洛谷 P2863 tarjan求强连通分量个数

本文介绍了一个使用Tarjan算法解决有向图中强连通分量问题的C语言实现。通过深度优先搜索(DFS)和回溯机制,算法能够高效地找出所有大小大于1的强连通分量。

题意:给定一个n个点,m条边的有向图,求大小大于1的强连通分量个数

tarjan模板

#include<stdio.h>
#include<string.h>
#define maxn 10010
#define maxm 50010

int time = 0, top = 0;
int l = 0;
int last[maxn], dfn[maxn], low[maxn], belong[maxn];
int size[maxn], z[maxn];
int vis[maxn];
int other[maxm], pre[maxm];

int min(int x, int y){
    return(x < y ? x : y);
}

void connect(int x, int y){
    l++;
    pre[l] = last[x];
    last[x] = l;
    other[l] = y;
}

void dfs(int x) {
    int p, q;
    int cur;

    time++;
    vis[x] = 1;
    dfn[x] = time;
    low[x] = time;
    top++;
    z[top] = x;
    //
    q = last[x];
    while (q != 0) {
        p = other[q];
        if (dfn[p] == 0) {
            dfs(p);
            low[x] = min(low[x], low[p]);
        } else if (vis[p] == 1) low[x] = min(low[x], dfn[p]);
        q = pre[q];
    }
//
    if (dfn[x] == low[x]) {
        cur = -1;
        while (cur != x) {
            cur = z[top];
            belong[cur] = x;
            vis[cur] = 0;
            size[belong[cur]]++;
            top--;
        }
    }

}

int main() {
    int i;
    int n, m, x, y;
    int ans = 0;

    memset(dfn, 0, sizeof(dfn));
    memset(vis, 0, sizeof(vis));
    memset(size, 0, sizeof(size));

    scanf("%d %d", &n, &m);
    for (i = 1; i <= m; i++) {
        scanf("%d %d", &x, &y);
        connect(x, y);
    }
    for (i = 1; i <= n; i++)
        if (dfn[i] == 0) dfs(i);
    for (i = 1; i <= n; i++)
        if ((belong[i] == i) && (size[belong[i]] > 1))  ans++;
    printf("%d\n", ans);
    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];而针对稠密则可能退化至平方级别(O(n²))。 #### 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(DAG),进而解其中存在的全部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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