Piggy-Bank (完全背包 动态规划)

本文介绍了一种通过已知存钱罐的总重量及其内含硬币类型来估算其最低金额的方法。这是一个典型完全背包问题的应用实例,文章提供了解决方案的详细步骤,并附带AC代码。
Before ACM can do anything, a budget must be prepared and the necessary financial support obtained. The main income for this action comes from Irreversibly Bound Money (IBM). The idea behind is simple. Whenever some ACM member has any small money, he takes all the coins and throws them into a piggy-bank. You know that this process is irreversible, the coins cannot be removed without breaking the pig. After a sufficiently long time, there should be enough cash in the piggy-bank to pay everything that needs to be paid.

But there is a big problem with piggy-banks. It is not possible to determine how much money is inside. So we might break the pig into pieces only to find out that there is not enough money. Clearly, we want to avoid this unpleasant situation. The only possibility is to weigh the piggy-bank and try to guess how many coins are inside. Assume that we are able to determine the weight of the pig exactly and that we know the weights of all coins of a given currency. Then there is some minimum amount of money in the piggy-bank that we can guarantee. Your task is to find out this worst case and determine the minimum amount of cash inside the piggy-bank. We need your help. No more prematurely broken pigs!
Input
The input consists of T test cases. The number of them (T) is given on the first line of the input file. Each test case begins with a line containing two integers E and F. They indicate the weight of an empty pig and of the pig filled with coins. Both weights are given in grams. No pig will weigh more than 10 kg, that means 1 <= E <= F <= 10000. On the second line of each test case, there is an integer number N (1 <= N <= 500) that gives the number of various coins used in the given currency. Following this are exactly N lines, each specifying one coin type. These lines contain two integers each, Pand W (1 <= P <= 50000, 1 <= W <=10000). P is the value of the coin in monetary units, W is it's weight in grams.
Output
Print exactly one line of output for each test case. The line must contain the sentence "The minimum amount of money in the piggy-bank is X." where X is the minimum amount of money that can be achieved using coins with the given total weight. If the weight cannot be reached exactly, print a line "This is impossible.".
Sample Input
3
10 110
2
1 1
30 50
10 110
2
1 1
50 30
1 6
2
10 3
20 4
Sample Output
The minimum amount of money in the piggy-bank is 60.
The minimum amount of money in the piggy-bank is 100.

This is impossible.


题意:不知道存钱罐里有多少钱,现在给你存的钱的总重量(可求得)及一些货币的面值和重量,让你计算出里面至少存了多少钱,如果给出的数据不能计算出,输出This is impossible.

思路:典型的完全背包问题,给出价值和重量,数量可任选。因为此题求的是最少的钱,所以用min,dp数组开始赋一个较大的值,然后模板套用。


AC代码:

#include <string.h>
#include <stdio.h>
#include <algorithm>
using namespace std;

int f[1000005];

int main()
{
    int t;
    int wa,wb,m;
    int n,val[505],wei[505],i,j;
    scanf("%d",&t);
    while(t--){
        scanf("%d%d",&wa,&wb);
        m = wb-wa;
        scanf("%d",&n);
        for(i = 0;i<n;i++)
        scanf("%d%d",&val[i],&wei[i]);
        for(i = 0;i<=m;i++)
            f[i] = 10000000;
        f[0] = 0;   //f[0]开始是0,容量为0的时候价值为0
        for(i = 0;i<n;i++)
            for(j = wei[i];j<=m;j++)       //从i物品的重量到总重量
                f[j] = min(f[j],f[j-wei[i]]+val[i]);
        if(f[m] == 10000000)
            printf("This is impossible.\n");
        else
            printf("The minimum amount of money in the piggy-bank is %d.\n",f[m]);
    }
    return 0;
}


### 问题描述 SWUST OJ平台上的Piggy-Bank问题通常涉及动态规划中的完全背包问题。题目大意是:给定一个存钱罐的空重量 $ e $ 和满重量 $ f $,以及若干种硬币的价值和重量,每种硬币的数量不限。要求用这些硬币恰好填满存钱罐(总重量为 $ f - e $),使得总价值最小。如果无法恰好填满,则输出“This is impossible.”。 ### 解法分析 此问题是一个典型的**完全背包问题**,其中每个物品可以被无限次使用。为了求解最小价值,可以采用动态规划的方法。 #### 动态规划思路 1. **状态定义**: - 定义数组 `ans[j]` 表示当总重量为 $ j $ 时,所需的最小价值。 - 初始化时,`ans[0] = 0`,其余位置初始化为一个较大的值(如 `inf`),表示无法达到该重量。 2. **状态转移**: - 对于每个硬币,重量为 `weight[i]`,价值为 `value[i]`。 - 遍历重量范围 $ j $ 从 `weight[i]` 到最大重量 $ f-e $,更新 `ans[j]` 的值: $$ ans[j] = \min(ans[j], ans[j - weight[i]] + value[i]) $$ 3. **最终结果**: - 如果 `ans[f-e]` 仍为 `inf`,说明无法恰好填满存钱罐;否则输出最小价值。 #### 示例代码 以下是该问题的完整解法代码实现: ```cpp #include <stdio.h> #include <string.h> #include <algorithm> using namespace std; #define inf 0x7fffff int value[508], weight[508], ans[10050]; int main() { int n; scanf("%d", &n); while (n--) { int e, f, t; scanf("%d%d%d", &e, &f, &t); for (int i = 0; i <= f; i++) { ans[i] = inf; } for (int i = 0; i < t; i++) { scanf("%d%d", &value[i], &weight[i]); } ans[0] = 0; for (int i = 0; i < t; i++) { for (int j = weight[i]; j <= f - e; j++) { ans[j] = min(ans[j], ans[j - weight[i]] + value[i]); } } if (ans[f - e] == inf) { printf("This is impossible.\n"); } else { printf("The minimum amount of money in the piggy-bank is %d.\n", ans[f - e]); } } } ``` #### 代码解析 - **初始化**:`ans` 数组初始化为一个极大值 `inf`,表示无法达到的状态。 - **输入处理**:循环读取多组测试数据,每组数据包括空重量 $ e $、满重量 $ f $ 和硬币种类数 $ t $。 - **动态规划处理**:通过两层循环遍历硬币和重量,更新动态规划数组。 - **结果判断**:根据 `ans[f-e]` 是否为 `inf` 判断是否可以填满存钱罐。 ### 算法复杂度 - **时间复杂度**:$ O(T \cdot W) $,其中 $ T $ 是硬币种类数,$ W $ 是目标重量 $ f-e $。 - **空间复杂度**:$ O(W) $,仅使用一维数组存储状态。 ### 相关问题 1. 如何将完全背包问题转换为动态规划解法? 2. 在动态规划中如何处理最小值问题? 3. 如何优化完全背包问题的空间复杂度? 4. 什么是完全背包问题与0-1背包问题的区别? 5. 如何处理无法达到目标状态的情况?
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