POJ Gold Balanced Lineup

本文介绍了一种算法,用于解决在一群具有多种特征的奶牛中找出最大的连续子区间,使得该区间内的每一种特征都被相同数量的奶牛展示的问题。通过将每头奶牛的特征表示为二进制形式并进行特定处理,最终能够高效地找到这个最大平衡范围。
Gold Balanced Lineup
Time Limit: 2000MS Memory Limit: 65536K
Total Submissions: 13141 Accepted: 3849

Description

Farmer John's N cows (1 ≤ N ≤ 100,000) share many similarities. In fact, FJ has been able to narrow down the list of features shared by his cows to a list of only K different features (1 ≤ K ≤ 30). For example, cows exhibiting feature #1 might have spots, cows exhibiting feature #2 might prefer C to Pascal, and so on.

FJ has even devised a concise way to describe each cow in terms of its "feature ID", a single K-bit integer whose binary representation tells us the set of features exhibited by the cow. As an example, suppose a cow has feature ID = 13. Since 13 written in binary is 1101, this means our cow exhibits features 1, 3, and 4 (reading right to left), but not feature 2. More generally, we find a 1 in the 2^(i-1) place if a cow exhibits feature i.

Always the sensitive fellow, FJ lined up cows 1..N in a long row and noticed that certain ranges of cows are somewhat "balanced" in terms of the features the exhibit. A contiguous range of cows i..j is balanced if each of the K possible features is exhibited by the same number of cows in the range. FJ is curious as to the size of the largest balanced range of cows. See if you can determine it.

Input

Line 1: Two space-separated integers, N and K
Lines 2..N+1: Line i+1 contains a single K-bit integer specifying the features present in cow i. The least-significant bit of this integer is 1 if the cow exhibits feature #1, and the most-significant bit is 1 if the cow exhibits feature #K.

Output

Line 1: A single integer giving the size of the largest contiguous balanced group of cows.

Sample Input

7 3
7
6
7
2
1
4
2

Sample Output

4


把所有数的二进制按位相加存在sum[][]中,对于满足题意的i,j两行 ,即有:

   sum[i][0]-sum[j][0] = sum[i][1]-sum[j][1]=.....=sum[i][k]-sum[j][k] 

   (即对于i,j两行,每种特性出现的次数相同,即是平衡的)

   可以得到

   sum[i][1]-sum[i][0] = sum[j][1]-sum[j][0]

   sum[i][2]-sum[i][0] = sum[j][2]-sum[j][0]

   .

   .

   sum[i][k]-sum[i][0] = sum[j][k]-sum[j][0]

   所以对于对于得到的sum 减去任意一列的值(这里选的第一列),所得到的值中,完全相同的两行是平衡的

   剩下的可以对这些二进制排下序,找到相同两行的最大差值

   

#include <cstdio>
#include <cstring>
#include <stdlib.h>
#include <algorithm>
#include <iostream>
#include <cmath>
#define N 100010
using namespace std;
int a[N],n,k;
int sum[N][31];
struct node
{
    int s[31];
    int b;
} q[N];
int cmp(const void *a,const void *b)
{
    for(int i=1; i<=k; i++)
    {
        if( (*(node *)a).s[i] != (*(node *)b).s[i])
            return (*(node *)a).s[i] - (*(node *)b).s[i];
    }
    return (*(node *)a).b - (*(node *)b).b;
}
bool flag(int j)
{
    for(int i=1; i<=k; i++)
        if( q[j].s[i] != q[j-1].s[i])
            return false;
    return true;
}
int main()
{
    while(~scanf("%d%d",&n,&k))
    {
        for(int i=1; i<=n; i++)
            scanf("%d",&a[i]);
        for(int i=0; i<=k; i++)
            sum[0][i]=0;
        for(int i=1 ; i<=n; i++)
        {
            int t=a[i];
            for(int j=1; j<=k; j++)
            {
                sum[i][j]+=sum[i-1][j]+t%2;
                t=t/2;
            }
        }

        for(int i=1; i<=k; i++)
            q[0].s[i]=0;
        for(int i=1; i<=n; i++)
        {
            for(int j=1; j<=k; j++)
                q[i].s[j] = sum[i][j] - sum[i][1];
            q[i].b=i;
        }

        qsort(q,n+1,sizeof(q[0]),cmp);
        int ans=0;
        int start=0,end=0;
        for(int i=1; i<=n; i++)
        {
            if(flag(i))
            {
                end=i;
            }
            else
            {
                ans=max(ans,q[end].b - q[start].b);
                start=i;
                end=i;
            }
        }
        ans=max(ans,q[end].b - q[start].b);
        printf("%d\n",ans);
    }
    return 0;
}

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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