CodeForces 899E Segments Removal

本文介绍了一种结合并查集和堆的数据结构应用方法,用于解决特定类型的问题。通过并查集维护数字团的属性,如数量、边界值,并利用堆来保持最大区间的实时更新,实现高效问题求解。

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题解:

这题解法比较多样,核心思路都是模拟,只是实现的方法不一样。我的话是采用并查集+堆来维护。并查集要维护三个值:这一团的数字个数,这一团最左边的数字,这一团最右边的数字(这些都是可以直接暴力合并的),然后用堆来维护最大的区间,每次取出来判断一下是否已经被删去,没有就计入答案然后更新并查集和堆就可以了。

代码如下:

#include<bits/stdc++.h>
#define mod 1000000007
using namespace std;

int n,ans;
int a[200010],s[200010];
int fa[200010],sum[200010],lft[200010],rht[200010];
bool f[200010]; 

struct node{
	int id,sum;
	bool operator <(const node &T)const{return sum<T.sum||(sum==T.sum&&id>T.id);}
};

priority_queue<node> Q;

int find(int x)
{
	if(fa[x]!=x) fa[x]=find(fa[x]);
	return fa[x];
}

void merge(int a,int b)
{
	a=find(a);b=find(b);
	if(a==b) return ;
	if(sum[a]<sum[b]) swap(a,b);
	fa[b]=a;sum[a]+=sum[b];lft[a]=min(lft[a],lft[b]);rht[a]=max(rht[a],rht[b]);
}

int main()
{
	int x;
	node b;
	scanf("%d",&n);
	for(int i=1;i<=n;i++)
	{
	   scanf("%d",&a[i]);fa[i]=lft[i]=rht[i]=i;sum[i]=1;
	   if(a[i]==a[i-1]){s[i]=s[i-1];merge(i,i-1);}
	   else s[i]=i;
	}
	for(int i=1;i<=n;i++){x=find(i);if(!f[x]){f[x]=1;Q.push((node){lft[x],sum[x]});}}
	while(!Q.empty())
	{
	  b=Q.top();Q.pop();x=find(b.id);
	  while(!f[x]&&!Q.empty()){b=Q.top();Q.pop();x=find(b.id);}
	  if(!f[x]&&Q.empty()) break;
	  ans++;f[x]=0;
	  while(lft[x]>1&&!f[find(lft[x]-1)]){merge(x,lft[x]-1);x=find(x);}
	  while(rht[x]<n&&!f[find(rht[x]+1)]){merge(x,rht[x]+1);x=find(x);}
	  if(lft[x]>1&&rht[x]<n&&a[lft[x]-1]==a[rht[x]+1])
	  {merge(lft[x]-1,rht[x]+1);Q.push((node){lft[find(lft[x]-1)],sum[find(lft[x]-1)]});}
	}
	printf("%d\n",ans);
	return 0;
}

 

### Codeforces 887E Problem Solution and Discussion The problem **887E - The Great Game** on Codeforces involves a strategic game between two players who take turns to perform operations under specific rules. To tackle this challenge effectively, understanding both dynamic programming (DP) techniques and bitwise manipulation is crucial. #### Dynamic Programming Approach One effective method to approach this problem utilizes DP with memoization. By defining `dp[i][j]` as the optimal result when starting from state `(i,j)` where `i` represents current position and `j` indicates some status flag related to previous moves: ```cpp #include <bits/stdc++.h> using namespace std; const int MAXN = ...; // Define based on constraints int dp[MAXN][2]; // Function to calculate minimum steps using top-down DP int minSteps(int pos, bool prevMoveType) { if (pos >= N) return 0; if (dp[pos][prevMoveType] != -1) return dp[pos][prevMoveType]; int res = INT_MAX; // Try all possible next positions and update 'res' for (...) { /* Logic here */ } dp[pos][prevMoveType] = res; return res; } ``` This code snippet outlines how one might structure a solution involving recursive calls combined with caching results through an array named `dp`. #### Bitwise Operations Insight Another critical aspect lies within efficiently handling large integers via bitwise operators instead of arithmetic ones whenever applicable. This optimization can significantly reduce computation time especially given tight limits often found in competitive coding challenges like those hosted by platforms such as Codeforces[^1]. For detailed discussions about similar problems or more insights into solving strategies specifically tailored towards contest preparation, visiting forums dedicated to algorithmic contests would be beneficial. Websites associated directly with Codeforces offer rich resources including editorials written after each round which provide comprehensive explanations alongside alternative approaches taken by successful contestants during live events. --related questions-- 1. What are common pitfalls encountered while implementing dynamic programming solutions? 2. How does bit manipulation improve performance in algorithms dealing with integer values? 3. Can you recommend any online communities focused on discussing competitive programming tactics? 4. Are there particular patterns that frequently appear across different levels of difficulty within Codeforces contests?
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