JZOJ 5459. 【NOIP2017提高A组冲刺11.7】密室

5459. 【NOIP2017提高A组冲刺11.7】密室 
(File IO): input:room.in output:room.out

Time Limits: 1000 ms  Memory Limits: 524288 KB  Detailed Limits  

Goto ProblemSet

Description

小X 正困在一个密室里，他希望尽快逃出密室。
密室中有N 个房间，初始时，小X 在1 号房间，而出口在N 号房间。
密室的每一个房间中可能有着一些钥匙和一些传送门，一个传送门会单向地创造一条从房间X 到房间Y 的通道。另外，想要通过某个传送门，就必须具备一些种类的钥匙（每种钥匙都要有才能通过）。幸运的是，钥匙在打开传送门的封印后，并不会消失。
然而，通过密室的传送门需要耗费大量的时间，因此，小X 希望通过尽可能少的传送门到达出口，你能告诉小X 这个数值吗？
另外，小X 有可能不能逃出这个密室，如果是这样，请输出"No Solution"。

 

Input

第一行三个整数N,M,K，分别表示房间的数量、传送门的数量以及钥匙的种类数。
接下来N 行，每行K 个0 或1，若第i 个数为1，则表示该房间内有第i 种钥匙，若第i 个数为0，则表示该房间内没有第i 种钥匙。
接下来M 行，每行先读入两个整数X,Y，表示该传送门是建立在X 号房间，通向Y 号房间的，再读入K 个0 或1，若第i 个数为1，则表示通过该传送门需要i 种钥匙，若第i 个数为0，则表示通过该传送门不需要第i 种钥匙。

Output

输出一行一个“No Solution”，或一个整数，表示最少通过的传送门数。

 

Sample Input

3 3 2
1 0
0 1
0 0
1 3 1 1
1 2 1 0
2 3 1 1

Sample Output

2

 

Data Constraint

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" 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做法： 将K状态压缩后直接bfs即可

代码如下：

#include <cstdio>
#include <iostream>
#include <cstring>
#include <cmath>
#include <queue>
#include <algorithm>
#define N 5007
using namespace std;
int n, m, k, pre[N], dis[N][], ls[N];
int mi[], tot;
struct edge
{
    int to, next, pre;
}e[N * ];
int list[N * ][];

void add(int x, int y, int z)
{
    e[++tot].to = y;
    e[tot].next = ls[x];
    e[tot].pre = z;
    ls[x] = tot;
}

void init()
{
    scanf("%d%d%d", &n, &m, &k);
    for (int i = ; i <= n; i++)
    {
        int x;
        pre[i] = ;
        for (int j = ; j <= k; j++)
        {
            scanf("%d", &x);
            pre[i] += x * mi[j];
        }
    }

    for (int i = ; i <= m; i++)
    {
        int x, y, z;
        scanf("%d%d", &x, &y);
        int p = ;
        for (int j = ; j <= k; j++)
            scanf("%d", &z), p += mi[j] * z;
        add(x, y, p);
    }
}

void pre_work()
{
    mi[] = ;
    for (int i = ; i <= ; i++)
        mi[i] = mi[i - ] * ;

} 

void work()
{
    memset(dis, 0x7f7f7f7f, sizeof(dis));
    dis[][pre[]] = ;
    list[][] = ;
    list[][] = pre[];
    int head = , tail = ;
    while (head < tail)
    {
        head++;
        int now = list[head][];
        int p = list[head][];
        for (int i = ls[now]; i; i = e[i].next)
        {
            if ((p | e[i].pre) != p)    continue;
            int net = p | pre[e[i].to];
            if (dis[e[i].to][net] == 0x7f7f7f7f)
            {
                dis[e[i].to][net] = dis[now][p] + ;
                list[++tail][] = e[i].to;
                list[tail][] = net;
            }
        }
    }
    int ans = 0x7f7f7f7f;
    for (int i = ; i <= mi[k + ] - ; i++)
        ans = min(ans, dis[n][i]);
    if (ans != 0x7f7f7f7f)    printf("%d", ans);
    else printf("No Solution");
}

int main()
{
    //freopen("room.in", "r", stdin);
    //freopen("room.out", "w", stdout);
    pre_work();
    init();
    work();
}