HDU - 1032 ( 3n+1 n/2)

本文深入分析了HDU-1032算法题,探讨了一个递归算法的性质,该算法用于计算特定序列的周期长度。通过实例讲解了如何确定任意两个整数之间的最大周期长度,提供了完整的C语言实现代码。

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L - 10p

 HDU - 1032

确实是水。   但是题目没有注明i 与 j 谁大谁小, 要先判断交换

 Problems in Computer Science are often classified as belonging to a certain class of problems (e.g., NP, Unsolvable, Recursive). In this problem you will be analyzing a property of an algorithm whose classification is not known for all possible inputs. 

Consider the following algorithm: 


    1.      input n 

    2.      print n 

    3.      if n = 1 then STOP 

    4.           if n is odd then n <- 3n + 1 

    5.           else n <- n / 2 

    6.      GOTO 2 



Given the input 22, the following sequence of numbers will be printed 22 11 34 17 52 26 13 40 20 10 5 16 8 4 2 1 

It is conjectured that the algorithm above will terminate (when a 1 is printed) for any integral input value. Despite the simplicity of the algorithm, it is unknown whether this conjecture is true. It has been verified, however, for all integers n such that 0 < n < 1,000,000 (and, in fact, for many more numbers than this.) 

Given an input n, it is possible to determine the number of numbers printed (including the 1). For a given n this is called the cycle-length of n. In the example above, the cycle length of 22 is 16. 

For any two numbers i and j you are to determine the maximum cycle length over all numbers between i and j. 

Input

The input will consist of a series of pairs of integers i and j, one pair of integers per line. All integers will be less than 1,000,000 and greater than 0. 

You should process all pairs of integers and for each pair determine the maximum cycle length over all integers between and including i and j. 

You can assume that no opperation overflows a 32-bit integer. 

Output

For each pair of input integers i and j you should output i, j, and the maximum cycle length for integers between and including i and j. These three numbers should be separated by at least one space with all three numbers on one line and with one line of output for each line of input. The integers i and j must appear in the output in the same order in which they appeared in the input and should be followed by the maximum cycle length (on the same line). 

Sample Input

1 10
100 200
201 210
900 1000

Sample Output

1 10 20
100 200 125
201 210 89
900 1000 174
#include<stdio.h>
int fun(int n)
 {
    int count = 0;
    while(n!=1)
    {
        if (n % 2 == 0)
            n = n / 2;
        else
            n = 3 * n + 1;
        count++;
    }
   count++;
return count;
}
int main()
{
    int a, b;
    while (~scanf("%d %d", &a, &b))
    {
        int k,count,temp=0, i;
        int x = a, y = b;
        if(y < x)
        {
            k = x;
            x = y;
            y = k;
        }
        for (i = x; i <= y; i++)
        {
            count = fun(i);
            if (count > temp)
                temp = count;
        }
        printf("%d %d %d\n", a, b, temp);
    }
    return 0;
}

 

HDU-3480 是一个典型的动态规划问题,其题目标题通常为 *Division*,主要涉及二维费用背包问题或优化后的动态规划策略。题目大意是:给定一个整数数组,将其划分为若干个连续的子集,每个子集最多包含 $ m $ 个元素,并且每个子集的最大值与最小值之差不能超过给定的阈值 $ t $,目标是使所有子集的划分代价总和最小。每个子集的代价是该子集最大值与最小值的差值。 ### 动态规划思路 设 $ dp[i] $ 表示前 $ i $ 个元素的最小代价。状态转移方程如下: $$ dp[i] = \min_{j=0}^{i-1} \left( dp[j] + cost(j+1, i) \right) $$ 其中 $ cost(j+1, i) $ 表示从第 $ j+1 $ 到第 $ i $ 个元素构成一个子集的代价,即 $ \max(a[j+1..i]) - \min(a[j+1..i]) $。 为了高效计算 $ cost(j+1, i) $,可以使用滑动窗口或单调队列等数据结构来维护区间最大值与最小值,从而将时间复杂度优化到可接受的范围。 ### 示例代码 以下是一个简化版本的动态规划实现,使用暴力方式计算区间代价,适用于理解问题结构: ```cpp #include <bits/stdc++.h> using namespace std; const int INF = 0x3f3f3f3f; const int MAXN = 10010; int a[MAXN]; int dp[MAXN]; int main() { int T, n, m; cin >> T; for (int Case = 1; Case <= T; ++Case) { cin >> n >> m; for (int i = 1; i <= n; ++i) cin >> a[i]; dp[0] = 0; for (int i = 1; i <= n; ++i) { dp[i] = INF; int mn = a[i], mx = a[i]; for (int j = i; j >= max(1, i - m + 1); --j) { mn = min(mn, a[j]); mx = max(mx, a[j]); if (mx - mn <= T) { dp[i] = min(dp[i], dp[j - 1] + mx - mn); } } } cout << "Case " << Case << ": " << dp[n] << endl; } return 0; } ``` ### 优化策略 - **单调队列**:可以使用两个单调队列分别维护当前窗口的最大值与最小值,从而将区间代价计算的时间复杂度从 $ O(n^2) $ 降低到 $ O(n) $。 - **斜率优化**:若问题满足特定的决策单调性,可以考虑使用斜率优化技巧进一步加速状态转移过程。 ### 时间复杂度分析 原始暴力解法的时间复杂度为 $ O(n^2) $,在 $ n \leq 10^4 $ 的情况下可能勉强通过。通过单调队列优化后,可以稳定运行于 $ O(n) $ 或 $ O(n \log n) $。 ### 应用场景 HDU-3480 的问题模型可以应用于资源调度、任务划分等场景,尤其适用于需要控制子集内部差异的问题,如图像分块压缩、数据分段处理等[^1]。 ---
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