HDU 6030 Happy Necklace【矩阵快速幂】

本文探讨了HappyNecklace问题,即寻找能使女友满意的项链组合方式,要求任意质数长度的连续子串中红珠不少于蓝珠。采用矩阵快速幂方法解决大规模数据输入问题。

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Happy Necklace

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 131072/131072 K (Java/Others)
Total Submission(s): 477    Accepted Submission(s): 198


Problem Description
Little Q wants to buy a necklace for his girlfriend. Necklaces are single strings composed of multiple red and blue beads.
Little Q desperately wants to impress his girlfriend, he knows that she will like the necklace only if for every prime length continuous subsequence in the necklace, the number of red beads is not less than the number of blue beads.
Now Little Q wants to buy a necklace with exactly  n  beads. He wants to know the number of different necklaces that can make his girlfriend happy. Please write a program to help Little Q. Since the answer may be very large, please print the answer modulo  109+7 .
Note: The necklace is a single string,  {not a circle}.
 

Input
The first line of the input contains an integer  T(1T10000) , denoting the number of test cases.
For each test case, there is a single line containing an integer  n(2n1018) , denoting the number of beads on the necklace.
 

Output
For each test case, print a single line containing a single integer, denoting the answer modulo  109+7 .
 

Sample Input
  
2 2 3
 

Sample Output
  
3 4
 

Source


因为2是最小的素数,考虑长度为2的子串。红色为A,蓝色为B,则只有AA,AB,BA三种情况。对每种情况,在后面加上A或B,AA可以形成AA,AB,AB可以形成BA,BA可以形成AA。通过这个递推扩展到长度为n的情况,用矩阵快速幂加速即可。矩阵为:


初始情况下,AA,AB,BA都有可能,因此最后将矩阵中的所有数字相加就是答案。


注意n为10的18次方会爆int所以在矩阵快速幂的时候要用long longMatrix quickpow(Matrix A,ll k)】


#include<iostream>
#include<algorithm>
#include<cmath>
#include<cstdio>
#include<cstring>

#define INF 0x3f3f3f3f

#define mod 1000000007

using namespace std;

typedef long long ll;
const int maxn = 100010;

ll n;

struct Matrix {
	ll a[5][5];
};


Matrix mul(Matrix x, Matrix y)
{
	Matrix temp;
	for (int i = 1; i <= 3; i++)
		for (int j = 1; j <= 3; j++) temp.a[i][j] = 0;

	for (int i = 1; i <= 3; i++)
	{
		for (int j = 1; j <= 3; j++)
		{
			ll sum = 0;
			for (int k = 1; k <= 3; k++)
			{
				sum = (sum + x.a[i][k] * y.a[k][j] % mod) % mod;
			}
			temp.a[i][j] = sum;
		}
	}
	return temp;
}

Matrix quickpow(Matrix A,ll k)
{
	Matrix res;
	res.a[1][1] = 1; res.a[1][2] = 0; res.a[1][3] = 0;
	res.a[2][1] = 0; res.a[2][2] = 1; res.a[2][3] = 0;
	res.a[3][1] = 0; res.a[3][2] = 0; res.a[3][3] = 1;
	while (k)
	{
		if (k & 1) res = mul(res, A);
		A = mul(A, A);
		k >>= 1;
	}
	return res;
}

int main()
{
	int t;
	scanf("%d", &t);
	while (t--)
	{
		scanf("%lld", &n);
		if (n == 2)
		{
			printf("3\n");
			continue;
		}
		Matrix A;
		A.a[1][1] = 1; A.a[1][2] = 0; A.a[1][3] = 1;
		A.a[2][1] = 1; A.a[2][2] = 0; A.a[2][3] = 0;
		A.a[3][1] = 0; A.a[3][2] = 1; A.a[3][3] = 0;
		Matrix res = quickpow(A, n - 2);
		ll x = (res.a[1][1] + res.a[1][2] + res.a[1][3]) % mod;
		ll y = (res.a[2][1] + res.a[2][2] + res.a[2][3]) % mod;
		ll z = (res.a[3][1] + res.a[3][2] + res.a[3][3]) % mod;
		printf("%lld\n", (x + y + z) % mod);
	}
}


1018
101Matrix quickpow(Matrix A,ll k)1018101810181018101810181018101810

10 18
10 18
10 18
10 18
### HDU 2544 题目分析 HDU 2544 是关于最短路径的经典问题,可以通过多种方法解决,其中包括基于邻接矩阵的 Floyd-Warshall 算法。以下是针对该问题的具体解答。 --- #### 基于邻接矩阵的 Floyd-Warshall 实现 Floyd-Warshall 算法是一种动态规划算法,适用于计算任意两点之间的最短路径。它的时间复杂度为 \( O(V^3) \),其中 \( V \) 表示节点的数量。对于本题中的数据规模 (\( N \leq 100 \)),此算法完全适用。 下面是具体的实现方式: ```cpp #include <iostream> #include <algorithm> using namespace std; const int INF = 0x3f3f3f3f; int dist[105][105]; int n, m; void floyd() { for (int k = 1; k <= n; ++k) { // 中间节点 for (int i = 1; i <= n; ++i) { // 起始节点 for (int j = 1; j <= n; ++j) { // 结束节点 if (dist[i][k] != INF && dist[k][j] != INF) { dist[i][j] = min(dist[i][j], dist[i][k] + dist[k][j]); } } } } } int main() { while (cin >> n >> m && (n || m)) { // 初始化邻接矩阵 for (int i = 1; i <= n; ++i) { for (int j = 1; j <= n; ++j) { if (i == j) dist[i][j] = 0; else dist[i][j] = INF; } } // 输入边的信息并更新邻接矩阵 for (int i = 0; i < m; ++i) { int u, v, w; cin >> u >> v >> w; dist[u][v] = min(dist[u][v], w); dist[v][u] = min(dist[v][u], w); // 如果是有向图,则去掉这一行 } // 执行 Floyd-Warshall 算法 floyd(); // 输出起点到终点的最短距离 cout << (dist[1][n] >= INF ? -1 : dist[1][n]) << endl; } return 0; } ``` --- #### 关键点解析 1. **邻接矩阵初始化** 使用二维数组 `dist` 存储每一对节点间的最小距离。初始状态下,设所有节点对的距离为无穷大 (`INF`),而同一节点自身的距离为零[^4]。 2. **输入处理** 对于每条边 `(u, v)` 和权重 `w`,将其存储至邻接矩阵中,并取较小值以防止重边的影响[^4]。 3. **核心逻辑** Floyd-Warshall 的核心在于三重循环:依次尝试通过中间节点优化其他两节点间的距离关系。具体而言,若从节点 \( i \) 到 \( j \) 可经由 \( k \) 达成更优解,则更新对应位置的值[^4]。 4. **边界条件** 若最终得到的结果仍为无穷大(即无法连通),则返回 `-1`;否则输出实际距离[^4]。 --- #### 性能评估 由于题目限定 \( N \leq 100 \),因此 \( O(N^3) \) 的时间复杂度完全可以接受。此外,空间需求也较低,适合此类场景下的应用。 ---
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