double root(double n){
// Max and min are used to take into account numbers less than 1
double lo = min(1, n), hi = max(1, n), mid;
// Update the bounds to be off the target by a factor of 10
while(100 * lo * lo < n) lo *= 10;
while(0.01 * hi * hi > n) hi *= 0.1;
for(int i = 0 ; i < 100 ; i++){
mid = (lo+hi)/2;
if(mid*mid == n) return mid;
if(mid*mid > n) hi = mid;
else lo = mid;
}
return mid;
}
请注意,while循环是最常见的使用二分搜索,但我个人更喜欢在处理十进制数字时使用 for ,它可以节省一些特殊情况的处理,并从像 1000 甚至 < code>500 (两者都会为几乎所有数字提供相同的结果,但只是为了安全起见)。
double root(double n){
// Max and min are used to take into account numbers less than 1
double lo = min(1, n), hi = max(1, n), mid;
// Update the bounds to be off the target by a factor of 10
while(100 * lo * lo < n) lo *= 10;
while(0.01 * hi * hi > n) hi *= 0.1;
for(int i = 0 ; i < 100 ; i++){
mid = (lo+hi)/2;
if(mid*mid == n) return mid;
if(mid*mid > n) hi = mid;
else lo = mid;
}
return mid;
}
Note that while loop is most common with the binary search but Personally I prefer using for when dealing with decimal numbers, it saves some special cases handling and gets pretty accurate result from small loops like that 1000 or even 500 (Both will give the same result for almost all numbers but just to be safe).
Edit: Check out this Wikipedia article for various -special purpose- methods specialized in computing the square root.
Edit 2: Apply updates suggested by @jorgbrown to fix the function in case of input less than 1. Also, apply optimization to make the bounds off the target root by a factor of 10
On Intel hardware, it's often implemented on top of the hardware SQRT instruction. Some libraries just use the result of that straight off, some may put it through a couple of rounds of Newton optimisation to make it more accurate in the corner cases.
The FDLIBM (Freely Distributable LIBM) has a quite nice documented version of sqrt. e_sqrt.c.
The have one version which uses integer arithmetic and a recurrence formula modifying one bit at a time.
Another method uses Newton's method. It starts with some black magic and a lookup table to get the first 8 bits and then applies the recurrence formula
y_{i+1} = 1/2 * ( y_i + x / y_i)
where x is the number we started with. This is the Babylonian method of Heron's method. It dates back to Hero of Alexandra in the first centuary AD.
There is another method called the Fast inverse square root or reciproot. which uses some "evil floating point bit level hacking" to find the value of 1/sqrt(x). i = 0x5f3759df - ( i >> 1 ); It exploits the binary representation of a float using the mantisse and exponent. If our number x is (1+m) * 2^e, where m is the mantissa and e the exponent and the result y = 1/sqrt(x) = (1+n) * 2^f. Taking logs
lg(y) = - 1/2 lg(x)
f + lg(1+n) = -1/2 e - 1/2 lg(1+m)
So we see the exponent part of the result is -1/2 the exponent of the number. The black magic basically does a bitwise shift on the exponent and uses a linear approximation on the mantissa.
Once you have a good first approximation you can use Newton's methods to get a better result and finally some bit level work to fix the last digit.
func Sqrt(x float64) float64 {
// let initial guess to be 1
z := 1.0
for i := 1; i <= 10; i++ {
z -= (z*z - x) / (2*z) // MAGIC LINE!!
fmt.Println(z)
}
return z
}
func Sqrt(x float64) float64 {
// let initial guess to be 1
z := 1.0
for i := 1; i <= 10; i++ {
z -= (z*z - x) / (2*z) // MAGIC LINE!!
