LeetCode 34. Search for a Range(搜索范围)

本文介绍了一种使用二分法查找算法解决在已排序整数数组中寻找特定目标值起始和结束位置的问题。该算法的时间复杂度为O(log n),符合题目要求。通过两次遍历分别确定目标值的开始和结束位置。

原题网址:https://leetcode.com/problems/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(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].

方法:二分法查找。

public class Solution {
    public int[] searchRange(int[] nums, int target) {
        int[] range = new int[2];
        range[0] = -1;
        range[1] = -1;
        int i=0, j=nums.length-1;
        while (i<=j) {
            int m = (i+j)/2;
            if (target == nums[m]) {
                range[0] = m;
                j = m-1;
            } else if (target < nums[m]) {
                j = m - 1;
            } else {
                i = m + 1;
            }
        }
        i=0;
        j=nums.length-1;
        while (i<=j) {
            int m = (i+j)/2;
            if (target == nums[m]) {
                range[1] = m;
                i = m + 1;
            } else if (target < nums[m]) {
                j = m - 1;
            } else {
                i = m + 1;
            }
        }
        return range;
    }
}


### 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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