codeforces 557D Vitaly and Cycle

本文介绍了一种利用二分图染色的方法来判断图中是否存在奇数长度的环,并提供了添加最少边数使图包含奇环的算法及其实现细节。

题意简述

给定一个图 求至少添加多少条边使得它存在奇环 并求出添加的方案数

(注意不考虑自环)

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一道二分图染色的讨论题

比赛时只会用二分图染色判断树以及偶环 忘记用这个来判奇环。。。

二分图染色这种联赛知识点的题目现在也不会写了。。。

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我们可以按需要添加边的条数来讨论这题

首先讨论添加边条数为3——即原原图边数m为0时

$ans=n*(n-1)*(n-2)/6$

再讨论添加边条数为2——即原图中所有边都没有公共端点/所有点度数<=1 时

$ans=m*(n-2)$

再讨论添加边数为0——即原图中存在奇环时

$ans=1$

最后讨论添加边数为1——即原图中只有树以及偶环

$ans=\sum(white[i]-1)*white[i]/2+(black[i]-1)*black[i]/2$

其实思路清晰后实现起来就很容易了

#include <cstdio>
#include <cstring>
#include <cmath>
#include <algorithm>
#define rep(i,n) for(int i=1;i<=n;++i)
#define imax(x,y) (x>y?x:y)
#define imin(x,y) (x<y?x:y)
using namespace std;
const int N=100010;
int firste[N],nexte[N<<1],v[N<<1];
int color[N],fa[N],bl[N],wh[N],degree[N];
int n,m,e=1,flag=1;
long long ans=0;
void build_edge(int x,int y)
{
    ++e;
    nexte[e]=firste[x];
    firste[x]=e;
    v[e]=y;
}
void dfs(int u,int c,int f)
{
    color[u]=c;
    fa[u]=f;
    if(c&1)++bl[f];
    else ++wh[f];
    for(int p=firste[u];p;p=nexte[p])
        if(!color[v[p]])dfs(v[p],3-c,f);
    else if(color[v[p]]==color[u])
    {
        flag=1;
        return;
    }
}
int main()
{
    int x,y;
    scanf("%d%d",&n,&m);
    if(!m)
    {
        ans=(long long)n*(n-1)*(n-2)/6;
        printf("3 %I64d",ans);
        return 0;
    }
    rep(i,m)
    {
        scanf("%d%d",&x,&y);
        build_edge(x,y);
        build_edge(y,x);
        ++degree[x];
        ++degree[y];
        if(degree[x]>1||degree[y]>1)flag=0;
    }
    if(flag)
    {
        ans=(long long)m*(n-2);
        printf("2 %I64d",ans);
        return 0;
    }
    int cnt=0;
    rep(i,n)
    if(!color[i])
    {
        dfs(i,1,++cnt);
        if(flag)
        {
            printf("0 1");
            return 0;
        }
    }
    rep(i,cnt)
    ans+=(long long)(wh[i]-1)*wh[i]/2+(long long)(bl[i]-1)*bl[i]/2;
    printf("1 %I64d",ans);
    return 0;
}

 

转载于:https://www.cnblogs.com/sagitta/p/4612214.html

### Codeforces 1487D Problem Solution The problem described involves determining the maximum amount of a product that can be created from given quantities of ingredients under an idealized production process. For this specific case on Codeforces with problem number 1487D, while direct details about this exact question are not provided here, similar problems often involve resource allocation or limiting reagent type calculations. For instance, when faced with such constraints-based questions where multiple resources contribute to producing one unit of output but at different ratios, finding the bottleneck becomes crucial. In another context related to crafting items using various materials, it was determined that the formula `min(a[0],a[1],a[2]/2,a[3]/7,a[4]/4)` could represent how these limits interact[^1]. However, applying this directly without knowing specifics like what each array element represents in relation to the actual requirements for creating "philosophical stones" as mentioned would require adjustments based upon the precise conditions outlined within 1487D itself. To solve or discuss solutions effectively regarding Codeforces' challenge numbered 1487D: - Carefully read through all aspects presented by the contest organizers. - Identify which ingredient or component acts as the primary constraint towards achieving full capacity utilization. - Implement logic reflecting those relationships accurately; typically involving loops, conditionals, and possibly dynamic programming depending on complexity level required beyond simple minimum value determination across adjusted inputs. ```cpp #include <iostream> #include <vector> using namespace std; int main() { int n; cin >> n; vector<long long> a(n); for(int i=0;i<n;++i){ cin>>a[i]; } // Assuming indices correspond appropriately per problem statement's ratio requirement cout << min({a[0], a[1], a[2]/2LL, a[3]/7LL, a[4]/4LL}) << endl; } ``` --related questions-- 1. How does identifying bottlenecks help optimize algorithms solving constrained optimization problems? 2. What strategies should contestants adopt when translating mathematical formulas into code during competitive coding events? 3. Can you explain why understanding input-output relations is critical before implementing any algorithmic approach? 4. In what ways do prefix-suffix-middle frameworks enhance model training efficiency outside of just tokenization improvements? 5. Why might adjusting sample proportions specifically benefit models designed for tasks requiring both strong linguistic comprehension alongside logical reasoning skills?
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