fmt.Println(z)
}
return z
}
The following is a mathematical explanation of the magic line. Suppose you want to find the root of the polynomial $f(x) = x^2 - a$. By Newton's method, you could start with an initial guess $x_0 = 1$. The next guess is $x_1 = x_0 - f(x_0)/f'(x_0)$, where $f'(x) = 2x$. Therefore, your new guess is
这是一个简单直接的例程,不使用 FP 或除法。每行都会计算结果中的另一位,但第一个 if 语句除外,该语句在输入较小时会加速例程。
constexpr unsigned int root(unsigned int x) {
unsigned int i = 0;
if (x >= 65536) {
if ((i + 32768) * (i + 32768) <= x) i += 32768;
if ((i + 16384) * (i + 16384) <= x) i += 16384;
if ((i + 8192) * (i + 8192) <= x) i += 8192;
if ((i + 4096) * (i + 4096) <= x) i += 4096;
if ((i + 2048) * (i + 2048) <= x) i += 2048;
if ((i + 1024) * (i + 1024) <= x) i += 1024;
if ((i + 512) * (i + 512) <= x) i += 512;
if ((i + 256) * (i + 256) <= x) i += 256;
}
if ((i + 128) * (i + 128) <= x) i += 128;
if ((i + 64) * (i + 64) <= x) i += 64;
if ((i + 32) * (i + 32) <= x) i += 32;
if ((i + 16) * (i + 16) <= x) i += 16;
if ((i + 8) * (i + 8) <= x) i += 8;
if ((i + 4) * (i + 4) <= x) i += 4;
if ((i + 2) * (i + 2) <= x) i += 2;
if ((i + 1) * (i + 1) <= x) i += 1;
return i;
}
Solutions so far have been mainly floating-point... and have also assumed that a divide instruction is available and fast.
Here's a simple straightforward routine that doesn't use FP or divide. Every line computes another bit in the result, except for the first if statement which speeds up the routine when the input is small.
constexpr unsigned int root(unsigned int x) {
unsigned int i = 0;
if (x >= 65536) {
if ((i + 32768) * (i + 32768) <= x) i += 32768;
if ((i + 16384) * (i + 16384) <= x) i += 16384;
if ((i + 8192) * (i + 8192) <= x) i += 8192;
if ((i + 4096) * (i + 4096) <= x) i += 4096;
if ((i + 2048) * (i + 2048) <= x) i += 2048;
if ((i + 1024) * (i + 1024) <= x) i += 1024;
if ((i + 512) * (i + 512) <= x) i += 512;
if ((i + 256) * (i + 256) <= x) i += 256;
}
if ((i + 128) * (i + 128) <= x) i += 128;
if ((i + 64) * (i + 64) <= x) i += 64;
if ((i + 32) * (i + 32) <= x) i += 32;
if ((i + 16) * (i + 16) <= x) i += 16;
if ((i + 8) * (i + 8) <= x) i += 8;
if ((i + 4) * (i + 4) <= x) i += 4;
if ((i + 2) * (i + 2) <= x) i += 2;
if ((i + 1) * (i + 1) <= x) i += 1;
return i;
}
它找到 2 和 4 的中点,即 = x ,然后再次找到 x 和 4 的中点(不包括此输入中的下限)。它一次又一次地重复这个步骤,直到得到完美的答案,即 sqrt(10)==3.16227766017 。它位于 b/w 2 和 4 。所有这些内置函数都是使用微积分、微分和积分创建的。
sqrt(); function Behind the scenes.
It always checks for the mid-points in a graph. Example: sqrt(16)=4; sqrt(4)=2;
Now if you give any input inside 16 or 4 like sqrt(10)==?
It finds the mid point of 2 and 4 i.e = x ,then again it finds the mid point of x and 4 (It excludes lower bound in this input). It repeats this step again and again until it gets the perfect answer i.e sqrt(10)==3.16227766017 .It lies b/w 2 and 4.All this in-built function are created using calculus,differentiation and Integration.
def mySqrt(x):
# return int(math.sqrt(x))
if x==0 or x==1:
return x
else:
start = 0
end = x
while (start <= end):
mid = int((start + end) / 2)
if (mid*mid == x):
return mid
elif (mid*mid < x):
start = mid + 1
ans = mid
else:
end = mid - 1
return ans
Implementation in Python: The floor of the root value is the output of this function. Example: The square root of 8 is 2.82842..., this function will give output '2'
def mySqrt(x):
# return int(math.sqrt(x))
if x==0 or x==1:
return x
else:
start = 0
end = x
while (start <= end):
mid = int((start + end) / 2)
if (mid*mid == x):
return mid
elif (mid*mid < x):
start = mid + 1
ans = mid
else:
end = mid - 1
return ans
double mysqrt(double n)
{
double x = n;
int it = 4;
if (n >= 90)
{
it = 6;
}
if (n >= 5000)
{
it = 8;
}
if (n >= 20000)
{
it = 10;
}
if (n >= 90000)
{
it = 11;
}
if (n >= 200000)
{
it = 12;
}
if (n >= 900000)
{
it = 13;
}
if (n >= 3000000)
{
it = 14;
}
if (n >= 10000000)
{
it = 15;
}
if (n >= 30000000)
{
it = 16;
}
if (n >= 100000000)
{
it = 17;
}
if (n >= 300000000)
{
it = 18;
}
if (n >= 1000000000)
{
it = 19;
}
for (int i = 0; i < it; i++)
{
x = 0.5*(x+n/x);
}
return x;
}
但最快的实现是:
float Q_rsqrt( float number )
{
long i;
float x2, y;
const float threehalfs = 1.5F;
x2 = number * 0.5F;
y = number;
i = * ( long * ) &y; // evil floating point bit level hacking
i = 0x5f3759df - ( i >> 1 ); // what the fuck?
