Codeforces 375D Tree and Queries dfs序+莫队

本文介绍了一种使用莫队算法解决特定树形结构查询问题的方法。问题要求查询以某个节点为根的子树中,颜色出现次数大于等于k的颜色种类数。通过构建DFS序并利用离线处理技巧,有效地解决了这一问题。

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这题好题啊.
我发现我没有写过莫队算法的博客.以后我会把博客加上去的.
先看看这题吧.
Codeforces 375D Tree and Queries
给一棵[Math Processing Error] 1 为根的树,每个节点有一种颜色,m个询问,询问[Math Processing Error] v 为根的子树中出现次数k的颜色有多少种.

只有询问.我们考虑莫队.
只有子树.我们考虑dfs序.
以1为根跑出dfs序并重新编号,用dfn和edf两个数组标明每个点控制的子树.
接下来莫队离线排序搞一下,用cnt数组维护每一种颜色出现的次数.
sum数组维护每一种颜色出现的次数分别出现的次数.(这个说法比较诡异).

然后按照莫队处理一下就可以了.

#include<bits/stdc++.h> //Ithea Myse Valgulious
namespace chtholly{
typedef long long ll;
#define re0 register int
#define rec register char
#define rel register ll
#define gc getchar
#define pc putchar
#define p32 pc(' ')
#define pl puts("")
/*By Citrus*/
inline int read(){
  re0 x=0,f=1;rec c=gc();
  for (;!isdigit(c);c=gc()) f^=c=='-';
  for (;isdigit(c);c=gc()) x=x*10+c-'0';
  return x*(f?1:-1);
  }
inline void read(rel &x){
  x=0;re0 f=1;rec c=gc();
  for (;!isdigit(c);c=gc()) f^=c=='-';
  for (;isdigit(c);c=gc()) x=x*10+c-'0';
  x*=f?1:-1;
  }
template <typename mitsuha>
inline int write(mitsuha x){
  if (!x) return pc(48);
  if (x<0) x=-x,pc('-');
  re0 bit[20],i,p=0;
  for (;x;x/=10) bit[++p]=x%10;
  for (i=p;i;--i) pc(bit[i]+48);
  }
inline char fuhao(){
  rec c=gc();
  for (;isspace(c);c=gc());
  return c;
  }
}using namespace chtholly;
using namespace std;
const int yuzu=1e5;
typedef int fuko[yuzu|10];
vector<int> lj[yuzu|10];
fuko a,dfn,edf,cnt,ans,sum,col;
int n=read(),m=read(),id,block=sqrt(n);

#define bl(x) ((x)/block)
#define all(u) dfn[u],edf[u]
void dfs(int u,int fa){
dfn[u]=++id;
col[id]=u;
for (int v:lj[u]) if (v^fa) dfs(v,u);
edf[u]=id;
}

struct query{
int l,r,k,id;
bool operator <(const query &b) const{
  return bl(l)^bl(b.l)?l<b.l:r<b.r;
  }
}q[yuzu|10];

int nowl=1,nowr;
void add(int x){sum[++cnt[a[col[x]]]]++;}
void del(int x){sum[cnt[a[col[x]]]--]--;}
int main(){
re0 i;
for (i=1;i<=n;++i) a[i]=read();
for (i=1;i<n;++i){
  int u=read(),v=read();
  lj[u].push_back(v);
  lj[v].push_back(u);
  }
dfs(1,0);
for (i=1;i<=m;++i){
  int u=read(),k=read();
  q[i]=query{all(u),k,i};
  }
sort(q+1,q+m+1);
for (i=1;i<=m;++i){
  for (;nowl<q[i].l;) del(nowl++);
  for (;nowl>q[i].l;) add(--nowl);
  for (;nowr<q[i].r;) add(++nowr);
  for (;nowr>q[i].r;) del(nowr--);
  ans[q[i].id]=sum[q[i].k];
  }
for (i=1;i<=m;++i) write(ans[i]),pl;
}
### Codeforces Problem 1009 Details and Solution The referenced content discusses a set of problems related to number theory on the Codeforces platform with difficulties ranging from 2000 to 3000+. The focus is primarily on optimizing algorithms due to large input sizes where complexities such as \(O(n \sqrt{n})\) are not feasible without additional optimizations[^1]. For **Problem 1009**, while specific details about this exact problem may vary depending on updates or changes within the Codeforces database, it generally involves handling operations over tree structures efficiently given constraints like those described: - Input consists of two integers \(n\) (number of nodes) and \(m\) (number of queries), followed by detailed descriptions of each node's children. - Queries include three types: adding an edge between two vertices ("1 v u"), removing edges based on height conditions ("2 v h"), and finding paths satisfying certain properties ("3 k"). Given these parameters, efficient data structure usage becomes critical. Below outlines how one might approach solving similar structured questions effectively. #### Efficient Data Structures Utilization To handle up to \(10^5\) elements per dimension (\(n,m\)), advanced techniques must be employed: - Preprocessing using Depth First Search (DFS) traversal can help compute auxiliary information quickly during runtime. - Segment trees or Binary Indexed Trees could assist managing dynamic range updates/queries required under different operation modes mentioned above. Here’s some sample pseudo-code demonstrating initialization phase alongside query processing logic utilizing DFS precomputation technique combined with segment tree implementation for fast querying capabilities: ```python from collections import defaultdict class Node: def __init__(self): self.children = [] def dfs(node_id, parent=-1): global depth, subtree_size start_time[node_id] = time_counter[0] time_increases() subtree_size[node_id]=1 for child in adj_list[node_id]: if(child !=parent): depth[child]=depth[node_id]+1 dfs(child,node_id) subtree_size[node_id]+=subtree_size[child] # Initialize variables & read inputs here... adj_list=defaultdict(list) for _ in range(m): op,*params=input().split() params=list(map(int,params)) if(op=='1'): # Add Edge Logic Here Using Params[v,u] elif(op=='2'): pass else: # Operation Type '3' result=process_query_k(params[k]) print(result) ``` This code snippet provides foundational steps towards constructing solutions tailored specifically toward addressing challenges posed through complex graph manipulations involving numerous sequential actions against hierarchical datasets represented via adjacency lists. §§Related Questions§§ 1. What other common optimization strategies exist when dealing with computational limits exceeding standard algorithmic approaches? 2. How does implementing lazy propagation affect performance improvements inside segment-tree-based systems used across competitive programming platforms? 3. Can you provide examples illustrating alternative methods besides DFS preorder/postorder traversals that achieve equivalent results more suitably suited under particular circumstances? 4. In what ways do persistent data structures contribute differently compared traditional mutable ones concerning memory consumption versus speed tradeoffs seen regularly throughout high-level contests including CF rounds?
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