[codeforces570D]Tree Requests(dsu on the tree)

题目:

我是超链接

题意:

一棵树,每一个点有一个字符,给出若干询问,每一次询问以某个点为根的子树中深度为x的点能否组成一个回文串。

题解:

维护树上的特定信息?dsu on the tree!
回文的话,出现次数为奇数的字符至多有一个
可以建一棵虚树,连接的是点和深度,在查询的时候,对于一个节点,可以维护ta每个深度的信息情况,再用这个虚树(链表)来查询输出

代码:

#include <cstdio>
#include <iostream>
#define N 500005
using namespace std;
int tot,nxt[N*2],point[N],v[N*2],size[N],tot1,nxt1[N*2],point1[N],v1[N*2],id[N],h[N],son[N];
int cnt[N][30],Son;
char st[N];bool ans[N];
void addline(int x,int y){++tot; nxt[tot]=point[x]; point[x]=tot; v[tot]=y;}
void addline1(int x,int y,int i){++tot1; nxt1[tot1]=point1[x]; point1[x]=tot1; v1[tot1]=y; id[tot1]=i;}
void getson(int x,int fa)
{
    size[x]=1; h[x]=h[fa]+1;
    for (int i=point[x];i;i=nxt[i])
      {
        getson(v[i],x);
        if (size[v[i]]>size[son[x]]) son[x]=v[i];
        size[x]+=size[v[i]];
      }
}
void add(int x,int vv)
{
    cnt[h[x]][st[x]-'a']+=vv;
    for (int i=point[x];i;i=nxt[i]) 
      if (v[i]!=Son) add(v[i],vv); 
}
void dfs(int x,int k)
{
    for (int i=point[x];i;i=nxt[i])
      if (v[i]!=son[x]) dfs(v[i],0);
    if (son[x]) dfs(son[x],1),Son=son[x];
    add(x,1);Son=0;//这个Son=0必须加,用来消除轻节点x的影响 
    for (int i=point1[x];i;i=nxt1[i])
    {
        int odd=0;
        for (int j=0;j<26;j++)
          if (cnt[v1[i]][j]&1) odd++;
        if (odd>1) ans[id[i]]=0;else ans[id[i]]=1;
    }
    if (!k) add(x,-1);
}
int main()
{
    int n,m,i,j;
    scanf("%d%d",&n,&m);
    for (i=2;i<=n;i++)
    {
        int p;
        scanf("%d",&p); 
        addline(p,i);
    }
    scanf("%s",st+1);
    getson(1,0);
    for (i=1;i<=m;i++)
    {
        int vv,hh;
        scanf("%d%d",&vv,&hh);
        addline1(vv,hh,i);
    }
    dfs(1,0);
    for (i=1;i<=m;i++)
      if (ans[i]) printf("Yes\n");else printf("No\n");
}
### 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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