y = * ( float * ) &i;
y = y * ( threehalfs - ( x2 * y * y ) ); // 1st iteration
// y = y * ( threehalfs - ( x2 * y * y ) ); // 2nd iteration, this can be removed
return y;
}
float mysqrt(float n) {return 1/Q_rsqrt(n);}
I'm making a sqrt function too, 100000000 iterations takes 14 seconds, still nothing compared to 1 seconds by sqrt
double mysqrt(double n)
{
double x = n;
int it = 4;
if (n >= 90)
{
it = 6;
}
if (n >= 5000)
{
it = 8;
}
if (n >= 20000)
{
it = 10;
}
if (n >= 90000)
{
it = 11;
}
if (n >= 200000)
{
it = 12;
}
if (n >= 900000)
{
it = 13;
}
if (n >= 3000000)
{
it = 14;
}
if (n >= 10000000)
{
it = 15;
}
if (n >= 30000000)
{
it = 16;
}
if (n >= 100000000)
{
it = 17;
}
if (n >= 300000000)
{
it = 18;
}
if (n >= 1000000000)
{
it = 19;
}
for (int i = 0; i < it; i++)
{
x = 0.5*(x+n/x);
}
return x;
}
But the fastest implementation is:
float Q_rsqrt( float number )
{
long i;
float x2, y;
const float threehalfs = 1.5F;
x2 = number * 0.5F;
y = number;
i = * ( long * ) &y; // evil floating point bit level hacking
i = 0x5f3759df - ( i >> 1 ); // what the fuck?
y = * ( float * ) &i;
y = y * ( threehalfs - ( x2 * y * y ) ); // 1st iteration
// y = y * ( threehalfs - ( x2 * y * y ) ); // 2nd iteration, this can be removed
return y;
}
float mysqrt(float n) {return 1/Q_rsqrt(n);}
public class SquareRootFunction {
public double squareRoot(double value,int decimalPoints)
{
int firstPart=0;
/*calculating the integer part*/
while(square(firstPart)<value)
{
firstPart++;
}
if(square(firstPart)==value)
return firstPart;
firstPart--;
/*calculating the decimal values*/
double precisionVal=0.1;
double[] decimalValues=new double[decimalPoints];
double secondPart=0;
for(int i=0;i<decimalPoints;i++)
{
while(square(firstPart+secondPart+decimalValues[i])<value)
{
decimalValues[i]+=precisionVal;
}
if(square(firstPart+secondPart+decimalValues[i])==value)
{
return (firstPart+secondPart+decimalValues[i]);
}
decimalValues[i]-=precisionVal;
secondPart+=decimalValues[i];
precisionVal*=0.1;
}
return(firstPart+secondPart);
}
public double square(double val)
{
return val*val;
}
}
MainApp.java
import java.util.Scanner;
public class MainApp {
public static void main(String[] args) {
double number;
double result;
int decimalPoints;
Scanner in = new Scanner(System.in);
SquareRootFunction sqrt=new SquareRootFunction();
System.out.println("Enter the number\n");
number=in.nextFloat();
System.out.println("Enter the decimal points\n");
decimalPoints=in.nextInt();
result=sqrt.squareRoot(number,decimalPoints);
System.out.println("The square root value is "+ result);
in.close();
}
}
To calculate the square root (without using inbuilt math.sqrt function):
SquareRootFunction.java
public class SquareRootFunction {
public double squareRoot(double value,int decimalPoints)
{
int firstPart=0;
/*calculating the integer part*/
while(square(firstPart)<value)
{
firstPart++;
}
if(square(firstPart)==value)
return firstPart;
firstPart--;
/*calculating the decimal values*/
double precisionVal=0.1;
double[] decimalValues=new double[decimalPoints];
double secondPart=0;
for(int i=0;i<decimalPoints;i++)
{
while(square(firstPart+secondPart+decimalValues[i])<value)
{
decimalValues[i]+=precisionVal;
}
if(square(firstPart+secondPart+decimalValues[i])==value)
{
return (firstPart+secondPart+decimalValues[i]);
}
decimalValues[i]-=precisionVal;
secondPart+=decimalValues[i];
precisionVal*=0.1;
}
return(firstPart+secondPart);
}
public double square(double val)
{
return val*val;
