Smallest Difference

探讨了如何从一组不重复的十进制数字中选取并形成两个整数,使这两个整数之间的绝对差值达到最小,涉及算法设计与实现,具体通过C++代码演示了解题过程。

Smallest Difference

Given a number of distinct decimal digits, you can form one integer by choosing a non-empty subset of these digits and writing them in some order. The remaining digits can be written down in some order to form a second integer. Unless the resulting integer is 0, the integer may not start with the digit 0.

For example, if you are given the digits 0, 1, 2, 4, 6 and 7, you can write the pair of integers 10 and 2467. Of course, there are many ways to form such pairs of integers: 210 and 764, 204 and 176, etc. The absolute value of the difference between the integers in the last pair is 28, and it turns out that no other pair formed by the rules above can achieve a smaller difference.

Input

The first line of input contains the number of cases to follow. For each case, there is one line of input containing at least two but no more than 10 decimal digits. (The decimal digits are 0, 1, …, 9.) No digit appears more than once in one line of the input. The digits will appear in increasing order, separated by exactly one blank space.

Output

For each test case, write on a single line the smallest absolute difference of two integers that can be written from the given digits as described by the rules above.

Sample Input

1
0 1 2 4 6 7

Sample Output

28

C++编写:

#include<iostream>
#include<algorithm>
#include<vector>
#include<string>
using namespace std;

vector<int> num;

int solve()
{
    num.clear();
    string a;
    getline(cin,a);
    for(int i=0;i<a.size();i++)          //将每一行数据存储到动态数组num中
    {
        if(a[i]>='0' && a[i]<='9')
            num.push_back(a[i]-'0');
    }
    
    int ans=0x3fffffff;
    int m,n;
    do{
        m=0,n=0;
        for(int i=0;i<num.size()/2;i++)         //得到第一个理想位数的整数
        {
            m *= 10;
            m += num[i];
        }
        for(int j=num.size()/2;j<num.size();j++)         //得到第二个理想位数的整数
        {
            n *= 10;
            n += num[j];
        }
        if((num[0]==0 && m!=0) || (num[num.size()/2])==0 && n!=0)    continue;
        ans=min(ans,abs(n-m));
    }while(next_permutation(num.begin(),num.end()));      //将num中的数据全排列
    cout<<ans<<endl;
}
 
