LeetCode 34: Search for a Range

本文详细介绍了如何使用二分查找算法在已排序的整数数组中快速定位目标值的起始和结束位置。通过两种策略实现时间复杂度为O(logn),并提供了关键代码实现细节。

Search for a Range

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(logn).

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].

解题思路

思路一:先使用二分搜索查找目标数,若找到则从该位置向两边查找,若没找到则返回[-1,-1]。该方法不能保证时间复杂度一定是 O(logn)

思路二:使用两次二分查找算法分别找到目标数出现的最左端位置和最右端位置。时间复杂度为O(logn)。代码如下:

class Solution {
private:
    /*
     * 查找 target 出现的最左边的位置
     */
    int findLeftMost(vector<int>& nums, int target) {
        int left = 0, right = nums.size() - 1;
        while ((nums[left] != target) && (left < right)) {
            int middle = (left + right) >> 1;
            if (nums[middle] >= target) {
                left++;
                right = middle;
            }
            else {
                left = middle + 1;
            }
        }
        return nums[left] == target ? left : -1;
    }

    /*
     * 查找 target 出现的最右边的位置
     */
    int findRightMost(vector<int>& nums, int target) {
        int left = 0, right = nums.size() - 1;
        while ((nums[right] != target) && (left < right)) {
            int middle = (left + right) >> 1;
            if (nums[middle] <= target) {
                right--;
                left = middle;
            }
            else {
                right = middle - 1;
            }
        }
        return nums[right] == target ? right : -1;
    }
public:
    vector<int> searchRange(vector<int>& nums, int target) {
        int left = findLeftMost(nums, target);
        int right = findRightMost(nums, target);

        vector<int> result({left, right});
        return result;
    }
};
### 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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