}
}
MainApp.java
import java.util.Scanner;
public class MainApp {
public static void main(String[] args) {
double number;
double result;
int decimalPoints;
Scanner in = new Scanner(System.in);
SquareRootFunction sqrt=new SquareRootFunction();
System.out.println("Enter the number\n");
number=in.nextFloat();
System.out.println("Enter the decimal points\n");
decimalPoints=in.nextInt();
result=sqrt.squareRoot(number,decimalPoints);
System.out.println("The square root value is "+ result);
in.close();
}
}
static float squareRoot(float n)
{
/*We are using n itself as
initial approximation This
can definitely be improved */
float x = n;
float y = 1;
// e decides the accuracy level
double e = 0.000001;
while(x - y > e)
{
x = (x + y)/2;
y = n/x;
}
return x;
}
static float squareRoot(float n)
{
/*We are using n itself as
initial approximation This
can definitely be improved */
float x = n;
float y = 1;
// e decides the accuracy level
double e = 0.000001;
while(x - y > e)
{
x = (x + y)/2;
y = n/x;
}
return x;
}
public class FindSquareRoot {
private static double newtonRaphson(double N, double X, double oldX) {
if(N <= 0) return 0;
if (Math.round(X) == Math.ceil(oldX))
return X;
return newtonRaphson(N, X - ((X * X) - N)/(2 * X), X);
}
//Driver method
public static void main (String[] args) {
System.out.println("Square root of 48.8: " + newtonRaphson(48.8, 10, 0));
}
}
So, just in case there are no specifications about whether not to use the in-built ceil or round function, here is a recursive approach in Java to finding the square root of an unsigned number using the Newton-Raphson method.
public class FindSquareRoot {
private static double newtonRaphson(double N, double X, double oldX) {
if(N <= 0) return 0;
if (Math.round(X) == Math.ceil(oldX))
return X;
return newtonRaphson(N, X - ((X * X) - N)/(2 * X), X);
}
//Driver method
public static void main (String[] args) {
System.out.println("Square root of 48.8: " + newtonRaphson(48.8, 10, 0));
}
}
long long int floorSqrt(long long int x)
{
long long r = 0;
while((long)(1<<r)*(long)(1<<r) <= x){
r++;
}
r--;
long long b = r -1;
long long ans = 1 << r;
while(b >= 0){
if(((long)(ans|1<<b)*(long)(ans|1<<b))<=x){
ans |= (1<<b);
}
b--;
}
return ans;
}
long long int floorSqrt(long long int x)
{
long long r = 0;
while((long)(1<<r)*(long)(1<<r) <= x){
r++;
}
r--;
long long b = r -1;
long long ans = 1 << r;
while(b >= 0){
if(((long)(ans|1<<b)*(long)(ans|1<<b))<=x){
ans |= (1<<b);
}
b--;
}
return ans;
}
package main
import (
"fmt"
)
func Sqrt(x float64) float64 {
z := 1.0 // initial guess to be 1
i := 0
for int(z*z) != int(x) { // until find the first approximation
// Newton root algorithm
z -= (z*z - x) / (2 * z)
i++
}
return z
}
func main() {
fmt.Println(Sqrt(8900009870))
}
遵循经典/常见的解决方案。
package main
import (
"fmt"
"math"
)
func Sqrt(num float64) float64 {
const DIFF = 0.0001 // To fix the precision
z := 1.0
for {
z1 := z - (((z * z) - num) / (2 * z))
// Return a result when the diff between the last execution
// and the current one is lass than the precision constant
if (math.Abs(z1 - z) < DIFF) {
break
}
z = z1
}
return z
}
func main() {
fmt.Println(Sqrt(94339))
}
package main
import (
"fmt"
)
func Sqrt(x float64) float64 {
z := 1.0 // initial guess to be 1
i := 0
for int(z*z) != int(x) { // until find the first approximation
// Newton root algorithm
z -= (z*z - x) / (2 * z)
i++
}
return z
}
func main() {
fmt.Println(Sqrt(8900009870))
}
Following a classic/common solution.
