杭电多校第一场1004[优先队列+模拟]

本文解析了一道关于车辆追击问题的算法竞赛题目,通过优先队列和并查集的方法,解决多辆车在受前车速度限制的情况下,计算最后一辆车通过终点线的最短时间问题。

1004 Vacation
Time Limit: 10000/5000 MS (Java/Others) Memory Limit: 262144/262144 K (Java/Others)
Total Submission(s): 2394 Accepted Submission(s): 1079
Special Judge

Problem Description
Tom and Jerry are going on a vacation. They are now driving on a one-way road and several cars are in front of them. To be more specific, there are n cars in front of them. The ith car has a length of li, the head of it is si from the stop-line, and its maximum velocity is vi. The car Tom and Jerry are driving is l0 in length, and s0 from the stop-line, with a maximum velocity of v0.
The traffic light has a very long cycle. You can assume that it is always green light. However, since the road is too narrow, no car can get ahead of other cars. Even if your speed can be greater than the car in front of you, you still can only drive at the same speed as the anterior car. But when not affected by the car ahead, the driver will drive at the maximum speed. You can assume that every driver here is very good at driving, so that the distance of adjacent cars can be kept to be 0.
Though Tom and Jerry know that they can pass the stop-line during green light, they still want to know the minimum time they need to pass the stop-line. We say a car passes the stop-line once the head of the car passes it.
Please notice that even after a car passes the stop-line, it still runs on the road, and cannot be overtaken.

Input
This problem contains multiple test cases.
For each test case, the first line contains an integer n (1≤n≤105,∑n≤2×106), the number of cars.
The next three lines each contains n+1 integers, li,si,vi (1≤si,vi,li≤109). It’s guaranteed that si≥si+1+li+1,∀i∈[0,n−1]

Output
For each test case, output one line containing the answer. Your answer will be accepted if its absolute or relative error does not exceed 10−6.
Formally, let your answer be a, and the jury’s answer is b. Your answer is considered correct if |a−b|max(1,|b|)≤10−6.
The answer is guaranteed to exist.

Sample Input
1
2 2
7 1
2 1
2
1 2 2
10 7 1
6 2 1

Sample Output
3.5000000000
5.0000000000

Source
2019 Multi-University Training Contest 1

题意:按照距离终点线的长短顺序给出n+1辆车距离终点线的距离Si,最高速度Vi,车长Li,问最远的那辆车什么时候可以到终点线

题解:由于每辆车被前面的车堵住之后只会影响它后面的车追上它的时间并且这个时间是减小的,所以可以把这n个追击问题丢到优先队列里,借助优先队列每次取出最先发生的时间,这个过程可以得到每个追击问题完成的时间,接下来模拟即可。(追击过程可以借助并查集来维护追击之后连在一起的车从而维护每个车真正会影响到的"后面的车"(也就是同一个集合里最左边的车后面的车))

