用c++找出质数最快的算法是什么?我已经使用了sieve的算法,但我仍然希望它更快!
当前回答
#include<stdio.h>
main()
{
long long unsigned x,y,b,z,e,r,c;
scanf("%llu",&x);
if(x<2)return 0;
scanf("%llu",&y);
if(y<x)return 0;
if(x==2)printf("|2");
if(x%2==0)x+=1;
if(y%2==0)y-=1;
for(b=x;b<=y;b+=2)
{
z=b;e=0;
for(c=2;c*c<=z;c++)
{
if(z%c==0)e++;
if(e>0)z=3;
}
if(e==0)
{
printf("|%llu",z);
r+=1;
}
}
printf("|\n%llu outputs...\n",r);
scanf("%llu",&r);
}
其他回答
i wrote it today in C,compiled with tcc, figured out during preparation of compititive exams several years back. don't know if anyone already have wrote it alredy. it really fast(but you should decide whether it is fast or not). took one or two minuts to findout about 1,00,004 prime numbers between 10 and 1,00,00,000 on i7 processor with average 32% CPU use. as you know, only those can be prime which have last digit either 1,3,7 or 9 and to check if that number is prime or not, you have to divide that number by previously found prime numbers only. so first take group of four number = {1,3,7,9}, test it by dividing by known prime numbers, if reminder is non zero then number is prime, add it to prime number array. then add 10 to group so it becomes {11,13,17,19} and repeat the process.
#include <stdio.h>
int main() {
int nums[4]={1,3,7,9};
int primes[100000];
primes[0]=2;
primes[1]=3;
primes[2]=5;
primes[3]=7;
int found = 4;
int got = 1;
int m=0;
int upto = 1000000;
for(int i=0;i<upto;i++){
//printf("iteration number: %d\n",i);
for(int j=0;j<4;j++){
m = nums[j]+10;
//printf("m = %d\n",m);
nums[j] = m;
got = 1;
for(int k=0;k<found;k++){
//printf("testing with %d\n",primes[k]);
if(m%primes[k]==0){
got = 0;
//printf("%d failed for %d\n",m,primes[k]);
break;
}
}
if(got==1){
//printf("got new prime: %d\n",m);
primes[found]= m;
found++;
}
}
}
printf("found total %d prime numbers between 1 and %d",found,upto*10);
return 0;
}
一个非常快速的Atkin Sieve的实现是Dan Bernstein的primegen。这个筛子比埃拉托色尼的筛子更有效率。他的页面有一些基准测试信息。
这是我一直在玩的埃拉托色尼筛子的Python实现。
def eratosthenes(maximum: int) -> list[int | None]:
"""
Find all the prime numbers between 2 and `maximum`.
Args:
maximum: The maximum number to check.
Returns:
A list of primes between 2 and `maximum`.
"""
if maximum < 2:
return []
# Discard even numbers by default.
sequence = dict.fromkeys(range(3, maximum+1, 2), True)
for num, is_prime in sequence.items():
# Already filtered, let's skip it.
if not is_prime:
continue
# Avoid marking the same number twice.
for num2 in range(num ** 2, maximum+1, num):
# Here, `num2` might contain an even number - skip it.
if num2 in sequence:
sequence[num2] = False
# Re-add 2 as prime and filter out the composite numbers.
return [2] + [num for num, is_prime in sequence.items() if is_prime]
在一台简陋的三星Galaxy A40上,该代码大约需要16秒才能输入10000000个数字。
欢迎提出建议!
我会让你决定这是不是最快的。
using System;
namespace PrimeNumbers
{
public static class Program
{
static int primesCount = 0;
public static void Main()
{
DateTime startingTime = DateTime.Now;
RangePrime(1,1000000);
DateTime endingTime = DateTime.Now;
TimeSpan span = endingTime - startingTime;
Console.WriteLine("span = {0}", span.TotalSeconds);
}
public static void RangePrime(int start, int end)
{
for (int i = start; i != end+1; i++)
{
bool isPrime = IsPrime(i);
if(isPrime)
{
primesCount++;
Console.WriteLine("number = {0}", i);
}
}
Console.WriteLine("primes count = {0}",primesCount);
}
public static bool IsPrime(int ToCheck)
{
if (ToCheck == 2) return true;
if (ToCheck < 2) return false;
if (IsOdd(ToCheck))
{
for (int i = 3; i <= (ToCheck / 3); i += 2)
{
if (ToCheck % i == 0) return false;
}
return true;
}
else return false; // even numbers(excluding 2) are composite
}
public static bool IsOdd(int ToCheck)
{
return ((ToCheck % 2 != 0) ? true : false);
}
}
}
在我使用2.40 GHz处理器的酷睿2 Duo笔记本电脑上,查找并打印1到1,000,000范围内的质数大约需要82秒。它找到了78,498个质数。
另一个Python实现比死亡面具推销员的答案更直接,也更快:
import numpy as np
def prime_numbers(limit: int) -> list[int]:
"""Provide a list of all prime numbers <= the limit."""
is_prime = np.full((limit + 1, ), True)
is_prime[0:2] = False
for n in range(2, limit + 1):
if is_prime[n]:
is_prime[n**2::n] = False
return list(np.where(is_prime)[0])
你可以进一步优化,例如,排除2,或者硬编码更多质数,但我想保持简单。
*示例运行时比较(注意:我使用了其他实现的优化形式,见我的评论):