poj 2187Beauty Contest(凸包)

本文介绍了一种算法,用于计算二维平面上多个农场间的最大距离,以帮助一头名为Bessie的牛确定旅行中携带的食物数量。通过极角排序和凸包算法优化计算过程。
Beauty Contest
Time Limit: 3000MS Memory Limit: 65536K
Total Submissions: 35138 Accepted: 10894

Description

Bessie, Farmer John's prize cow, has just won first place in a bovine beauty contest, earning the title 'Miss Cow World'. As a result, Bessie will make a tour of N (2 <= N <= 50,000) farms around the world in order to spread goodwill between farmers and their cows. For simplicity, the world will be represented as a two-dimensional plane, where each farm is located at a pair of integer coordinates (x,y), each having a value in the range -10,000 ... 10,000. No two farms share the same pair of coordinates.

Even though Bessie travels directly in a straight line between pairs of farms, the distance between some farms can be quite large, so she wants to bring a suitcase full of hay with her so she has enough food to eat on each leg of her journey. Since Bessie refills her suitcase at every farm she visits, she wants to determine the maximum possible distance she might need to travel so she knows the size of suitcase she must bring.Help Bessie by computing the maximum distance among all pairs of farms.

Input

* Line 1: A single integer, N

* Lines 2..N+1: Two space-separated integers x and y specifying coordinate of each farm

Output

* Line 1: A single integer that is the squared distance between the pair of farms that are farthest apart from each other.

Sample Input

4
0 0
0 1
1 1
1 0

Sample Output

2
 /*
9
1 2
2 2
0 1
1 1
2 1
3 1
0 0
1 0
2 0

10
*/
#include<stdio.h>
#include<math.h>
#include<algorithm>
using namespace std;
int n,top;
struct point
{
    int x,y;
}pt[51000],Stack[51000],pointa;
int cross(point a,point b,point c)
{
    return (b.x-a.x)*(c.y-a.y)-(b.y-a.y)*(c.x-a.x);
}
int cmp(point a,point b)
{
    if(a.y==b.y)
        return a.x<b.x;
      return a.y<b.y;
}

int cmp1(point a,point b)
{
    double k = cross(pointa,a,b);
    if(k>0) return 1;
    if(k<0) return 0;
    return pow(a.x-pointa.x,2)+pow(a.y-pointa.y,2)>pow(b.x-pointa.x,2) + pow(b.y-pointa.y,2);//当共线时候,距离按照从大到小排时
                                                                                             //下文cross()<=0;
                                                                                             //当从小到大排时cross()<0;
}                                                                                            //注意   从大到小排序  共线时只记录远的不记录近的,从小到大排都记录
void gram()
{
    sort(pt,pt+n,cmp);
    pointa = pt[0];
    pt[n] = pt[0];
    sort(pt+1,pt+n,cmp1);
    int i;
    top=2;
    Stack[0]=pt[0];
    Stack[1]=pt[1];
    Stack[2]=pt[2];
    for(i=3;i<=n;i++)
    {
        while(top>=2&&cross(Stack[top-1],Stack[top],pt[i])<=0)//如果是<=0那么极角排序时当共线时距离从大到小排
            top--;                                            //如果<0那么极角排序时当共线时距离从小到大排
        Stack[++top] = pt[i];
    }
}
int main()
{
    int i,j;
    while(~scanf("%d",&n))
    {
        for(i=0;i<n;i++)
            scanf("%d%d",&pt[i].x,&pt[i].y);
        gram();
        int max = 0;

             for(i=0;i<=top;i++)
            {
                for(j=0;j<=top;j++)
                {
                    if(max < pow( Stack[i].x-Stack[j].x,2 ) + pow( Stack[i].y-Stack[j].y,2 ) )
                        max = pow(Stack[i].x-Stack[j].x,2)+pow(Stack[i].y-Stack[j].y,2);
                }
            }
            printf("%d\n",max);
    }
    return 0;
}

基于径向基函数神经网络RBFNN的自适应滑模控制学习(Matlab代码实现)内容概要:本文介绍了基于径向基函数神经网络(RBFNN)的自适应滑模控制方法,并提供了相应的Matlab代码实现。该方法结合了RBF神经网络的非线性逼近能力和滑模控制的强鲁棒性,用于解决复杂系统的控制问题,尤其适用于存在不确定性和外部干扰的动态系统。文中详细阐述了控制算法的设计思路、RBFNN的结构与权重更新机制、滑模面的构建以及自适应律的推导过程,并通过Matlab仿真验证了所提方法的有效性和稳定性。此外,文档还列举了大量相关的科研方向和技术应用,涵盖智能优化算法、机器学习、电力系统、路径规划等多个领域,展示了该技术的广泛应用前景。; 适合人群:具备一定自动控制理论基础和Matlab编程能力的研究生、科研人员及工程技术人员,特别是从事智能控制、非线性系统控制及相关领域的研究人员; 使用场景及目标:①学习和掌握RBF神经网络与滑模控制相结合的自适应控制策略设计方法;②应用于电机控制、机器人轨迹跟踪、电力电子系统等存在模型不确定性或外界扰动的实际控制系统中,提升控制精度与鲁棒性; 阅读建议:建议读者结合提供的Matlab代码进行仿真实践,深入理解算法实现细节,同时可参考文中提及的相关技术方向拓展研究思路,注重理论分析与仿真验证相结合。
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