poj 3264 -- Balanced Lineup (区间最值,线段树/RMQ)

本文介绍了如何使用预处理最小最大值表和线段树两种方法解决区间最值查询(RMQ)问题。通过具体代码实现展示了每种方法的构建过程及查询操作,为读者提供了理解RMQ问题解决方案的途径。

第一次RMQ ,还好


RMQ版:

# include <cstdio>
# include <iostream>
# include <set>
# include <map>
# include <vector>
# include <list>
# include <queue>
# include <stack>
# include <cstring>
# include <string>
# include <cstdlib>
# include <cmath>
# include <algorithm>

using namespace std ;

const int maxn = 51000 ;

int dp1 [ 20 ] [ maxn ] ;
int dp2 [ 20 ] [ maxn ] ;                //小的放前面效率高不少
int a [ 51000 ] ;
int n ;

void getdp ( )
{
    for ( int i = 1 ; i <= n ; i ++ )
    {
        dp1 [ 0 ] [ i ] = dp2 [ 0 ] [ i ] = a [ i ] ;
    }
    int len = floor ( log2 ( 51000 ) ) ;       //C++没有log2,取自然对数算
    for ( int j = 1 ; j <= len ; j ++ )
        for ( int i = 1 ; i + ( 1 << j ) - 1 <= n ; i ++ )
        {
            dp1 [ j ] [ i ] = min ( dp1 [ j - 1 ] [ i ] , dp1 [ j - 1 ] [ i + (1 << (j - 1)) ] ) ;
            dp2 [ j ] [ i ] = max ( dp2 [ j - 1 ] [ i ] , dp2 [ j - 1 ] [ i + (1 << (j - 1)) ] ) ;
        }
}

int main ( )
{
    int q ;
    scanf ( "%d%d" , & n , & q ) ;
    for ( int i = 1 ; i <= n ; i ++ )
    {
        scanf ( "%d" , a + i ) ;
    }
    getdp ( ) ;
    while ( q -- )
    {
        int m , n ;
        scanf ( "%d%d" , & m , & n ) ;
        int len = floor ( log2 ( n - m + 1 ) ) ;
        int maxx = max ( dp2 [ len ] [ m ] , dp2 [ len ] [ n - ( 1 << len ) + 1 ] ) ;
        int minn = min ( dp1 [ len ] [ m ] , dp1 [ len ] [ n - ( 1 << len ) + 1 ] ) ;
        printf ( "%d\n" , maxx - minn ) ;
    }
}



线段树版:


# include <cstdio>
# include <iostream>
# include <set>
# include <map>
# include <vector>
# include <list>
# include <queue>
# include <stack>
# include <cstring>
# include <string>
# include <cstdlib>
# include <cmath>
# include <algorithm>

using namespace std ;

const int maxn = 51000 ;
const int MAX = 2100000000 ;

int low , high ;

struct Tree
{
    int maxx , minn ;
} tree [ maxn * 4 ] ;

void pushup ( int pos )
{
    tree [ pos ] . minn = min ( tree [ pos * 2 ] . minn , tree [ pos * 2 + 1 ] . minn ) ;
    tree [ pos ] . maxx = max ( tree [ pos * 2 ] . maxx , tree [ pos * 2 + 1 ] . maxx ) ;
}

void build ( int l , int r , int pos )
{
    if ( l == r )
    {
        scanf ( "%d" , & tree [ pos ] . minn ) ;
        tree [ pos ] . maxx = tree [ pos ] . minn ;
        return ;
    }
    int m = ( l + r ) / 2 ;
    build ( l , m , pos * 2 ) ;
    build ( m + 1 , r , pos * 2 + 1 ) ;
    pushup ( pos ) ;
}

void quer ( int l , int r , int pos , int L , int R )
{
    if ( l >= L && r <= R )
    {
        high = max ( high , tree [ pos ] . maxx ) ;
        low = min ( low , tree [ pos ] . minn ) ;
        return ;
    }
    int m = ( l + r ) / 2 ;
    if ( m >= L )
        quer ( l , m , pos * 2 , L , R ) ;
    if ( m + 1 <= R )
        quer ( m + 1 , r , pos * 2 + 1 , L , R ) ;
}

int main ( )
{
    int n , q ;
    scanf ( "%d%d" , & n , & q ) ;
    build ( 1 , n , 1 ) ;
    while ( q -- )
    {
        int L , R ;
        scanf ( "%d%d" , & L , & R ) ;
        high = 0 , low = MAX ;
        quer ( 1 , n , 1 , L , R ) ;
        printf ( "%d\n" , high - low ) ;
    }
}


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。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。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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