codeforces 1198D Rectangle Painting 1

本文介绍了一种解决棋盘上最小代价将指定区域染成单一颜色的算法。通过动态规划(DP),利用记忆化搜索优化,对每个子问题求解最小代价。算法考虑了直接染色整个区域和分治两种策略,适用于平面区域染色问题。

比赛中写了个假贪心,赛后发现就是个经典的平面区域(棋盘)DP

f[x1][y1][x2][y2]为把这一块变白的最小代价,如果他就是全白的,那么为0,如果只有一个点,那么直接判断然后返回

使用记忆化搜索的形式DP,对于每一次,有一种方案是直接把这一片区域全染白,那么代价是max(y2-y1+1,x2-x1+1)。

另一种方案是把这片区域分成两半然后递归下去找这两半的最小答案加起来。

#include<bits/stdc++.h>
#define maxl 51
using namespace std;
 
int n,ans;
int a[maxl][maxl];
int sum[maxl][maxl];
int f[maxl][maxl][maxl][maxl]; 
char s[maxl][maxl];
struct node
{
	int x,y;
};
queue <node> q;
 
inline void prework()
{
	scanf("%d",&n);
	for(int i=1;i<=n;i++)
		scanf("%s",s[i]+1);
	for(int i=1;i<=n;i++)
		for(int j=1;j<=n;j++)
		if(s[i][j]=='#')
			a[i][j]=1;
		else
			a[i][j]=0;	
	for(int i=1;i<=n;i++)
		for(int j=1;j<=n;j++)
			sum[i][j]=sum[i][j-1]+sum[i-1][j]-sum[i-1][j-1]+a[i][j];
}
 
inline int num(int x1,int y1,int x2,int y2)
{
	return sum[x2][y2]-sum[x2][y1-1]-sum[x1-1][y2]+sum[x1-1][y1-1];
}
 
inline int find(int x1,int y1,int x2,int y2)
{
	if(f[x1][y1][x2][y2]>=0)
		return f[x1][y1][x2][y2];
	if(x1==x2 && y1==y2)
	{
		if(a[x1][y1]==1)
			f[x1][y1][x2][y2]=1;
		else
			f[x1][y1][x2][y2]=0;
		return f[x1][y1][x2][y2];
	}
	if(num(x1,y1,x2,y2)==0)
	{
		f[x1][y1][x2][y2]=0;
		return 0;
	} 
	f[x1][y1][x2][y2]=max(y2-y1+1,x2-x1+1);
	for(int i=x1;i<=x2-1;i++)
		f[x1][y1][x2][y2]=min(f[x1][y1][x2][y2],find(x1,y1,i,y2)+find(i+1,y1,x2,y2));
	for(int i=y1;i<=y2-1;i++)
		f[x1][y1][x2][y2]=min(f[x1][y1][x2][y2],find(x1,y1,x2,i)+find(x1,i+1,x2,y2));
	return f[x1][y1][x2][y2];
}
 
inline void mainwork()
{
	memset(f,-1,sizeof(f));
	ans=find(1,1,n,n);
}
 
inline void print()
{
	printf("%d",ans);	
}
 
int main()
{
	prework();
	mainwork();
	print();
	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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