UVALive 7662 Perfect Matchings

7662 Perfect Matchings

Given a graph G = (V, E), a matching in G is a set of pairwise non-adjacent edges; that is, no two edges
share a common vertex. A perfect matching is a matching which matches all vertices of the graph. That
is, every vertex of the graph is incident to exactly one edge of the matching. Finally, a bipartite graph
G, is a graph whose vertices can be divided into two disjoint sets U and V such that every edge connects
a vertex in U to one in V . In this task, please determine whether the number of perfect matchings of
a bipartite graph is odd or even. Note that counting the number of perfect matchings on a bipartite
graph has been proven to be #P − complete. Thus, it would be better to avoid actually counting the
number of perfect matchings.
Technical Specification
• The number of bipartite graphs is n, n ≤ 20.
• Maximum number of vertices in a bipartite graph is v, v ≤ 20 and v is an even number.
• Maximum number of edges in a bipartite graph is e, e ≤ 46.
Input
The first line consists of a number n, which is the number of bipartite graphs to follow. The first line
of each bipartite graph G contains two integers v and e, separated by a blank space. The v vertices are
naturally numbered from 1, … , v. One partition of the bipartite graph G contains vertices 1, … , v/2,
while the other partition contains vertices v/2 + 1, … , v. The next e lines each contains two integers
vi and vj , separated by a blank space, indicating an edge going from vertex vi to vertex vj , where vi
and vj belong to distinct partitions in the bipartite graph G.
Output
Please output ‘0’ if there is an odd number of perfect matchings on the input graph, and output ‘1’
otherwise.

Sample Input
2
10 5
1 6
2 7
3 8
4 9
5 10
10 7
1 6
1 7
2 6
2 7
3 8
4 9
5 10

Sample Output
0
1

题意:给一个二分图(u,v)(u和v数量相同),问从u一个一个往v映射,最多有多少种不同的映射(每个u,v一定有且只有一条边)

思路:暴力搜索即可。
因为给的是二分图,我们可以从 u 的第一个点开始dfs,若找到了与之对应的 v 点,则往下搜 u 的第二个点……当搜完所有点时,ans++

#include<bits/stdc++.h>
using namespace std;
int t,v,e,a,b;
int mp[21][21];
int vis[21];
int ans;

void dfs(int u)
{
    if(u==v/2+1)
    {
        ans++;
        return;
    }
    int flag=0;

    for(int i=v/2+1;i<=v;i++)
    {
        if(!vis[i]&&mp[u][i])
        {
            vis[i]=1;
            dfs(u+1);
            vis[i]=0;//记得 dfs 之后把vis修改回来
            flag=1;
        }
    }
    if(!flag)return;
}

int main()
{
    cin>>t;
    while(t--)
    {
        memset(mp,0,sizeof(mp));
        scanf("%d%d",&v,&e);
        for(int i=1;i<=e;i++)
        {
            scanf("%d%d",&a,&b);
            mp[a][b]=mp[b][a]=1;
        }
        ans = 0;
        memset(vis,0,sizeof(vis));
        dfs(1);
        cout<<(ans%2==0)<<endl;
    }
    return 0;
}

转载请注明出处^ ^

在图论中,最大匹配问题通常不通过高斯消元法来解决。然而,在特定情况下,可以利用线性代数的方法处理某些类型的匹配问题。对于一般情况下的二分图的最大匹配问题,常用匈牙利算法或基于网络流的增广路径方法求解。 如果要讨论使用类似于高斯消元的思想解决问题,则涉及到的是完美匹配计数问题中的Pfaffian orientation以及Tutte矩阵的概念[^1]。这种方法主要用于计算完美匹配的数量而非寻找具体的最大匹配方案。 对于实际应用来说,更常见的方式是采用Edmonds' Blossom收缩算法或者Hopcroft-Karp算法针对非加权无向图;而对于带权重的情况则可能考虑Kuhn-Munkres (Hungarian) Algorithm 或者其他专门设计用于解决赋权二部图上的指派问题的技术。 尽管如此,为了满足请求并提供一种理论框架内的解释: ### 使用Gaussian Elimination实现Perfect Matching Counting #### 定义 Tutte Matrix 给定一个具有偶数个顶点的简单未定向图 \( G=(V,E) \),定义关联于该图的一个反对称方阵\( A(G)=(a_{ij})\)如下: \[ a_{ij}=\begin{cases} x_{ij}, &\text{if } i<j \text { and }(i,j)\in E\\ -x_{ji},&\text{if } j<i \text { and }(j,i)\in E \\ 0,&\text{otherwise } \end{cases}\] 其中变量 \( x_{ij}=−x_{ji}(i≠j) \) 是独立未知量。这个矩阵被称为图G的Tutte matrix。 当且仅当存在完美匹配时,此矩阵行列式的值才有可能是非零多项式形式。因此可以通过检测行列式是否恒等于零来判断是否存在完美匹配。 ```python import numpy as np from sympy import symbols, det def tutte_matrix(graph_edges, n_vertices): # 创建符号表 variables = {(u,v):symbols(f"x_{{{min(u,v)}},{max(u,v)}}") for u,v in graph_edges} # 初始化tutte矩阵 T = [[None]*n_vertices for _ in range(n_vertices)] for i in range(n_vertices): for j in range(i+1,n_vertices): if (i,j) in variables: T[i][j]=variables[(i,j)] T[j][i]=-variables[(i,j)] else: T[i][j], T[j][i] = 0, 0 return np.array(T) # 假设有一个简单的完全图作为例子 edges_example=[(0,1),(0,2),(1,2)] matrix=tutte_matrix(edges_example,3) print("Tutte Matrix:") print(matrix) print("\nDeterminant of the Tutte Matrix:",det(matrix)) ``` 上述代码片段展示了如何构建一个给定边集和节点数量对应的Tutte矩阵,并计算其行列式以确定是否存在完美匹配的可能性。请注意这只是一个概念验证性质的例子,真正的高效实现会更加复杂并且依赖具体的应用场景。
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