HDU 1069 Monkey and Banana dp类型:最长上升子序列

探讨如何通过不同尺寸的积木堆叠帮助猴子够到悬挂的香蕉,解决这一问题涉及积木放置方式的选择及寻找最高稳定堆叠的方法。

摘要生成于 C知道 ,由 DeepSeek-R1 满血版支持, 前往体验 >

http://acm.hdu.edu.cn/showproblem.php?pid=1069

Monkey and Banana

Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others)
Total Submission(s): 14484    Accepted Submission(s): 7619


Problem Description
A group of researchers are designing an experiment to test the IQ of a monkey. They will hang a banana at the roof of a building, and at the mean time, provide the monkey with some blocks. If the monkey is clever enough, it shall be able to reach the banana by placing one block on the top another to build a tower and climb up to get its favorite food.

The researchers have n types of blocks, and an unlimited supply of blocks of each type. Each type-i block was a rectangular solid with linear dimensions (xi, yi, zi). A block could be reoriented so that any two of its three dimensions determined the dimensions of the base and the other dimension was the height.

They want to make sure that the tallest tower possible by stacking blocks can reach the roof. The problem is that, in building a tower, one block could only be placed on top of another block as long as the two base dimensions of the upper block were both strictly smaller than the corresponding base dimensions of the lower block because there has to be some space for the monkey to step on. This meant, for example, that blocks oriented to have equal-sized bases couldn't be stacked.

Your job is to write a program that determines the height of the tallest tower the monkey can build with a given set of blocks.
 

Input
The input file will contain one or more test cases. The first line of each test case contains an integer n,
representing the number of different blocks in the following data set. The maximum value for n is 30.
Each of the next n lines contains three integers representing the values xi, yi and zi.
Input is terminated by a value of zero (0) for n.
 

Output
For each test case, print one line containing the case number (they are numbered sequentially starting from 1) and the height of the tallest possible tower in the format "Case case: maximum height = height".
 

Sample Input
1 10 20 30 2 6 8 10 5 5 5 7 1 1 1 2 2 2 3 3 3 4 4 4 5 5 5 6 6 6 7 7 7 5 31 41 59 26 53 58 97 93 23 84 62 64 33 83 27 0
 

Sample Output
Case 1: maximum height = 40 Case 2: maximum height = 21 Case 3: maximum height = 28 Case 4: maximum height = 342
题意:给你一些规格的箱子(无限个),现在让你把箱子堆起来,求最高能有多高。上面的箱子长宽都要严格小于下面的箱子。

思路:一个箱子有6种放法,排序后就模拟最长递增子序列的dp方法就行。dp入门题。

#include<iostream>
#include<cstdio>
#include<cstdlib>
#include<cstring>
#include<algorithm>
#include<cmath>
#include<queue>
#include<vector>
#include<map>
#include<string>
#define LL long long
#define eps 1e-8
using namespace std;
const int mod = 1e7+7;
const int INF = 1e8;
const int inf = 0x3f3f3f3f;
const int maxx = 10100;
const int N = 1000;
int n,cnt;
struct node
{
    int x,y,z;
} a[N];
int cmp(node s1,node s2)
{
    if(s1.x==s2.x)
        return s1.y>s2.y;
    return s1.x>s2.x;
}
int dp[N];
int ddp()
{
    sort(a,a+cnt,cmp);
    memset(dp,0,sizeof(dp));
    int num=0;
    for(int i=0; i<cnt; i++)
    {
        dp[i]=a[i].z;
        for(int j=0; j<i; j++)
        {
            if(a[i].x<a[j].x&&a[i].y<a[j].y&&a[i].z+dp[j]>dp[i])
                dp[i]=a[i].z+dp[j];
        }
        if(dp[i]>num)
            num=dp[i];
    }
    return num;
}
void add(int x,int y,int z)
{
    a[cnt].x=x,a[cnt].y=y,a[cnt++].z=z;
}
int main()
{
    int cas=1;
    while(~scanf("%d",&n))
    {
        if(n==0)
            break;
        int b,c,d;
        cnt=0;
        for(int i=0; i<n; i++)
        {
            scanf("%d%d%d",&b,&c,&d);
            add(b,c,d);
            add(c,b,d);
            add(c,d,b);
            add(d,c,b);
            add(d,b,c);
            add(b,d,c);
        }
        printf("Case %d: maximum height = %d\n",cas++,ddp());
    }
    return 0;
}










内容概要:本文档详细介绍了基于MATLAB实现多目标差分进化(MODE)算法进行无人机三维路径规划的项目实例。项目旨在提升无人机在复杂三维环境中路径规划的精度、实时性、多目标协调处理能力、障碍物避让能力和路径平滑性。通过引入多目标差分进化算法,项目解决了传统路径规划算法在动态环境和多目标优化中的不足,实现了路径长度、飞行安全距离、能耗等多个目标的协调优化。文档涵盖了环境建模、路径编码、多目标优化策略、障碍物检测与避让、路径平滑处理等关键技术模块,并提供了部分MATLAB代码示例。 适合人群:具备一定编程基础,对无人机路径规划和多目标优化算法感兴趣的科研人员、工程师和研究生。 使用场景及目标:①适用于无人机在军事侦察、环境监测、灾害救援、物流运输、城市管理等领域的三维路径规划;②通过多目标差分进化算法,优化路径长度、飞行安全距离、能耗等多目标,提升无人机任务执行效率和安全性;③解决动态环境变化、实时路径调整和复杂障碍物避让等问题。 其他说明:项目采用模块化设计,便于集成不同的优化目标和动态环境因素,支持后续算法升级与功能扩展。通过系统实现和仿真实验验证,项目不仅提升了理论研究的实用价值,还为无人机智能自主飞行提供了技术基础。文档提供了详细的代码示例,有助于读者深入理解和实践该项目。
评论
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值