codeforces1344D Résumé Review

本文深入解析了Codeforces编号为1344/D的题目,采用了一种创新的二分搜索策略来解决最大利润问题。通过计算每个元素的贡献增量,并对其应用二分法,以确定最佳的购买数量,确保总利润最大化。文章详细介绍了算法实现过程,包括如何处理增量相同的特殊情况。

https://codeforces.com/problemset/problem/1344/D

这题晚上unr以后再群里讨论,牛逼网友说了对增值二分后,我还是没想出来是什么意思,最后还是看了题解。。。

对于每一个i,我们设f(x)=x(a[i]-x^2),a[i]为常数,那么f'(x)=a[i]-3x^2,他的导数是递减的,所以对于f(x)->f(x+1)这个增量,是递减的。

那么我们队这个增量进行二分,对于每个i , 当前已经拿了b[i],如果拿b[i]+1大于这个增量delta,那么就让b[i]++

由于这个增量是递减的,所以我们可以对b[i]进行二分,当然如果无论如何都<delta,那么让b[i]=0

这个二分是有点奇怪的,我们令在delta情况下,一共有sum个,那么sum>=k,说明合法,此时移动l,因为增量是递减的,所以增量越小,个数越多。

由于最后二分出来的答案,sum可能大于k,因为有些时候不同的i的增量可能一样,那么由于能输出任意一种情况,所以直接记录一下最后二分出来答案下哪些增量是等于答案的,他们是可以减去的。

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

const int maxl=3e5+10;

int n,m,cas;
ll k,ans;
ll a[maxl],b[maxl];
char s[maxl];
bool eqlans[maxl];

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

inline ll calc(int i,ll x)
{
	return a[i]-3*x*x+3*x-1;
}

inline ll jug(ll delta)
{
	ll sum=0,l,r,mid,d;
	for(int i=1;i<=n;i++)
	{
		l=1,r=a[i];
		if(calc(i,1)<delta)
		{
			b[i]=0;eqlans[i]=false;
			continue;
		}
		while(l+1<r)
		{
			mid=(l+r)>>1;
			if(calc(i,mid)>=delta)
				l=mid;
			else
				r=mid;	
		}
		if(calc(i,r)>=delta)
			d=r;
		else
			d=r-1;
		b[i]=d;
		eqlans[i]=(calc(i,d)==delta);
		sum+=d;
	}
	return sum;
}

inline void mainwork()
{
	ll l=-4e18,r=1e9,mid;
	while(l+1<r)
	{
		mid=(l+r)>>1;
		if(jug(mid)>=k)
			l=mid;
		else
			r=mid;
	}
	if(jug(r)>=k)
		ans=r;
	else if(jug(r-1)>=k)
		ans=r-1;
	k-=jug(ans);
	for(int i=1;i<=n && k<0;i++)
	if(eqlans[i] && b[i]>0)
		++k,--b[i];
}

inline void print()
{
	for(int i=1;i<=n;i++)
		printf("%lld%c",b[i],(i==n)?'\n':' ');
}

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