cf#579 F2

http://codeforces.com/contest/1203/problem/F2
F2. Complete the Projects (hard version)
time limit per test2 seconds
memory limit per test256 megabytes
inputstandard input
outputstandard output
The only difference between easy and hard versions is that you should complete all the projects in easy version but this is not necessary in hard version.

Polycarp is a very famous freelancer. His current rating is r units.

Some very rich customers asked him to complete some projects for their companies. To complete the i-th project, Polycarp needs to have at least ai units of rating; after he completes this project, his rating will change by bi (his rating will increase or decrease by bi) (bi can be positive or negative). Polycarp’s rating should not fall below zero because then people won’t trust such a low rated freelancer.

Polycarp can choose the order in which he completes projects. Furthermore, he can even skip some projects altogether.

To gain more experience (and money, of course) Polycarp wants to choose the subset of projects having maximum possible size and the order in which he will complete them, so he has enough rating before starting each project, and has non-negative rating after completing each project.

Your task is to calculate the maximum possible size of such subset of projects.

Input
The first line of the input contains two integers n and r (1≤n≤100,1≤r≤30000) — the number of projects and the initial rating of Polycarp, respectively.

The next n lines contain projects, one per line. The i-th project is represented as a pair of integers ai and bi (1≤ai≤30000, −300≤bi≤300) — the rating required to complete the i-th project and the rating change after the project completion.

Output
Print one integer — the size of the maximum possible subset (possibly, empty) of projects Polycarp can choose.

Examples
inputCopy
3 4
4 6
10 -2
8 -1
outputCopy
3
inputCopy
5 20
45 -6
34 -15
10 34
1 27
40 -45
outputCopy
5
inputCopy
3 2
300 -300
1 299
1 123
outputCopy
3
算减少部分的时候,如果不满足条件,就在前面找减得最多的(优先队列,开始的两个为什么要那样建优先队列前一篇有证明

#include<stdio.h>
#include<queue>
using namespace std;
struct node1
{
	int num,d;
	bool operator < (const node1& b) const 
	{
		return num>b.num;
	}
};
struct node2
{
	int num,d;
	bool operator < (const node2& b) const 
	{
		return num<b.num;
	}
};
priority_queue<node1> q1;
priority_queue<node2> q2;
priority_queue<int,vector<int>,greater<int> >q;
int main()
{
	int n,r;
	int tmp1,tmp2;
	scanf("%d%d",&n,&r);
	int ans=0;
	for(int i=0;i<n;i++)
	{
		scanf("%d%d",&tmp1,&tmp2);
		if(tmp2>=0)
		{
			node1 tmp;
			tmp.num=tmp1;
			tmp.d=tmp2;
			q1.push(tmp);
		}
		else
		{
			node2 tmp;
			tmp.num=tmp1+tmp2;
			tmp.num=max(tmp.num,0);
			tmp.d=tmp2;		
			q2.push(tmp);
		}
	}
	while(!q1.empty())
	{
		node1 tmp=q1.top();
		q1.pop();
		if(r<tmp.num)		
		{
			continue;
		}
		r+=tmp.d;
		ans++;
	}
	while(!q2.empty())
	{
		node2 tmp=q2.top();
		q2.pop();
		r+=tmp.d;
		q.push(tmp.d);
		if(r<tmp.num)
		{
			r-=q.top();
			q.pop();	
		}
	}
	ans+=q.size();
	printf("%d\n",ans);
	return 0;
}
内容概要:本书《Deep Reinforcement Learning with Guaranteed Performance》探讨了基于李雅普诺夫方法的深度强化学习及其在非线性系统最优控制中的应用。书中提出了一种近似最优自适应控制方法,结合泰勒展开、神经网络、估计器设计及滑模控制思想,解决了不同场景下的跟踪控制问题。该方法不仅保证了性能指标的渐近收敛,还确保了跟踪误差的渐近收敛至零。此外,书中还涉及了执行器饱和、冗余解析等问题,并提出了新的冗余解析方法,验证了所提方法的有效性和优越性。 适合人群:研究生及以上学历的研究人员,特别是从事自适应/最优控制、机器人学和动态神经网络领域的学术界和工业界研究人员。 使用场景及目标:①研究非线性系统的最优控制问题,特别是在存在输入约束和系统动力学的情况下;②解决带有参数不确定性的线性和非线性系统的跟踪控制问题;③探索基于李雅普诺夫方法的深度强化学习在非线性系统控制中的应用;④设计和验证针对冗余机械臂的新型冗余解析方法。 其他说明:本书分为七章,每章内容相对独立,便于读者理解。书中不仅提供了理论分析,还通过实际应用(如欠驱动船舶、冗余机械臂)验证了所提方法的有效性。此外,作者鼓励读者通过仿真和实验进一步验证书中提出的理论和技术。
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