leetcode--34. Search for a Range

本文介绍了一种算法,用于在已排序的整数数组中找到指定目标值的起始和结束位置,采用二分查找法实现O(log n)的时间复杂度。

Given a sorted array of integers, find the starting and ending position of a given target value.

Your algorithm's runtime complexity must be in the order of O(log n).

If the target is not found in the array, return [-1, -1].

For example,
Given [5, 7, 7, 8, 8, 10] and target value 8,
return [3, 4].

题目大意:给定一个排序的数组和一个目标数,寻找该目标数在数组中出现的首尾位置。如题中例子,8在数组中是第3、4个。

思路:排序数组->二分查找。如果nums[mid] > target 那么end需要变为mid-1;如果nums[mid] < target,那么begin需要变为mid+1;如果nums[mid]==target,那么首尾值肯定是在左右两边,注意边界处理。

class Solution {
public:
    vector<int> searchRange(vector<int>& nums, int target) {
        int begin = 0;
        int end = nums.size()-1;
        vector<int> result;
        
        while(nums[begin] <= nums[end]){
           int mid = (begin+end)/2; 
           if(nums[mid] == target){
                begin = mid ; 
                while(nums[begin] == target && begin>=0){
                    --begin;
                }
                result.push_back(begin+1);
                end = mid;
                while(nums[end] == target && end <= nums.size()-1){
                    ++end;
                }
                result.push_back(end-1);
                return result;
            }
            else if(nums[mid] < target){
                begin = mid + 1;
            }
            else if(nums[mid] > target){
                end = mid -1;
            }
        }
        result.push_back(-1);
        result.push_back(-1);
        return result;
    }
};

leetcode编译通过。
### 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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