Post Office Problem

On one line there are n houses. Give you an array of integer means the the position of each house. Now you need to pick k position to build k post office, so that the sum distance of each house to the nearest post office is the smallest. Return the least possible sum of all distances between each village and its nearest post office.

样例

Given array a = [1,2,3,4,5], k = 2.return 3.

挑战

Could you solve this problem in O(n^2) time ?

class Solution {
public:
    /**
     * @param A an integer array
     * @param k an integer
     * @return an integer
     */
    int postOffice(vector<int>& A, int k) {
        // Write your code here
        int n = A.size();
        sort(A.begin(), A.end());
        int cost[n][n];
        memset(cost, 0, n*n*sizeof(int));

        for (int i = 0; i < n; i++)
        {
            for (int j = i+1; j < n; j++)
            {
                int mid = i + (j-i)/2;
                cost[i][j] = 0;
                for (int k = i; k <= j; k++)
                {
                    cost[i][j] += abs(A[k] - A[mid]);
                }
            }
        }

        int buf[n][k+1];
        memset(buf, 0, n*(k+1)*sizeof(int));
        for (int i = 0; i < n; i++)
        {
            for (int j = 1; j <= k; j++)
            {
                if (j == 1)
                {
                    buf[i][j] = cost[0][i];
                }
                else if (j > i)
                {
                    buf[i][j] = 0;
                }
                else
                {
                    buf[i][j] = INT_MAX;
                    for (int k = 0; k < i; k++)
                    {
                        buf[i][j] = min(buf[i][j], buf[k][j-1] + cost[k+1][i]);
                    }
                }
            }
        }

        return buf[n-1][k];
    }
};


邮局选址问题是经典的优化问题,通常涉及到在一个给定区域选择最合适的地点设立邮局,以便最小化服务覆盖成本或满足客户服务需求。在Java中解决这个问题,可以采用线性规划、贪心算法或者基于模拟退火等搜索算法。 这里给出一个简单的线性规划求解思路的伪代码示例: ```java import com.github.mustapha74987.LinearProgramming; class PostOffice { // 地点坐标和成本 private int[] locations; private double[] costs; public PostOffice(int[][] coordinates, double[] costs) { this.locations = coordinates; this.costs = costs; } // 构建并求解线性规划模型 public void optimize() { LinearProgramming lp = new LinearProgramming(); // 目标函数:最小化总成本 lp.setObjectiveFunction(new ObjectiveFunction(this.costs)); // 约束条件:每个地点至多有一个邮局 for (int i = 0; i < locations.length; i++) { lp.addConstraint(new Constraint("At most one post office at location " + i, new BinaryVariable[]{lp.createBinaryVariable("PostOffice_" + i)}, new double[]{1})); } // 求解 lp.solve(); // 获取最优解 int optimalLocation = lp.getOptimalSolutionIndex(0); System.out.println("Optimal post office location: " + optimalLocation); } } public class Main { public static void main(String[] args) { int[][] locations = {{0, 0}, {5, 5}, {10, 10}}; double[] costs = {100, 200, 300}; // 例如每个位置的成本 PostOffice problem = new PostOffice(locations, costs); problem.optimize(); } } ``` 请注意,这只是一个简化版的示例,实际应用可能需要处理更复杂的约束条件,如距离限制,交通网络等因素,并可能需要引入一些库来支持线性规划求解,比如JPLib、Apache Commons Math等。
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