codeforces1406D Three Sequences

本文详细解析了Codeforces比赛中的D题,通过分析序列ai和bi、ci的关系,提出了求解min{max(bn,c1)}

https://codeforces.com/contest/1406/problem/D

菜到爆炸,1个小时20分钟没想出来怎么求这个值

这题就是要求min{max(bn,c1)},然后发现ai和ai-1之间的大小关系可以限制bi,bi-1,ci,ci-1的大小。。。

假设a1=b1+c1, b1=a1-mx,c1=mx

那么由于b1<=b2,c1>=c2

得b2>=a1-mx,b2>=a2-mx ,我们希望b序列递增的尽可能慢来让最后bn尽可能小,所以会取max(a1-mx,a2-mx)

那么a1>=a2时,b1=b2=a1-mx,但是a1<a2时,b2=a2-mx,b1+a2-a1=b2,

设所有相邻差值大于0的差值之和为K,那么bn=a1+K-mx   ,而bn本身也就是mx,那么最小值就是2mx=a1+K了

修改操作就只要考虑一下l,l-1和r+1,r的差值即可

用树状数组维护一下每个点的值就行

#include<bits/stdc++.h>
using namespace std;
typedef long long ll;

const int maxl=3e5+10;

int n,m,cnt,tot,cas;ll ans;
ll now;
ll a[maxl];
ll b[maxl];
bool vis[maxl];
char s[maxl];

inline void prework()
{
	scanf("%d",&n);
	for(int i=1;i<=n;i++)
		scanf("%lld",&a[i]);
	now=0;
	for(int i=2;i<=n;i++)
	if(a[i-1]<a[i])
		now+=a[i]-a[i-1];
}

inline ll sum(int i)
{
	ll ret=0;
	while(i)
	{
		ret+=b[i];
		i-=i&-i;
	}
	return ret;
}

inline void add(int i,int x)
{
	while(i<=n+1)
	{
		b[i]+=x;
		i+=i&-i;
	}
}

inline void mainwork()
{
	scanf("%d",&m);
	if(a[1]+sum(1)+now>=0)
		ans=(a[1]+sum(1)+now+1)/2;
	else
		ans=(a[1]+sum(1)+now)/2;
	printf("%lld\n",ans);
	int l,r,x;ll d;
	for(int i=1;i<=m;i++)
	{
		scanf("%d%d%d",&l,&r,&x);
		if(l>1)
		{
			d=a[l]+sum(l)-a[l-1]-sum(l-1);
			if(d>0)
				now-=d;
		}
		if(r<n)
		{
			d=a[r+1]+sum(r+1)-a[r]-sum(r);
			if(d>0)
				now-=d;
		}
		add(l,x);
		add(r+1,-x);
		if(l>1)
		{
			d=a[l]+sum(l)-a[l-1]-sum(l-1);
			if(d>0)
				now+=d;
		}
		if(r<n)
		{
			d=a[r+1]+sum(r+1)-a[r]-sum(r);
			if(d>0)
				now+=d;
		}
		if(a[1]+sum(1)+now>=0)
			ans=(a[1]+sum(1)+now+1)/2;
		else
			ans=(a[1]+sum(1)+now)/2;
		printf("%lld\n",ans);
	}
}

int main()
{
	int t=1;
	//scanf("%d",&t);
	for(cas=1;cas<=t;cas++)
	{
		prework();
		mainwork();
	}
	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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