int main()
{
    ios::sync_with_stdio(false);
    int m;        //m代表案例数
    cin>>m;
    cin.ignore();
    while(m--)
        solve();
}
Load balancing (负载均衡) refers to efficiently distributing incoming network traffic across a group of backend servers. A load balancing algorithm distributes loads in a specific way. If we can estimate the maximum incoming traffic load, here is an algorithm that works according to the following rule: The incoming traffic load of size S will first be partitioned into two parts, and each part may be again partitioned into two parts, and so on. Only one partition is made at a time. At any time, the size of the smallest load must be strictly greater than half of the size of the largest load. All the sizes are positive integers. This partition process goes on until it is impossible to make any further partition. For example, if S=7, then we can break it into 3+4 first, then continue as 4=2+2. The process stops at requiring three servers, holding loads 3, 2, and 2. Your job is to decide the maximum number of backend servers required by this algorithm. Since such kind of partitions may not be unique, find the best solution -- that is, the difference between the largest and the smallest sizes is minimized. Input Specification: Each input file contains one test case, which gives a positive integer S (2≤N≤200), the size of the incoming traffic load. Output Specification: For each case, print two numbers in a line, namely, M, the maximum number of backend servers required, and D, the minimum of the difference between the largest and the smallest sizes in a partition with M servers. The numbers in a line must be separated by one space, and there must be no extra space at the beginning or the end of the line. Sample Input: 22 Sample Output: 4 1 Hint: There are more than one way to partition the load. For example: 22 = 8 + 14 = 8 + 7 + 7 = 4 + 4 + 7 + 7 or 22 = 10 + 12 = 10 + 6 + 6 = 4 + 6 + 6 + 6 or 22 = 10 + 12 = 10 + 6 + 6 = 5 + 5 + 6 + 6 All requires 4 servers. The last partition has the smallest difference 6−5=1, hence 1 is printed out.请给这一到题设置测试用例,包含最大最小情况,和一些特殊的情况,以及对于回溯算法最好和最坏的情况
最新发布
06-03
<think>嗯,用户需要为负载均衡算法设计测试用例,覆盖边界情况、特殊分区以及回溯算法的性能评估。首先,我得理清楚负载均衡算法的常见输入范围和关键参数。根据用户提到的,输入值的最小是2,最大是200,所以边界值测试需要包括这些点,比如1、2、3、199、200、201,这样能检查系统在边界附近的行为。接下来是特殊分区情况,比如无法继续分割或者均匀分割的情况。比如当任务数量是质数时,可能无法均匀分配到服务器,这时候需要测试算法如何处理。另外,任务类型混合的情况,比如CPU密集型和I/O密集型任务并存,算法是否能合理分配,避免资源争用。然后是回溯算法的性能评估。最佳情况可能是指任务能快速找到最优分配,比如均匀分割的情况,而最坏情况可能需要处理大量无效回溯,比如任务数量远大于服务器容量,导致算法需要不断回溯。这时候需要设计测试用例来模拟这些场景,记录响应时间和资源消耗。还要考虑引用中的内容,比如输入域测试法的极端测试、中间范围测试和特殊值测试。比如极端测试中的最小和最大任务数,中间的正常值比如100,以及特殊值如质数任务数。同时,引用提到边界值测试需要覆盖最小值、略大于、正常、略小于、最大值,这五个点,所以测试用例需要包括这些。另外,用户可能希望测试用例不仅覆盖功能正确性,还要评估性能,尤其是在高负载或复杂情况下的表现。比如,当任务数达到最大值200时,算法是否能及时响应,或者当出现无法分割的情况时,系统是否有降级处理策略。最后,生成相关问题的时候,需要确保涵盖用户提到的各个方面,比如测试用例设计方法、性能优化策略、不同算法的比较,以及异常处理机制。这些问题能帮助用户进一步深入理解负载均衡测试的各个方面。</think>### 负载均衡算法测试用例设计方法 #### 一、边界值测试用例(输入范围:$2 \leq n \leq 200$) 1. **极小值验证** - 输入任务数$n=2$,验证是否能分配到2台服务器 - 输入任务数$n=1$(非法值),检查异常处理机制[^4] 2. **极大值验证** - 输入任务数$n=200$,观察算法响应时间和内存占用 - 输入任务数$n=201$(溢出值),测试错误提示功能 3. **临界值验证** - 输入$n=3$(最小有效分割基数) - 输入$n=199$(接近最大值的质数) ```python # 边界值测试示例代码 def test_boundary_values(): test_cases = [1, 2, 3, 199, 200, 201] for n in test_cases: try: result = load_balancer(n) assert validate_allocation(result) except ValueError: log_invalid_input(n) ``` #### 二、特殊分区测试用例 1. **不可分割场景** - 输入质数任务数$n=199$,验证动态调整机制 - 输入任务类型冲突组合(如CPU密集型和I/O密集型混合) 2. **非均匀分配场景** - 创建$n=150$任务,其中30%为长时任务 - 模拟服务器异构环境(不同节点的计算能力差异) 3. **资源限制场景** $$ \max_{1 \leq i \leq k} \sum_{j=1}^{m} w_{ij} \leq C_i $$ 其中$C_i$表示第$i$台服务器的容量约束,测试超载保护机制[^3] #### 三、回溯算法性能测试 | 场景类型 | 任务特征 | 预期表现指标 | |---------|---------|-------------| | **最佳情况** | 可均匀分割的任务集 | 时间复杂度$O(n)$ | | **最坏情况** | 高度冲突的任务组合 | 时间复杂度$O(2^n)$ | | **典型情况** | 混合类型任务 | 平均响应时间<500ms | ```python # 回溯性能测试框架 def benchmark_backtracking(): for scenario in [best_case, worst_case, normal_case]: start = time() allocation = backtracking_allocator(scenario) duration = time() - start record_metrics(scenario, duration) ``` ### 验证指标设计 1. **分配均衡度** $$ \text{均衡系数} = \frac{\max(负载)}{\min(负载)} \leq 1.2 $$ 2. **回溯效率** - 成功分配所需迭代次数 - 无效路径剪枝率 3. **异常恢复能力** - 服务器宕机后的重新分配时间 - 任务突增时的弹性扩展速度[^2]
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