[bzoj1629][Usaco2007 Demo]Cow Acrobats

探讨了如何通过算法确定牛叠罗汉游戏的最佳顺序,以最小化最危险情况的风险值。采用排序与遍历的方法,实现了计算最优解的目标。

1629: [Usaco2007 Demo]Cow Acrobats

Time Limit: 5 Sec Memory Limit: 64 MB
Submit: 975 Solved: 507
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Description

Farmer John’s N (1 <= N <= 50,000) cows (numbered 1..N) are planning to run away and join the circus. Their hoofed feet prevent them from tightrope walking and swinging from the trapeze (and their last attempt at firing a cow out of a cannon met with a dismal failure). Thus, they have decided to practice performing acrobatic stunts. The cows aren’t terribly creative and have only come up with one acrobatic stunt: standing on top of each other to form a vertical stack of some height. The cows are trying to figure out the order in which they should arrange themselves within this stack. Each of the N cows has an associated weight (1 <= W_i <= 10,000) and strength (1 <= S_i <= 1,000,000,000). The risk of a cow collapsing is equal to the combined weight of all cows on top of her (not including her own weight, of course) minus her strength (so that a stronger cow has a lower risk). Your task is to determine an ordering of the cows that minimizes the greatest risk of collapse for any of the cows. //有三个头牛,下面三行二个数分别代表其体重及力量 //它们玩叠罗汉的游戏,每个牛的危险值等于它上面的牛的体重总和减去它的力量值,因为它要扛起上面所有的牛嘛. //求所有方案中危险值最大的最小
Input

  • Line 1: A single line with the integer N. * Lines 2..N+1: Line i+1 describes cow i with two space-separated integers, W_i and S_i.
    Output

  • Line 1: A single integer, giving the largest risk of all the cows in any optimal ordering that minimizes the risk.
    Sample Input

3

10 3

2 5

3 3

Sample Output

2

OUTPUT DETAILS:

Put the cow with weight 10 on the bottom. She will carry the other

two cows, so the risk of her collapsing is 2+3-3=2. The other cows

have lower risk of collapsing.

HINT

Source

Silver

sol:
如果2只奶牛交换位置。设奶牛的力量为s,重量为w,设1号奶牛更优秀,则有w1+sum-s2 < w2+sum-s1(另一牛在上方),整理w1+s1 < w2+s2,则总和小的更加优秀。

#include<cstdio>
#include<algorithm>
#include<string>
#include<cstring>
#include<cstdlib>
#include<cmath>
#include<iostream>
using namespace std;
int n,m;
inline int read()
{
    char c;
    int res,flag=0;
    while((c=getchar())>'9'||c<'0') if(c=='-')flag=1;
    res=c-'0';
    while((c=getchar())>='0'&&c<='9') res=(res<<3)+(res<<1)+c-'0';
    return flag?-res:res;
}
const int N=1000000;
struct cc
{
    int x,y;
}a[N];
inline bool cmp(const cc &a,const cc &b)
{
    return a.x+a.y<b.x+b.y;
}
int main()
{
//  freopen("1629.in","r",stdin);
//  freopen(".out","w",stdout);
    n=read();
    for(int i=1;i<=n;++i)
    a[i].x=read(),a[i].y=read();
    sort(a+1,a+1+n,cmp);
    int sum=0,ans=-1e9;
    for(int i=1;i<=n;++i)
    {
        ans=max(ans,sum-a[i].y);
        sum+=a[i].x;
    }
    printf("%d",ans);
}

内容概要:本文系统介绍了算术优化算法(AOA)的基本原理、核心思想及Python实现方法,并通过图像分割的实际案例展示了其应用价值。AOA是一种基于种群的元启发式算法,其核心思想来源于四则运算,利用乘除运算进行全局勘探,加减运算进行局部开发,通过数学优化器加速函数(MOA)和数学优化概率(MOP)动态控制搜索过程,在全局探索与局部开发之间实现平衡。文章详细解析了算法的初始化、勘探与开发阶段的更新策略,并提供了完整的Python代码实现,结合Rastrigin函数进行测试验证。进一步地,以Flask框架搭建前后端分离系统,将AOA应用于图像分割任务,展示了其在实际工程中的可行性与高效性。最后,通过收敛速度、寻优精度等指标评估算法性能,并提出自适应参数调整、模型优化和并行计算等改进策略。; 适合人群:具备一定Python编程基础和优化算法基础知识的高校学生、科研人员及工程技术人员,尤其适合从事人工智能、图像处理、智能优化等领域的从业者;; 使用场景及目标:①理解元启发式算法的设计思想与实现机制;②掌握AOA在函数优化、图像分割等实际问题中的建模与求解方法;③学习如何将优化算法集成到Web系统中实现工程化应用;④为算法性能评估与改进提供实践参考; 阅读建议:建议读者结合代码逐行调试,深入理解算法流程中MOA与MOP的作用机制,尝试在不同测试函数上运行算法以观察性能差异,并可进一步扩展图像分割模块,引入更复杂的预处理或后处理技术以提升分割效果。
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