POJ 3667 Hotel

本文介绍了一个用于处理酒店房间分配请求的高效算法。该算法通过维护一个特殊的数状数组来跟踪房间占用状态,并能够快速响应入住和退房操作,确保客户能够获得连续的可用房间。
Hotel
Time Limit: 3000MS Memory Limit: 65536K
Total Submissions: 9597 Accepted: 4113

Description

The cows are journeying north to Thunder Bay in Canada to gain cultural enrichment and enjoy a vacation on the sunny shores of Lake Superior. Bessie, ever the competent travel agent, has named the Bullmoose Hotel on famed Cumberland Street as their vacation residence. This immense hotel has N (1 ≤ N ≤ 50,000) rooms all located on the same side of an extremely long hallway (all the better to see the lake, of course).

The cows and other visitors arrive in groups of size Di (1 ≤ Di ≤ N) and approach the front desk to check in. Each group i requests a set of Di contiguous rooms from Canmuu, the moose staffing the counter. He assigns them some set of consecutive room numbers r..r+Di-1 if they are available or, if no contiguous set of rooms is available, politely suggests alternate lodging. Canmuu always chooses the value of r to be the smallest possible.

Visitors also depart the hotel from groups of contiguous rooms. Checkout i has the parameters Xi and Di which specify the vacating of rooms Xi ..Xi +Di-1 (1 ≤ XiN-Di+1). Some (or all) of those rooms might be empty before the checkout.

Your job is to assist Canmuu by processing M (1 ≤ M < 50,000) checkin/checkout requests. The hotel is initially unoccupied.

Input

* Line 1: Two space-separated integers: N and M
* Lines 2..M+1: Line i+1 contains request expressed as one of two possible formats: (a) Two space separated integers representing a check-in request: 1 and Di (b) Three space-separated integers representing a check-out: 2, Xi, and Di

Output

* Lines 1.....: For each check-in request, output a single line with a single integer r, the first room in the contiguous sequence of rooms to be occupied. If the request cannot be satisfied, output 0.

Sample Input

10 6
1 3
1 3
1 3
1 3
2 5 5
1 6

Sample Output

1
4
7
0
5

num数组记录该区间是否住慢,0代表都是空的,1代表都住了人。-1代表有的住了,有的空着。

d[rt][0]代表从区间最左端开始的空的最长长度,d[rt][1]代表从区间最右端开始的空的最长长度,d[rt][2]代表区间的最长空的长度。


#include<cstdio>

#define maxn  51111
#define lson l,m,rt<<1
#define rson m+1,r,rt<<1|1
#define Max(a,b) (a>b?a:b)

int num[maxn<<2],d[maxn<<2][3];

void PushDown(int rt,int tmp)
{
	if(num[rt]==-1)
		return ;
	num[rt<<1]=num[rt<<1|1]=num[rt];
	if(num[rt]==0)
	{
		d[rt<<1][0]=d[rt<<1][1]=d[rt<<1][2]=(tmp+1)>>1;
		d[rt<<1|1][0]=d[rt<<1|1][1]=d[rt<<1|1][2]=tmp>>1;
	}
	else
	{
		d[rt<<1][0]=d[rt<<1][1]=d[rt<<1][2]=0;
		d[rt<<1|1][0]=d[rt<<1|1][1]=d[rt<<1|1][2]=0;
	}
	num[rt]=-1;
}

void build(int l,int r,int rt)
{
	num[rt]=0;
	for(int i=0;i<3;i++)
		d[rt][i]=r-l+1;
	if(l==r)
		return ;
	int m=(l+r)>>1;
	build(lson);
	build(rson);
}

int check(int l,int r,int rt,int D)								//查找满足条件的最左点
{
	if(d[rt][2]<D)
		return 0;
	PushDown(rt,r-l+1);
	int m=(l+r)>>1;
	if(d[rt<<1][2]>=D)
		return check(lson,D);
	if(d[rt<<1][1]+d[rt<<1|1][0]>=D)
		return m-d[rt<<1][1]+1;
	return check(rson,D);
}

void PushUp(int rt,int tmp)
{
	if(d[rt<<1][0] < (tmp+1)>>1)
		d[rt][0]=d[rt<<1][0];
	else
		d[rt][0]=((tmp+1)>>1)+d[rt<<1|1][0];

	if(d[rt<<1|1][1]<tmp>>1)
		d[rt][1]=d[rt<<1|1][1];
	else
		d[rt][1]=d[rt<<1][1]+(tmp>>1);

	d[rt][2]=Max(d[rt<<1][2],d[rt<<1|1][2]);
	d[rt][2]=Max(d[rt][2],d[rt<<1][1]+d[rt<<1|1][0]);
}

void updata(int l,int r,int rt,int L,int R,int flag)
{
	if(l>=L && r<=R)
	{
		num[rt]=flag;
		if(flag==1)
			d[rt][0]=d[rt][1]=d[rt][2]=0;
		else
			d[rt][0]=d[rt][1]=d[rt][2]=r-l+1;
		return ;
	}
	PushDown(rt,r-l+1);
	int m=(l+r)>>1;
	if(L<=m)
		updata(lson,L,R,flag);
	if(R>m)
		updata(rson,L,R,flag);
	PushUp(rt,r-l+1);
}

int main()
{
	int N,M;
	int flag,X,D,tmp;

	while(scanf("%d%d",&N,&M)==2)
	{
		build(1,N,1);

		while(M--)
		{
			scanf("%d",&flag);

			if(flag==1)
			{
				scanf("%d",&D);
				tmp=check(1,N,1,D);
				printf("%d\n",tmp);
				if(tmp!=0)
				{
					updata(1,N,1,tmp,tmp+D-1,1);
				}
			}
			if(flag==2)
			{
				scanf("%d%d",&X,&D);
				updata(1,N,1,X,X+D-1,0);
			}
		}
	}

	return 0;
}


基于数据驱动的 Koopman 算子的递归神经网络模型线性化,用于纳米定位系统的预测控制研究(Matlab代码实现)内容概要:本文围绕“基于数据驱动的Koopman算子的递归神经网络模型线性化”展开,旨在研究纳米定位系统的预测控制问题,并提供完整的Matlab代码实现。文章结合数据驱动方法与Koopman算子理论,利用递归神经网络(RNN)对非线性系统进行建模与线性化处理,从而提升纳米级定位系统的精度与动态响应性能。该方法通过提取系统隐含动态特征,构建近似线性模型,便于后续模型预测控制(MPC)的设计与优化,适用于高精度自动化控制场景。文中还展示了相关实验验证与仿真结果,证明了该方法的有效性和先进性。; 适合人群:具备一定控制理论基础和Matlab编程能力,从事精密控制、智能制造、自动化或相关领域研究的研究生、科研人员及工程技术人员。; 使用场景及目标:①应用于纳米级精密定位系统(如原子力显微镜、半导体制造设备)中的高性能控制设计;②为非线性系统建模与线性化提供一种结合深度学习与现代控制理论的新思路;③帮助读者掌握Koopman算子、RNN建模与模型预测控制的综合应用。; 阅读建议:建议读者结合提供的Matlab代码逐段理解算法实现流程,重点关注数据预处理、RNN结构设计、Koopman观测矩阵构建及MPC控制器集成等关键环节,并可通过更换实际系统数据进行迁移验证,深化对方法泛化能力的理解。
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