Leetcode 34. Find First and Last Position of Element in Sorted Array

本文详细解析了LeetCode第34题“在有序数组中查找元素的第一个和最后一个位置”,介绍了如何使用二分查找算法在O(logn)的时间复杂度内解决此问题。通过示例说明了算法的具体实现过程,包括定位目标元素并确定其范围。

Leetcode 34. Find First and Last Position of Element in Sorted Array

Given an array of integers nums sorted in ascending order, 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].

Example 1:
Input: nums = [5,7,7,8,8,10], target = 8
Output: [3,4]
Example 2:
Input: nums = [5,7,7,8,8,10], target = 6
Output: [-1,-1]

题目大意:
给定一个有序数组,返回要寻找的值的起始和终止位置(可能有多个),若不存在则返回两个-1.

解题思路:
O(logn)且数组有序可以自然想到二分查找,找到指定元素后就从该位置向两边扩展直到不相等为止。

代码:

class Solution {
public:
    vector<int> searchRange(vector<int>& nums, int target) {
        int l = 0, r = nums.size() - 1;
        vector<int> res;
        while(l <= r)
        {
            int mid = l + (r - l) / 2;
            if(nums[mid] == target)
            {
                l = mid, r = mid;
                while(l > 0 && nums[l - 1] == target)
                    --l;
                while(r < nums.size() - 1 && nums[r + 1] == target)
                    ++r;
                res.push_back(l);
                res.push_back(r);
                break;
            }
            else if(nums[mid] < target)
                l = mid + 1;
            else
                r = mid - 1;
        }
        if(res.empty())
            return {-1,-1};
        return res;
    }
};
### LeetCode Problem 34: Find First and Last Position of Element in Sorted Array The task involves finding the starting and ending position of a given target value within an array of integers. If the target is not found in the array, [-1, -1] should be returned. For instance, consider an input where `nums` = [5,7,7,8,8,10], and `target` = 8. The expected output would be [3, 4]. Another example could involve `nums` = [5,7,7,8,8,10], but this time with `target` = 6, leading to an output of [-1, -1]. To solve this problem efficiently: A binary search approach can achieve logarithmic complexity by narrowing down potential positions for both the first and last occurrence of the target element[^1]: ```python def searchRange(nums, target): def findLeftIndex(nums, target): left, right = 0, len(nums) - 1 while left <= right: mid = (left + right) // 2 if nums[mid] < target: left = mid + 1 else: right = mid - 1 return left def findRightIndex(nums, target): left, right = 0, len(nums) - 1 while left <= right: mid = (left + right) // 2 if nums[mid] <= target: left = mid + 1 else: right = mid - 1 return right left_index = findLeftIndex(nums, target) right_index = findRightIndex(nums, target) # Check if the target exists in the list. if left_index <= right_index < len(nums) and nums[left_index] == target: return [left_index, right_index] return [-1, -1] ``` This code snippet defines two helper functions that perform modified versions of binary searches—one looking for the start index (`findLeftIndex`) and another for the end index (`findRightIndex`). After determining these indices, it checks whether they are valid before returning them as part of the result.
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