package main
import (
"fmt"
"math"
)
func Sqrt(num float64) float64 {
const DIFF = 0.0001 // To fix the precision
z := 1.0
for {
z1 := z - (((z * z) - num) / (2 * z))
// Return a result when the diff between the last execution
// and the current one is lass than the precision constant
if (math.Abs(z1 - z) < DIFF) {
break
}
z = z1
}
return z
}
func main() {
fmt.Println(Sqrt(94339))
}
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评论(15)
使用二分搜索和C++进行简单实现
请注意,
while
循环是最常见的使用二分搜索,但我个人更喜欢在处理十进制数字时使用for
,它可以节省一些特殊情况的处理,并从像1000
甚至 < code>500 (两者都会为几乎所有数字提供相同的结果,但只是为了安全起见)。编辑:查看这篇维基百科文章了解各种“特殊用途”方法专门用于计算平方根。
编辑2:应用@jorgbrown建议的更新来修复输入小于1的情况下的函数。此外,应用优化以使目标根的边界增加10倍
Simple implementation using Binary Search with C++
Note that
while
loop is most common with the binary search but Personally I prefer usingfor
when dealing with decimal numbers, it saves some special cases handling and gets pretty accurate result from small loops like that1000
or even500
(Both will give the same result for almost all numbers but just to be safe).Edit: Check out this Wikipedia article for various -special purpose- methods specialized in computing the square root.
Edit 2: Apply updates suggested by @jorgbrown to fix the function in case of input less than 1. Also, apply optimization to make the bounds off the target root by a factor of 10
在 Intel 硬件上,它通常在硬件 SQRT 指令之上实现。有些库只是直接使用该结果,有些库可能会对其进行几轮牛顿优化,以使其在极端情况下更加准确。
On Intel hardware, it's often implemented on top of the hardware SQRT instruction. Some libraries just use the result of that straight off, some may put it through a couple of rounds of Newton optimisation to make it more accurate in the corner cases.
FDLIBM(可自由分发的 LIBM)有一个非常好的 sqrt 文档版本。 e_sqrt.c。
有一种版本使用整数算术和一次修改一位的递推公式。
另一种方法使用牛顿法。它首先使用一些黑魔法和一个查找表来获取前 8 位,然后应用递归公式,
其中 x 是我们开始使用的数字。这是 Heron 方法的巴比伦方法。它可以追溯到公元一世纪的亚历山德拉英雄。
还有另一种方法称为快速平方根倒数或reciproot。它使用一些“邪恶的浮点位级黑客”来查找 1/sqrt(x) 的值。
i = 0x5f3759df - ( i >> 1 );
它使用尾数和指数来利用浮点数的二进制表示形式。如果我们的数字 x 是 (1+m) * 2^e,其中 m 是尾数,e 是指数,结果 y = 1/sqrt(x) = (1+n) * 2^f。取对数所以我们看到结果的指数部分是 -1/2 数字的指数。黑魔法基本上对指数进行按位移位,并对尾数使用线性近似。
一旦获得了良好的第一近似值,您就可以使用牛顿方法获得更好的结果,最后进行一些位级工作来修复最后一位数字。
The FDLIBM (Freely Distributable LIBM) has a quite nice documented version of sqrt. e_sqrt.c.
The have one version which uses integer arithmetic and a recurrence formula modifying one bit at a time.
Another method uses Newton's method. It starts with some black magic and a lookup table to get the first 8 bits and then applies the recurrence formula
where x is the number we started with. This is the Babylonian method of Heron's method. It dates back to Hero of Alexandra in the first centuary AD.
There is another method called the Fast inverse square root or reciproot. which uses some "evil floating point bit level hacking" to find the value of 1/sqrt(x).
i = 0x5f3759df - ( i >> 1 );
It exploits the binary representation of a float using the mantisse and exponent. If our number x is (1+m) * 2^e, where m is the mantissa and e the exponent and the result y = 1/sqrt(x) = (1+n) * 2^f. Taking logsSo we see the exponent part of the result is -1/2 the exponent of the number. The black magic basically does a bitwise shift on the exponent and uses a linear approximation on the mantissa.
Once you have a good first approximation you can use Newton's methods to get a better result and finally some bit level work to fix the last digit.
这是牛顿算法的实现,请参见 https://tour.golang.org/flowcontrol/8。
以下是魔线的数学解释。假设您要求多项式 $f(x) = x^2 - a$ 的根。通过牛顿法,您可以从初始猜测 $x_0 = 1$ 开始。下一个猜测是 $x_1 = x_0 - f(x_0)/f'(x_0)$,其中 $f'(x) = 2x$。因此,您的新猜测是
$x_1 = x_0 - (x_0^2 - a)/2x_0$
This is an implementation of Newton's algorithm, see https://tour.golang.org/flowcontrol/8.
