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

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
 
Source

题意:

把给定的长方体(不限)叠加在一起,叠加的条件是。上面一个长方体的长和宽都比以下长方体的长

和宽短;求这些长方体能叠加的最高的高度.(当中(3,2。1)能够摆放成(3,1,2)、(2,1,3)等).

PS:
每块积木最多有
3 个不同的底面和高度。我们能够把每块积木看成三个不同的积木,
那么n种类型的积木就转化为3
*
n个不同的积木,对这3
* n个积木的长依照从大到小排序;
然后找到一个递减的子序列,使得子序列的高度和最大。

代码例如以下:

#include <cstdio>
#include <cstring>
#include <algorithm>
#include <iostream>
using namespace std;
struct node
{
int l, w, h;
} a[1047];
bool cmp(node a, node b)
{
if(a.l == b.l)
{
return a.w > b.w;
}
return a.l > b.l;
}
int MAX(int a, int b)
{
if(a > b)
return a;
return b;
}
int dp[1047];//dp[i]:以第i块积木为顶的最大高度
int main()
{
int n;
int cas = 0;
while(scanf("%d",&n) && n)
{
//int L, W, H;
int tt[3];
int k = 0;
for(int i = 0; i < n; i++)
{
scanf("%d%d%d",&tt[0],&tt[1],&tt[2]);
sort(tt,tt+3);
a[k].l = tt[0];
a[k].w = tt[1];
a[k].h = tt[2];
k++;
a[k].l = tt[1];
a[k].w = tt[2];
a[k].h = tt[0];
k++;
a[k].l = tt[0];
a[k].w = tt[2];
a[k].h = tt[1];
k++;
}
sort(a,a+k,cmp);
int maxx = 0;
for(int i = 0; i < k; i++)
{
dp[i] = a[i].h;
for(int j = i-1; j >= 0; j--)
{
if(a[j].l>a[i].l && a[j].w>a[i].w)
{
dp[i] = MAX(dp[i], dp[j]+a[i].h);
}
}
if(dp[i] > maxx)
{
maxx = dp[i];
}
}
printf("Case %d: maximum height = %d\n",++cas,maxx);
}
return 0;
}

HDU 1069 Monkey and Banana(最大的单调递减序列啊 dp)的更多相关文章

  1. HDU 1069 Monkey and Banana / ZOJ 1093 Monkey and Banana (最长路径)

    HDU 1069 Monkey and Banana / ZOJ 1093 Monkey and Banana (最长路径) Description A group of researchers ar ...

  2. HDU 1069 Monkey and Banana dp 题解

    HDU 1069 Monkey and Banana 纵有疾风起 题目大意 一堆科学家研究猩猩的智商,给他M种长方体,每种N个.然后,将一个香蕉挂在屋顶,让猩猩通过 叠长方体来够到香蕉. 现在给你M种 ...

  3. HDU 1069 Monkey and Banana(转换成LIS,做法很值得学习)

    题目链接:http://acm.hdu.edu.cn/showproblem.php?pid=1069 Monkey and Banana Time Limit: 2000/1000 MS (Java ...

  4. HDU 1069 Monkey and Banana(二维偏序LIS的应用)

    ---恢复内容开始--- Monkey and Banana Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K ...

  5. HDU 1069 Monkey and Banana (DP)

    Monkey and Banana Time Limit:1000MS     Memory Limit:32768KB     64bit IO Format:%I64d & %I64u S ...

  6. HDU 1069—— Monkey and Banana——————【dp】

    Monkey and Banana Time Limit:1000MS     Memory Limit:32768KB     64bit IO Format:%I64d & %I64u S ...

  7. hdu 1069 Monkey and Banana

    Monkey and Banana Time Limit: 2000/1000 MS (Java/Others)    Memory Limit: 65536/32768 K (Java/Others ...

  8. HDU 1069 Monkey and Banana(动态规划)

    Monkey and Banana Problem Description A group of researchers are designing an experiment to test the ...

  9. HDU 1069 Monkey and Banana(DP 长方体堆放问题)

    Monkey and Banana Problem Description A group of researchers are designing an experiment to test the ...

  10. HDU 1069 Monkey and Banana 基础DP

    题目链接:Monkey and Banana 大意:给出n种箱子的长宽高.每种不限个数.可以堆叠.询问可以达到的最高高度是多少. 要求两个箱子堆叠的时候叠加的面.上面的面的两维长度都严格小于下面的. ...

随机推荐

  1. Linux Command : top

    top命令是Linux下常用的性能分析工具,能够实时显示系统中各个进程所占用的系统资源,类似于Windows的任务管理器.下面详细介绍它的使用方法.top是一个动态显示过程,即可以通过用户按键来不断刷 ...

  2. jboss中控制台jmx-console 登录的用户名和密码设置

    默认情况访问 http://localhost:8080/jmx-console 就可以浏览jboss的部署管理的一些信息,不需要输入用户名和密码,使用起来有点安全隐患.下面我们针对此问题对jboss ...

  3. solr高亮设置以及摘要

    高亮显示 public SolrDocumentList query(String str) { SolrQuery query = new SolrQuery(str); //设置高亮,以下两种方式 ...

  4. 读书笔记,《Java 8实战》,第三章,Lambda表达式

    第一节,Lambda管中窥豹    可以把Lambda表达式理解为简洁地表示可传递的匿名函数的一种方式,它没有名称,但它有参数列表.函数主题和返回值.    本节介绍了Lambda表达式的语法,它包括 ...

  5. Valid Palindrome leetcode java

    题目: Given a string, determine if it is a palindrome, considering only alphanumeric characters and ig ...

  6. CentOS7安装openjdk、tomcat和mysql流程介绍

    首先是前戏,推荐一个远程工具Xshell和Xftp搭配使用,以下是Xshell的官网 http://www.netsarang.com/products/xsh_overview.html 1.ope ...

  7. jQuery 重复加载,导致依赖于 jQuery的JS全部失效问题

    父页面引入子页面,子页面引入jQuery.js文件,父页面JS依赖jQuery.js   ,出现问题是,总提示JS对象无效.猜测jQuery加载顺序不是最早造成的. 父页面: 子页面: 从这里看 ,j ...

  8. Kalman滤波器从原理到实现

    Kalman滤波器的历史渊源 We are like dwarfs on the shoulders of giants, by whose grace we see farther than the ...

  9. 几种梯度下降方法对比(Batch gradient descent、Mini-batch gradient descent 和 stochastic gradient descent)

    https://blog.csdn.net/u012328159/article/details/80252012 我们在训练神经网络模型时,最常用的就是梯度下降,这篇博客主要介绍下几种梯度下降的变种 ...

  10. 用Hadoop构建电影推荐系统

    转自:http://blog.fens.me/hadoop-mapreduce-recommend/ Hadoop家族系列文章,主要介绍Hadoop家族产品,常用的项目包括Hadoop, Hive, ...