Leetcode475.Heaters供暖器

本文介绍了一种寻找供暖器最小加热半径的算法,确保所有房屋都能被供暖。通过排序和遍历房屋与供暖器的位置,计算每个房屋所需的最小加热半径。

冬季已经来临。 你的任务是设计一个有固定加热半径的供暖器向所有房屋供暖。

现在,给出位于一条水平线上的房屋和供暖器的位置,找到可以覆盖所有房屋的最小加热半径。

所以,你的输入将会是房屋和供暖器的位置。你将输出供暖器的最小加热半径。

说明:

  1. 给出的房屋和供暖器的数目是非负数且不会超过 25000。
  2. 给出的房屋和供暖器的位置均是非负数且不会超过10^9。
  3. 只要房屋位于供暖器的半径内(包括在边缘上),它就可以得到供暖。
  4. 所有供暖器都遵循你的半径标准,加热的半径也一样。

示例 1:

输入: [1,2,3],[2] 输出: 1 解释: 仅在位置2上有一个供暖器。如果我们将加热半径设为1,那么所有房屋就都能得到供暖。

示例 2:

输入: [1,2,3,4],[1,4] 输出: 1 解释: 在位置1, 4上有两个供暖器。我们需要将加热半径设为1,这样所有房屋就都能得到供暖。

 

 

class Solution {
public:
    int findRadius(vector<int>& houses, vector<int>& heaters) {
        sort(houses.begin(), houses.end());
        sort(heaters.begin(), heaters.end());
        int len1 = houses.size();
        int len2 = heaters.size();
        int res = 0;
        int cnt = 0;//设定一个标记,标记为位置比目前第i个房屋小的供暖器,优化,不然会超时
        for(int i = 0; i < len1; i++)
        {
            int temp = abs(houses[i] - heaters[cnt]);
            for(int j = cnt + 1; j < len2; j++)
            {
                if(houses[i] == heaters[j])
                {
                    temp = 0;
                    break;
                }
                else if(houses[i] < heaters[j])
                {
                    temp = min(temp, heaters[j] - houses[i]);
                }
                else
                {
                    temp = min(temp, houses[i] - heaters[j]);
                    cnt = j;
                }
            }
            res = max(temp, res);
        }
        return res;
    }
};

 

转载于:https://www.cnblogs.com/lMonster81/p/10434075.html

### LeetCode 475 Heaters Problem Solution and Explanation In this problem, one needs to find the minimum radius of heaters so that all houses can be warmed. Given positions of `houses` and `heaters`, both represented as integer arrays, the task is to determine the smallest maximum distance from any house to its nearest heater[^1]. To solve this issue efficiently: #### Binary Search Approach A binary search on answer approach works well here because increasing the radius monotonically increases the number of covered houses. Start by sorting the list of heaters' locations which allows using binary search for finding closest heater distances quickly. ```python def findRadius(houses, heaters): import bisect houses.sort() heaters.sort() max_distance = 0 for house in houses: pos = bisect.bisect_left(heaters, house) dist_to_right_heater = abs(heaters[pos] - house) if pos < len(heaters) else float('inf') dist_to_left_heater = abs(heaters[pos-1] - house) if pos > 0 else float('inf') min_dist_for_house = min(dist_to_right_heater, dist_to_left_heater) max_distance = max(max_distance, min_dist_for_house) return max_distance ``` This code snippet sorts the lists of houses and heaters first. For each house, it finds the nearest heater either directly or indirectly (to the left side). It calculates the minimal distance between these two options and updates the global maximal value accordingly[^3]. The constraints specify that numbers of houses and heaters do not exceed 25000 while their positions range up to \(10^9\)[^2], making efficient algorithms like binary search necessary due to large input sizes involved.
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