The following is a mathematical explanation of the magic line. Suppose you want to find the root of the polynomial $f(x) = x^2 - a$. By Newton's method, you could start with an initial guess $x_0 = 1$. The next guess is $x_1 = x_0 - f(x_0)/f'(x_0)$, where $f'(x) = 2x$. Therefore, your new guess is
$x_1 = x_0 - (x_0^2 - a)/2x_0$
迄今为止的解决方案主要是浮点......并且还假设除法指令可用且快速。
这是一个简单直接的例程,不使用 FP 或除法。每行都会计算结果中的另一位,但第一个 if 语句除外,该语句在输入较小时会加速例程。
Solutions so far have been mainly floating-point... and have also assumed that a divide instruction is available and fast.
Here's a simple straightforward routine that doesn't use FP or divide. Every line computes another bit in the result, except for the first if statement which speeds up the routine when the input is small.
开方();幕后功能。
它始终检查图表中的中点。示例:sqrt(16)=4;开方(4)=2;
现在,如果您在 16 或 4 内输入任何输入,例如 sqrt(10)==?
它找到 2 和 4 的中点,即 = x ,然后再次找到 x 和 4 的中点(不包括此输入中的下限)。它一次又一次地重复这个步骤,直到得到完美的答案,即 sqrt(10)==3.16227766017 。它位于 b/w 2 和 4 。所有这些内置函数都是使用微积分、微分和积分创建的。
sqrt(); function Behind the scenes.
It always checks for the mid-points in a graph. Example: sqrt(16)=4; sqrt(4)=2;
Now if you give any input inside 16 or 4 like sqrt(10)==?
It finds the mid point of 2 and 4 i.e = x ,then again it finds the mid point of x and 4 (It excludes lower bound in this input). It repeats this step again and again until it gets the perfect answer i.e sqrt(10)==3.16227766017 .It lies b/w 2 and 4.All this in-built function are created using calculus,differentiation and Integration.
Python 实现:
根值的下限是该函数的输出。
示例:8 的平方根是 2.82842...,该函数将给出输出“2”
Implementation in Python:
The floor of the root value is the output of this function.
Example: The square root of 8 is 2.82842..., this function will give output '2'
我也在制作一个 sqrt 函数,100000000 次迭代需要 14 秒,与 sqrt 的 1 秒相比仍然没有什么,
但最快的实现是:
I'm making a sqrt function too, 100000000 iterations takes 14 seconds, still nothing compared to 1 seconds by sqrt
But the fastest implementation is:
计算平方根(不使用内置 math.sqrt 函数):
SquareRootFunction.java
MainApp.java
To calculate the square root (without using inbuilt math.sqrt function):
SquareRootFunction.java
MainApp.java
有一种叫做巴比伦方法的东西。
欲了解更多信息链接:https://www.geeksforgeeks.org/完全平方的平方根/
there is some thing called Babylonian method.
for more information link: https://www.geeksforgeeks.org/square-root-of-a-perfect-square/
因此,以防万一没有关于是否不使用内置 ceil 或 round 函数的规范,这里有一种 Java 中的递归方法,使用 Newton-Raphson 方法查找无符号数的平方根。
So, just in case there are no specifications about whether not to use the in-built ceil or round function, here is a recursive approach in Java to finding the square root of an unsigned number using the Newton-Raphson method.
用法: root(number,root,深度)
示例: root(16,2) == sqrt(16) == 4示例: root(16,2,2) == sqrt(sqrt(16)) == 2
示例:root(64,3) == 4
C#实现:
用法:Sqrt(Number,深度)
示例:Sqrt(16) == 4
示例:Sqrt(8,2) == sqrt(sqrt(8))
作者:Imk0tter
Usage: root(number,root,depth)
Example: root(16,2) == sqrt(16) == 4
Example: root(16,2,2) == sqrt(sqrt(16)) == 2
Example: root(64,3) == 4
Implementation in C#:
Usage: Sqrt(Number,depth)
Example: Sqrt(16) == 4
Example: Sqrt(8,2) == sqrt(sqrt(8))
By: Imk0tter
遵循我在 Golang 中的解决方案。
遵循经典/常见的解决方案。
如需了解更多信息,请查看此处
Following my solution in Golang.
Following a classic/common solution.
For further information check here