#include<bits/stdc++.h>
using namespace std;
typedef long long ll;
#define debug(x) cout<<#x<<" is "<<x<<endl;
const int maxn=1e5+5;
struct pot{
    int id;
    double tim;
    double dis;
    bool operator<(const struct pot &aa)const{
        return tim>aa.tim;
    }
};
struct pot2{
    int id;
    double tim;
}acc[maxn];
bool cmp(struct pot2 aa,struct pot2 bb){
    return aa.tim<bb.tim;
}
double vis[maxn];
ll l[maxn],s[maxn],v[maxn],fa[maxn],L[maxn],fa2[maxn],sum[maxn];
int finds(int x){
    int xx=x;
    while(fa[x]!=x){
        x=fa[x];
    }
    while(fa[xx]!=x){
        int t=fa[xx];
        fa[xx]=x;
        xx=t;
    }
    return x;
}
int find2(int x){
    int xx=x;
    while(fa2[x]!=x){
        x=fa2[x];
    }
    while(fa2[xx]!=x){
        int t=fa2[xx];
        fa2[xx]=x;
        xx=t;
    }
    return x;
}
int main(){
    int n;
    //freopen("in.txt","r",stdin);
    //freopen("out.txt","w",stdout);
    while(scanf("%d",&n)!=EOF){
        for(int i=1;i<=n+1;i++)vis[i]=-1;
        for(int i=1;i<=n+1;i++)L[i]=fa[i]=i;
        for(int i=1;i<=n+1;i++){
            scanf("%lld",&l[i]);
            sum[i]=sum[i-1]+l[i];
        }
        for(int i=1;i<=n+1;i++){
            scanf("%lld",&s[i]);
        }
        for(int i=1;i<=n+1;i++){
            scanf("%lld",&v[i]);
        }
        priority_queue<struct pot>pq;
        while(!pq.empty())pq.pop();
        for(int i=1;i<=n;i++){
            struct pot ac;
            if(v[i]>=v[i+1]&&(s[i]-s[i+1]-l[i+1])==0){
                ac.id=i;
                ac.tim=0;
                ac.dis=ac.tim*v[ac.id];
                pq.push(ac);
                continue;
            }
            if(v[i]<=v[i+1])continue;
            ac.id=i;
            ac.tim=1.0*(s[i]-s[i+1]-l[i+1])/(v[i]-v[i+1]);
            ac.dis=ac.tim*v[ac.id];
            pq.push(ac);
        }
        while(!pq.empty()){
            struct pot ak=pq.top();
            pq.pop();
            if(vis[ak.id]!=-1)continue;
            vis[ak.id]=ak.tim;
            int f1=finds(ak.id+1);
            int f2=finds(ak.id);
            if(L[f2]>1&&vis[L[f2]-1]==-1&&v[L[f2]-1]>v[f1]){
                struct pot cxk;
                cxk.id=L[f2]-1;
                cxk.tim=ak.tim+1.0*(s[L[f2]-1]-(s[f2]-ak.dis+sum[f2]-sum[L[f2]-1])-ak.tim*v[L[f2]-1])/(v[L[f2]-1]-v[f1]);
                cxk.dis=cxk.tim*v[L[f2]-1];
                pq.push(cxk);
            }
            fa[f2]=f1;
            L[f1]=min(L[f1],L[f2]);
        }
        double ans=0;
        double ww=1.0*s[1]/v[1];
        if(vis[1]==-1||ww<=vis[1]){
            ans=ww;
        }
        else{
            int tot=0;
            for(int i=1;i<=n;i++){
                if(vis[i]==-1)continue;
                acc[++tot].id=i;
                acc[tot].tim=vis[i];
            }
            sort(acc+1,acc+tot+1,cmp);
            for(int i=1;i<=n+1;i++)fa2[i]=i;
            double res=s[1];
            for(int i=1;i<=tot;i++){
                int f1=find2(acc[i].id+1);
                int f2=find2(acc[i].id);
                int f3=find2(1);
                if(res>(acc[i].tim-ans)*v[f3]){
                    res-=(acc[i].tim-ans)*v[f3];
                    ans=acc[i].tim;
                }
                else{
                    ans+=res/v[f3];
                    res=0;
                    break;
                }
                fa2[f2]=f1;
            }
            if(res){
                int ff=find2(1);
                ans+=res/v[ff];
                res=0;
            }
        }
        printf("%.10f\n",ans);
    }
    return 0;
}
内容概要:本文介绍了基于贝叶斯优化的CNN-LSTM混合神经网络在时间序列预测中的应用,并提供了完整的Matlab代码实现。该模型结合了卷积神经网络(CNN)在特征提取方面的优势与长短期记忆网络(LSTM)在处理时序依赖问题上的强大能力,形成一种高效的混合预测架构。通过贝叶斯优化算法自动调参,提升了模型的预测精度与泛化能力,适用于风电、光伏、负荷、交通流等种复杂非线性系统的预测任务。文中还展示了模型训练流程、参数优化机制及实际预测效果分析,突出其在科研与工程应用中的实用性。; 适合人群:具备一定机器学习基基于贝叶斯优化CNN-LSTM混合神经网络预测(Matlab代码实现)础和Matlab编程经验的高研究生、科研人员及从事预测建模的工程技术人员,尤其适合关注深度学习与智能优化算法结合应用的研究者。; 使用场景及目标:①解决各类时间序列预测问题,如能源出力预测、电力负荷预测、环境数据预测等;②学习如何将CNN-LSTM模型与贝叶斯优化相结合,提升模型性能;③掌握Matlab环境下深度学习模型搭建与超参数自动优化的技术路线。; 阅读建议:建议读者结合提供的Matlab代码进行实践操作,重点关注贝叶斯优化模块与混合神经网络结构的设计逻辑,通过调整数据集和参数加深对模型工作机制的理解,同时可将其框架迁移至其他预测场景中验证效果。
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