codeforces 557d

本文介绍了一种算法,用于确定在给定图中至少还需添加多少边才能构造出奇数长度的环。通过判断图是否为二分图及其组成部分的连通性,算法能够高效地找出解决方案。

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题意:给你一幅图 问给至少还要几条边 使得这个图还存在奇环。

思路:如果是只存在偶环 那么表示为二分图 那么图的左边的点之间有联通 那么只需要选择左边任意两个点加上一条边  就可以构成奇环 同理可以算出所有答案。

当然还有2种特殊情况 一种是 左边点不联通和 没有边的情况,这些另作考虑



(看不懂题目 去查题意 结果看到二分图 于是。。。)


#include<cstring>
#include<cstdlib>
#include<iostream>
#include<stdio.h>
#include<cmath>
#include<algorithm>
#include<vector>
using namespace std;
#define N 100010
#define MOD 1000000007
#define LL long long

vector<int> e[N];
int v[N];
LL x,y;
bool dfs(int fa,int t)
{
    if(v[t]==1)
        x++;
    else
        y++;

    for(int i=0;i<e[t].size();i++)
    {
        int to=e[t][i];
        if(fa==to)continue;
        if(v[to]==v[t])return false;
        if(v[to]==0)
        {
            v[to]=-v[t];
            if(!dfs(t,to))return false;
        }
    }
    return true;
}

int main()
{
    LL n,m;
    scanf("%lld%lld",&n,&m);
    for(LL i=0;i<m;i++)
    {
        scanf("%lld%lld",&x,&y);
        e[x].push_back(y);
        e[y].push_back(x);
    }
    LL ans=0;
    if(m==0)
    {
        ans=n*(n-1)*(n-2)/6;
        printf("3 %lld\n",ans);
    }
    else
    {
        m=0;
        for(int i=1;i<=n;i++)
        {
            if(v[i]!=0)continue;
            v[i]=1;
            x=y=0;
            if(dfs(i,i))
            {
                ans=(ans+x*(x-1)/2+y*(y-1)/2);
                if(x+y==2)
                    m++;
            }
            else
            {
                printf("0 1\n");
                return 0;
            }
        }
        if(ans==0){
            ans=m*(n-2);
            printf("2 %lld\n",ans);
        }
        else
            printf("1 %lld\n",ans);
    }
    return 0;
}


### 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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