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本文深入探讨了如何通过特定算法计算所有小于2^k(k<=63)的Mersenne复合数,并揭示了它们背后的数学原理。通过将每个复合数分解为质因数并展示其形成过程,读者将领略到数学与计算机科学的完美结合。
  1. Mersenne Composite N

Constraints

Time Limit: 1 secs, Memory Limit: 32 MB

Description

One of the world-wide cooperative computing tasks is the “Grand Internet Mersenne Prime Search” – GIMPS – striving to find ever-larger prime numbers by examining a particular category of such numbers.
A Mersenne number is defined as a number of the form (2p–1), where p is a prime number – a number divisible only by one and itself. (A number that can be divided by numbers other than itself and one are called “composite” numbers, and each of these can be uniquely represented by the prime numbers that can be multiplied together to generate the composite number — referred to as its prime factors.)
Initially it looks as though the Mersenne numbers are all primes.
Prime Corresponding Mersenne Number
2 4–1 = 3 – prime
3 8–1 = 7 – prime
5 32–1 = 31 – prime
7 128–1 = 127 – prime
If, however, we are having a “Grand Internet” search, that must not be the case.
Where k is an input parameter, compute all the Mersenne composite numbers less than 2k – where k <= 63 (that is, it will fit in a 64-bit signed integer on the computer). In Java, the “long” data type is a signed 64 bit integer. Under gcc and g++ (C and C++ in the programming contest environment), the “long long” data type is a signed 64 bit integer.
Input

Input is from file a. in. It contains a single number, without leading or trailing blanks, giving the value of k. As promised, k <= 63.

Output

One line per Mersenne composite number giving first the prime factors (in increasing order) separate by asterisks, an equal sign, the Mersenne number itself, an equal sign, and then the explicit statement of the Mersenne number, as shown in the sample output. Use exactly this format. Note that all separating white space fields consist of one blank.

Sample Input

31
Sample Output

23 * 89 = 2047 = ( 2 ^ 11 ) - 1
47 * 178481 = 8388607 = ( 2 ^ 23 ) - 1
233 * 1103 * 2089 = 536870911 = ( 2 ^ 29 ) - 1

#include <iostream>
#include <cmath>
#include <string>
#include <algorithm>
using namespace std;

bool isPrime(int a) {
    for (int i = 2; i*i <= a; i++) {
        if (a % i == 0) return false;
    }
    return true;
}

int main(int argc, char **argv)
{
    int k;
    cin >> k;
    int i;
    for (i = 3; i < k; i += 2) {
        if (!isPrime(i)) continue;
        //如果是素数,对其进行分解
        long long bigNum = (long long) pow(2.0, i) - 1;
        long long temp;
        temp  = bigNum;
        bool first = false, isCome = false;
        for (long long j = 3; j * j <= bigNum; j += 2) {
            if (bigNum % j == 0 && isPrime(j)) { //不是素数
                if (first == false)  cout << j;
                else cout << " * " << j;
                bigNum /= j;
                j = 3;
                first = true;
                isCome = true;
            }
        }

        if (isCome) {
            cout << " * " << bigNum;
            cout << " = " << temp  << " = ( 2 ^ " <<  i << " ) - 1" << endl;
        }

    }
    return 0;
}
基于可靠性评估序贯蒙特卡洛模拟法的配电网可靠性评估研究(Matlab代码实现)内容概要:本文围绕“基于可靠性评估序贯蒙特卡洛模拟法的配电网可靠性评估研究”,介绍了利用Matlab代码实现配电网可靠性的仿真分析方法。重点采用序贯蒙特卡洛模拟法对配电网进行长时间段的状态抽样与统计,通过模拟系统元件的故障与修复过程,评估配电网的关键可靠性指标,如系统停电频率、停电持续时间、负荷点可靠性等。该方法能够有效处理复杂网络结构与设备时序特性,提升评估精度,适用于含分布式电源、电动汽车等新型负荷接入的现代配电网。文中提供了完整的Matlab实现代码与案例分析,便于复现和扩展应用。; 适合人群:具备电力系统基础知识和Matlab编程能力的高校研究生、科研人员及电力行业技术人员,尤其适合从事配电网规划、运行与可靠性分析相关工作的人员; 使用场景及目标:①掌握序贯蒙特卡洛模拟法在电力系统可靠性评估中的基本原理与实现流程;②学习如何通过Matlab构建配电网仿真模型并进行状态转移模拟;③应用于含新能源接入的复杂配电网可靠性定量评估与优化设计; 阅读建议:建议结合文中提供的Matlab代码逐段调试运行,理解状态抽样、故障判断、修复逻辑及指标统计的具体实现方式,同时可扩展至不同网络结构或加入更多不确定性因素进行深化研究。
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