hdu 2767(强连通分量)

本文探讨了如何通过证明一系列等价性来确定多个陈述的等效性,包括矩阵运算、线性代数概念及证明技巧。通过实例分析,展示了简化证明过程的方法,以及在特定任务中所需额外证明步骤的数量。

Proving Equivalences

Time Limit: 4000/2000 MS (Java/Others)    Memory Limit: 32768/32768 K (Java/Others)
Total Submission(s): 584    Accepted Submission(s): 238


Problem Description
Consider the following exercise, found in a generic linear algebra textbook.

Let A be an n × n matrix. Prove that the following statements are equivalent:

1. A is invertible.
2. Ax = b has exactly one solution for every n × 1 matrix b.
3. Ax = b is consistent for every n × 1 matrix b.
4. Ax = 0 has only the trivial solution x = 0. 

The typical way to solve such an exercise is to show a series of implications. For instance, one can proceed by showing that (a) implies (b), that (b) implies (c), that (c) implies (d), and finally that (d) implies (a). These four implications show that the four statements are equivalent.

Another way would be to show that (a) is equivalent to (b) (by proving that (a) implies (b) and that (b) implies (a)), that (b) is equivalent to (c), and that (c) is equivalent to (d). However, this way requires proving six implications, which is clearly a lot more work than just proving four implications!

I have been given some similar tasks, and have already started proving some implications. Now I wonder, how many more implications do I have to prove? Can you help me determine this?
 

Input
On the first line one positive number: the number of testcases, at most 100. After that per testcase:

* One line containing two integers n (1 ≤ n ≤ 20000) and m (0 ≤ m ≤ 50000): the number of statements and the number of implications that have already been proved.
* m lines with two integers s1 and s2 (1 ≤ s1, s2 ≤ n and s1 ≠ s2) each, indicating that it has been proved that statement s1 implies statement s2.
 

Output
Per testcase:

* One line with the minimum number of additional implications that need to be proved in order to prove that all statements are equivalent.
 

Sample Input
  
2 4 0 3 2 1 2 1 3
 

Sample Output
  
4 2
 

Source
 

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lcy
 
分析:模板题,可以用来测模板。。。注意缩成1个点的情况特殊判掉
代码:
#include<cstdio>
#define min(a,b) a<b?a:b
#define max(a,b) a>b?a:b
using namespace std;
const int mm=50005;
const int mn=20002;
int s[mm],t[mm],p[mm];
int id[mn],q[mn],dfn[mn],low[mn],h[mn];
int i,j,k,n,m,qe,tsp,cnt,cas,sum1,sum2;
void dfs(int u)
{
    int i,v;
    dfn[u]=low[u]=++tsp;
    q[qe++]=u;
    for(i=h[u];i>=0;i=p[i])
        if(!dfn[v=t[i]])
            dfs(v),low[u]=min(low[u],low[v]);
        else if(id[v]<0)low[u]=min(low[u],dfn[v]);
    if(low[u]==dfn[u])
    {
        id[u]=++cnt;
        while((v=q[--qe])!=u)id[v]=cnt;
    }
}
void tarjan()
{
    int i;
    for(qe=tsp=cnt=i=0;i<=n;++i)dfn[i]=0,id[i]=-1;
    for(i=1;i<=n;++i)
        if(!dfn[i])dfs(i);
}
int main()
{
    scanf("%d",&cas);
    while(cas--)
    {
        scanf("%d%d",&n,&m);
        for(i=0;i<=n;++i)h[i]=-1;
        for(k=0;k<m;++k)
        {
            scanf("%d%d",&i,&j);
            s[k]=i,t[k]=j,p[k]=h[i],h[i]=k;
        }
        tarjan();
        if(cnt==1)
        {
            printf("0\n");
            continue;
        }
        for(i=1;i<=cnt;++i)p[i]=q[i]=0;
        for(k=0;k<m;++k)
            if((i=id[s[k]])!=(j=id[t[k]]))
                ++p[i],++q[j];
        for(sum1=sum2=0,i=1;i<=cnt;++i)
            sum1+=(p[i]==0),sum2+=(q[i]==0);
        printf("%d\n",max(sum1,sum2));
    }
    return 0;
}


### 使用Tarjan算法计算强连通分量数量 #### 算法原理 Tarjan算法通过深度优先搜索(DFS)遍历有向图中的节点,记录访问顺序和低链值(low-link value),从而识别出所有的强连通分量。当发现一个节点的访问序号等于其最低可达节点编号时,表明找到了一个新的强连通分量。 #### 时间复杂度分析 该方法的时间效率取决于存储结构的选择。对于采用邻接表表示的稀疏图而言,整体性能更优,能够在线性时间内完成操作,即O(n+m)[^4];而针对稠密图则可能退化至平方级别(O())。 #### Python代码实现 下面给出一段Python程序用于演示如何基于NetworkX库构建并处理带权无环图(DAG),进而求解其中存在的全部SCC及其总数: ```python import networkx as nx def tarjan_scc(graph): index_counter = [0] stack = [] lowlinks = {} index = {} result = [] def strongconnect(node): # Set the depth index for this node to be the next available incrementing counter. index[node] = index_counter[0] lowlinks[node] = index_counter[0] index_counter[0] += 1 stack.append(node) try: successors = graph.successors(node) except AttributeError: successors = graph.neighbors(node) for successor in successors: if successor not in lowlinks: strongconnect(successor) lowlinks[node] = min(lowlinks[node], lowlinks[successor]) elif successor in stack: lowlinks[node] = min(lowlinks[node], index[successor]) if lowlinks[node] == index[node]: scc = set() while True: current_node = stack.pop() scc.add(current_node) if current_node == node: break result.append(scc) for node in graph.nodes(): if node not in lowlinks: strongconnect(node) return result if __name__ == "__main__": G = nx.DiGraph() # Create a directed graph object using NetworkX library edges_list = [(1, 2),(2, 3),(3, 1)] # Define edge list according to sample input data from hdu1269 problem statement[^5] G.add_edges_from(edges_list) components = tarjan_scc(G) print(f"Number of Strongly Connected Components found: {len(components)}") ``` 此段脚本定义了一个名为`tarjan_scc()`的功能函数接收网络对象作为参数,并返回由集合组成的列表形式的结果集,每个子集中包含了构成单个SCC的所有顶点。最后部分展示了创建测试用DAG实例的过程以及调用上述功能获取最终答案的方式。
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