<cf>Walking in the Rain

在Berland,反对派计划举行一场沿着林荫大道的大规模游行。此林荫大道由若干块瓷砖组成,每块瓷砖会在特定天数后因雨水侵蚀而损坏。任务是计算从起点到终点的路径能在多少天内保持可用。
B. Walking in the Rain
time limit per test
2 seconds
memory limit per test
256 megabytes
input
standard input
output
standard output

In Berland the opposition is going to arrange mass walking on the boulevard. The boulevard consists ofn tiles that are lain in a row and are numbered from 1 to n from right to left. The opposition should start walking on the tile number 1 and the finish on the tile number n. During the walk it is allowed to move from right to left between adjacent tiles in a row, and jump over a tile. More formally, if you are standing on the tile number i (i < n - 1), you can reach the tiles number i + 1 or the tile number i + 2 from it (if you stand on the tile number n - 1, you can only reach tile number n). We can assume that all the opposition movements occur instantaneously.

In order to thwart an opposition rally, the Berland bloody regime organized the rain. The tiles on the boulevard are of poor quality and they are rapidly destroyed in the rain. We know that the i-th tile is destroyed after ai days of rain (on day ai tile isn't destroyed yet, and on day ai + 1 it is already destroyed). Of course, no one is allowed to walk on the destroyed tiles! So the walk of the opposition is considered thwarted, if either the tile number 1 is broken, or the tile number n is broken, or it is impossible to reach the tile number n from the tile number 1 if we can walk on undestroyed tiles.

The opposition wants to gather more supporters for their walk. Therefore, the more time they have to pack, the better. Help the opposition to calculate how much time they still have and tell us for how many days the walk from the tile number 1 to the tile number n will be possible.

Input

The first line contains integer n (1 ≤ n ≤ 103) — the boulevard's length in tiles.

The second line contains n space-separated integers ai — the number of days after which the i-th tile gets destroyed (1 ≤ ai ≤ 103).

Output

Print a single number — the sought number of days.

Sample test(s)
input
4
10 3 5 10
output
5
input
5
10 2 8 3 5
output
5
Note

In the first sample the second tile gets destroyed after day three, and the only path left is 1 → 3 → 4. After day five there is a two-tile gap between the first and the last tile, you can't jump over it.

In the second sample path 1 → 3 → 5 is available up to day five, inclusive. On day six the last tile is destroyed and the walk is thwarted.

AC Code:

#include <iostream>
#include <algorithm>
using namespace std;

struct Days_Destroyed
{
    int i;//tile的序号
    int day;//要使 i-th tile 被毁的日数
}a[1001];
bool dest[1001]; //记录是否被毁,默认为假,即木有被毁

int cmp(const void *a,const void *b)
{
    struct Days_Destroyed* aa=(Days_Destroyed*)a;
    struct Days_Destroyed* bb=(Days_Destroyed*)b;
    return aa->day-bb->day;
}

int main()
{
    int n,MaxDays,j;
    while(cin>>n)
    {
        MaxDays=1;
        for(j=1;j<=n;j++)
        {
            cin>>a[j].day;
            a[j].i=j;
            dest[j]=false;
        }
        qsort(a+1,n,sizeof(a[0]),cmp);//快速排序
        for(j=1;j<=n;j++)
        {
            //如果是第一或最后一个tile被毁,又或相邻两个tile之一被毁,当前tile就不能再被毁
            if(a[j].i==1 || a[j].i==n || dest[a[j].i-1] || dest[a[j].i+1])
            {
                MaxDays=a[j].day;//注意不能直接break,要先执行这一步
                break;
            }
            else
            {
                MaxDays=a[j].day;
                dest[a[j].i]=true;//标记为已被破坏
            }
        }
        cout<<MaxDays<<endl;
    }
    return 0;
}


内容概要:本文提出了一种基于融合鱼鹰算法和柯西变异的改进麻雀优化算法(OCSSA),用于优化变分模态分解(VMD)的参数,进而结合卷积神经网络(CNN)与双向长短期记忆网络(BiLSTM)构建OCSSA-VMD-CNN-BILSTM模型,实现对轴承故障的高【轴承故障诊断】基于融合鱼鹰和柯西变异的麻雀优化算法OCSSA-VMD-CNN-BILSTM轴承诊断研究【西储大学数据】(Matlab代码实现)精度诊断。研究采用西储大学公开的轴承故障数据集进行实验验证,通过优化VMD的模态数和惩罚因子,有效提升了信号分解的准确性与稳定性,随后利用CNN提取故障特征,BiLSTM捕捉时间序列的深层依赖关系,最终实现故障类型的智能识别。该方法在提升故障诊断精度与鲁棒性方面表现出优越性能。; 适合人群:具备一定信号处理、机器学习基础,从事机械故障诊断、智能运维、工业大数据分析等相关领域的研究生、科研人员及工程技术人员。; 使用场景及目标:①解决传统VMD参数依赖人工经验选取的问题,实现参数自适应优化;②提升复杂工况下滚动轴承早期故障的识别准确率;③为智能制造与预测性维护提供可靠的技术支持。; 阅读建议:建议读者结合Matlab代码实现过程,深入理解OCSSA优化机制、VMD信号分解流程以及CNN-BiLSTM网络架构的设计逻辑,重点关注参数优化与故障分类的联动关系,并可通过更换数据集进一步验证模型泛化能力。
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