0.2)=-1.6094379124341003
-------
myln(0.3)=-1.2039728043259359ln(0.3)=-1.2039728043259359
-------
myln(0.4)=-0.9162907318741550ln(0.4)=-0.9162907318741550
-------
myln(0.5)=-0.6931471805599396ln(0.5)=-0.6931471805599453
-------
myln(0.6)=-0.5108256237659907ln(0.6)=-0.5108256237659907
-------
myln(0.7)=-0.3566749439387324ln(0.7)=-0.3566749439387324
-------
myln(0.8)=-0.2231435513142100ln(0.8)=-0.2231435513142098
-------
myln(0.9)=-0.1053605156578265ln(0.9)=-0.1053605156578264
-------
myln(1.0)=-0.0000000000000002ln(1.0)=-0.0000000000000001
-------
myln(1.1)=0.0953101798043247ln(1.1)=0.0953101798043247
-------
myln(1.2)=0.1823215567939545ln(1.2)=0.1823215567939546
-------
myln(1.3)=0.2623642644674911ln(1.3)=0.2623642644674911
-------
myln(1.4)=0.3364722366212130ln(1.4)=0.3364722366212130
-------
myln(1.5)=0.4054651081081644ln(1.5)=0.4054651081081646
-------
myln(1.6)=0.4700036292457357ln(1.6)=0.4700036292457357
-------
myln(1.7)=0.5306282510621706ln(1.7)=0.5306282510621706
-------
myln(1.8)=0.5877866649021192ln(1.8)=0.5877866649021193
-------
myln(1.9)=0.6418538861723950ln(1.9)=0.6418538861723950
-------
myln(2.0)=0.6931471805599509ln(2.0)=0.6931471805599455
-------
myln(2.1)=0.7419373447293780ln(2.1)=0.7419373447293776
-------
myln(2.2)=0.7884573603642703ln(2.2)=0.7884573603642705
-------
myln(2.3)=0.8329091229351041ln(2.3)=0.8329091229351043
-------
myln(2.4)=0.8754687373539001ln(2.4)=0.8754687373539003
-------
myln(2.5)=0.9162907318741552ln(2.5)=0.9162907318741554
-------
myln(2.6)=0.9555114450274365ln(2.6)=0.9555114450274368
-------
myln(2.7)=0.9932517730102837ln(2.7)=0.9932517730102838
-------
myln(2.8)=1.0296194171811583ln(2.8)=1.0296194171811586
-------
myln(2.9)=1.0647107369924285ln(2.9)=1.0647107369924287
-------
速度测试，编译器优化设为/O2，CPU：Core i7-11800H
sum=13254515.057331, myln_Time: 3.817000s
sum=13254515.057331,   ln_Time: 3.838000s