hdu--4027(线段树更新操作的变形)

本文详细阐述了一种利用线段树解决包含复杂更新操作和查询任务的问题,特别关注如何通过更新操作调整连续部分的值,并通过查询获取特定区间内元素的累积效果。重点在于实现细节和技术策略,包括对更新操作的优化和对查询结果的高效计算,最终展示了解决此类问题的有效方法。

Can you answer these queries?

Time Limit: 4000/2000 MS (Java/Others)    Memory Limit: 65768/65768 K (Java/Others)
Total Submission(s): 11488    Accepted Submission(s): 2693


Problem Description
A lot of battleships of evil are arranged in a line before the battle. Our commander decides to use our secret weapon to eliminate the battleships. Each of the battleships can be marked a value of endurance. For every attack of our secret weapon, it could decrease the endurance of a consecutive part of battleships by make their endurance to the square root of it original value of endurance. During the series of attack of our secret weapon, the commander wants to evaluate the effect of the weapon, so he asks you for help.
You are asked to answer the queries that the sum of the endurance of a consecutive part of the battleship line.

Notice that the square root operation should be rounded down to integer.
 

Input
The input contains several test cases, terminated by EOF.
  For each test case, the first line contains a single integer N, denoting there are N battleships of evil in a line. (1 <= N <= 100000)
  The second line contains N integers Ei, indicating the endurance value of each battleship from the beginning of the line to the end. You can assume that the sum of all endurance value is less than 2 63.
  The next line contains an integer M, denoting the number of actions and queries. (1 <= M <= 100000)
  For the following M lines, each line contains three integers T, X and Y. The T=0 denoting the action of the secret weapon, which will decrease the endurance value of the battleships between the X-th and Y-th battleship, inclusive. The T=1 denoting the query of the commander which ask for the sum of the endurance value of the battleship between X-th and Y-th, inclusive.
 

Output
For each test case, print the case number at the first line. Then print one line for each query. And remember follow a blank line after each test case.
 

Sample Input
  
10 1 2 3 4 5 6 7 8 9 10 5 0 1 10 1 1 10 1 1 5 0 5 8 1 4 8
 

Sample Output
  
Case #1: 19 7 6
 

Source
 
解题思路:模板的线段树更新操作中,都是某一整块区间都加上某个数,和减去某个数,而这题不能够整体更改,需要一个点一个点改,但这样容易超时,哪么接着我们可以发现,数最大为2^63,对于一个数经过6次更新便成为1,再更新大小就不会变了,始终为1,所以当一个区间其sum值等于区间长度时,就不用往下面更新了。
代码如下:
#include<cstdio>
#include<cstring>
#include<iostream>
#include<algorithm>
#include<cmath>
using namespace std;
const int maxn=100000+1000;
int n,m;
__int64 a[maxn],sumv[maxn*6];
int op,ql,qr;
__int64 tmp;

void build(int o,int L,int R)
{
    int M=(L+R)/2;
    if(L==R)
    {
        sumv[o]=a[L];
    }
    else
    {
        build(o*2,L,M);
        build(o*2+1,M+1,R);
        sumv[o]=sumv[o*2]+sumv[o*2+1];
    }
}

void update(int o,int L,int R)
{


    int M=(L+R)/2;
    if(sumv[o]==R-L+1) return;//不需要更新了
    if(L==R)
    {
        sumv[o]=(__int64)sqrt(sumv[o]);
    }
    else
    {
        if(ql<=M)   update(o*2,L,M);
        if(qr>M)     update(o*2+1,M+1,R);
        sumv[o]=sumv[o*2]+sumv[o*2+1];
    }
}

__int64 query(int o,int L,int R)
{
    int M=(L+R)/2;
    if(ql<=L&&R<=qr)     return sumv[o];
    else
    {
        __int64 tmp=0;
        if(ql<=M)    tmp+=query(o*2,L,M);
        if(qr>M)      tmp+=query(o*2+1,M+1,R);
        return tmp;
    }
}

int main()
{
   int kase=0;
    int n,m;
    while(scanf("%d",&n)!=EOF)
    {
        for(int i=1; i<=n; i++)
            scanf("%I64d",&a[i]);
        build(1,1,n);
        scanf("%d",&m);
        printf("Case #%d:\n",++kase);
        for(int i=1;i<=m;i++)
        {
            scanf("%d%d%d",&op,&ql,&qr);
        if(ql>qr)  swap(ql,qr);
            if(op==0)
            {
                update(1,1,n);
            }
            else if(op=1)
            {
        printf("%I64d\n",query(1,1,n));
            }
        }
         printf("\n");
    }
    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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