【codeforces 1026 D】 Shortest Cycle(最小环)

本文探讨了一种新颖的方法来解决特定条件下的图论问题。在给定数组中,通过分析二进制位数的出现次数,可以高效地计算出构成图的最小环。当满足特定条件时,可以直接得出最小环的大小为3。对于不满足条件的情况,文章提供了一个使用Floyd算法进行计算的示例。

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题面

题意:

一个长度为 n n n的数组,如果 a i & a j ! = 0 a_{i} \& a_{j}!=0 ai&aj!=0,那么 i i i j j j之间就有一条边,计算所构成图的最小环。
( 1 ≤ n ≤ 100000 , 1 ≤ a i ≤ 1 0 18 ) (1 \leq n \leq 100000,1 \leq a_i \leq 10^{18}) (1n100000,1ai1018)

思路

这个题目的想法还是比较妙的,你会发现如果数组 a a a把所有非 0 0 0的数字去掉( 0 0 0没有任何意义),剩下的数字的个数 n n n如果满足 n ≥ 3 ∗ 64 n \ge 3*64 n364,那么最小环一定为 3 3 3,因为这样一定满足每个数字的二进制位数的某一位出现了三次,那么这三个数字就一定能够构成一个环,答案显然为 3 3 3,而对于 n &lt; 3 ∗ 64 n &lt; 3*64 n<364的情况,我们可以直接利用 f l o y e d floyed floyed O ( n 3 ) O(n^3) O(n3)计算最小环。

#include<bits/stdc++.h>
using namespace std;
const int N = 1e6+100;
const int mod = 1e9+7;
typedef long long ll;
const int INF = 0x3f3f3f3f;
const ll llINF = 0x3f3f3f3f3f3f3f3f;
#define rep(i,a,b) for(int i=(a);i<=(b);i++)
#define fep(i,a,b) for(int i=(a);i>=(b);i--)
inline bool read(ll &num) {
    char in;bool IsN=false;
    in=getchar();
    if(in==EOF) return false;
    while(in!='-'&&(in<'0'||in>'9')) in=getchar();
    if(in=='-'){ IsN=true;num=0;}
    else num=in-'0';
    while(in=getchar(),in>='0'&&in<='9'){
            num*=10,num+=in-'0';
    } 
    if(IsN) num=-num;
    return true;
}
ll n,ans,rt,val[500][500],dis[500][500],a[N];
bool vis[N];
int num[100];
int main(){
   // freopen("1.in", "r", stdin);
    read(n);
    rep(i,1,n){
         read(a[i]);
         if(!a[i]){
             i--;n--;continue;
         }
    }
    if(n>=3*64){
        cout<<3<<endl;
        return 0;
    }
    rep(i,1,n){
        rep(j,1,n){
            val[i][j]=dis[i][j]=99999999999999;
            if(i!=j&&(a[i]&a[j])){
                val[i][j]=dis[i][j]=1;
            }
        }
    }
    ll ans=1e9+100;
    rep(k,1,n){
        rep(i,1,k-1){
            rep(j,1,i-1){
                ans=min(ans,dis[i][j]+val[i][k]+val[k][j]);
            }
        }
        rep(i,1,n){
            rep(j,1,n){
                dis[i][j]=min(dis[i][j],dis[i][k]+dis[k][j]);
            }
        }
    }
    if(ans>1e9) cout<<-1<<endl;
    else cout<<ans<<endl;
    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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