poj3264

Balanced Lineup
Time Limit: 5000MS Memory Limit: 65536K
Total Submissions: 23031 Accepted: 10702
Case Time Limit: 2000MS

Description

For the daily milking, Farmer John's N cows (1 ≤ N ≤ 50,000) always line up in the same order. One day Farmer John decides to organize a game of Ultimate Frisbee with some of the cows. To keep things simple, he will take a contiguous range of cows from the milking lineup to play the game. However, for all the cows to have fun they should not differ too much in height.

Farmer John has made a list of Q (1 ≤ Q ≤ 200,000) potential groups of cows and their heights (1 ≤ height ≤ 1,000,000). For each group, he wants your help to determine the difference in height between the shortest and the tallest cow in the group.

Input

Line 1: Two space-separated integers,  N and  Q
Lines 2.. N+1: Line  i+1 contains a single integer that is the height of cow  i 
Lines  N+2.. N+ Q+1: Two integers  A and  B (1 ≤  A ≤  B ≤  N), representing the range of cows from  A to  B inclusive.

Output

Lines 1.. Q: Each line contains a single integer that is a response to a reply and indicates the difference in height between the tallest and shortest cow in the range.

Sample Input

6 3
1
7
3
4
2
5
1 5
4 6
2 2

Sample Output

6
3
0

Source

线段树。
#include<iostream>
#include<algorithm>
using namespace std;
#define MY_MAX -99999999
#define MY_MIN 999999999

struct CNode {
    int L, R;
    int nMin, nMax;
    CNode * pLeft, * pRight;
};
int nMax, nMin;
CNode Tree[1000000]; //其实两倍叶子节点的数量+1就够 int nCount = 0; //总节点数目
int nCount = 0;

void BuildTree(CNode * pRoot, int L, int R) {
    pRoot->L = L;
    pRoot->R = R;
    pRoot->nMin = MY_MIN;
    pRoot->nMax = MY_MAX;
    if (L != R) {
        nCount++;
        pRoot->pLeft = Tree + nCount;
        nCount++;
        pRoot->pRight = Tree + nCount;
        BuildTree(pRoot->pLeft, L, (L + R) / 2);
        BuildTree(pRoot->pRight, (L + R) / 2 + 1, R);
    }
}

void Insert(CNode * pRoot, int i, int v) {
    //将第i个数,其值为v,插入线段树 {
    if (pRoot->L == i && pRoot->R == i) {
        pRoot->nMin = pRoot->nMax = v;
        return;
    }
    pRoot->nMin = min(pRoot->nMin, v);
    pRoot->nMax = max(pRoot->nMax, v);
    if (i <= (pRoot->L + pRoot->R) / 2)
        Insert(pRoot->pLeft, i, v);
    else
        Insert(pRoot->pRight, i, v);
}

void Query(CNode * pRoot, int s, int e) {
    //查询区间[s,e]中的最小值和最大值,如果更优就记在全局变量里 {
    if (pRoot->nMin >= nMin && pRoot->nMax <= nMax) return;
    if (s == pRoot->L && e == pRoot->R) {
        nMin = min(pRoot->nMin, nMin);
        nMax = max(pRoot->nMax, nMax);
        return;
    }
    if (e <= (pRoot->L + pRoot->R) / 2)
        Query(pRoot->pLeft, s, e);
    else if (s >= (pRoot->L + pRoot->R) / 2 + 1)
        Query(pRoot->pRight, s, e);
    else {
        Query(pRoot->pLeft, s, (pRoot->L + pRoot->R) / 2);
        Query(pRoot->pRight, (pRoot->L + pRoot->R) / 2 + 1, e);
    }
}

int main() {
    int n, q, h;
    int i, j, k;
    scanf("%d%d", &n, &q);
    nCount = 0;
    BuildTree(Tree, 1, n);
    for (i = 1; i <= n; i++) {
        scanf("%d", &h);
        Insert(Tree, i, h);
    }
    for (i = 0; i < q; i++) {
        int s, e;
        scanf("%d%d", &s, &e);
        nMax = MY_MAX;
        nMin = MY_MIN;
        Query(Tree, s, e);
        printf("%d\n", nMax - nMin);
    }
    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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