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There is an integer array nums
sorted in ascending order (with distinct values).
Prior to being passed to your function, nums
is possibly rotated at an unknown pivot index k
(1 <= k < nums.length
) such that the resulting array is [nums[k], nums[k+1], ..., nums[n-1], nums[0], nums[1], ..., nums[k-1]]
(0-indexed). For example, [0,1,2,4,5,6,7]
might be rotated at pivot index 3
and become [4,5,6,7,0,1,2]
.
Given the array nums
after the possible rotation and an integer target
, return the index of target
if it is in nums
, or -1
if it is not in nums
.
You must write an algorithm with O(log n)
runtime complexity.
Example 1:
Input: nums = [4,5,6,7,0,1,2], target = 0 Output: 4
Example 2:
Input: nums = [4,5,6,7,0,1,2], target = 3 Output: -1
Example 3:
Input: nums = [1], target = 0 Output: -1
Constraints:
1 <= nums.length <= 5000
-10 4 <= nums[i] <= 10 4
- All values of
nums
are unique. nums
is an ascending array that is possibly rotated.-10 4 <= target <= 10 4
Python
from typing import List class Solution: def search(self, nums: List[int], target: int) -> int: # return nums.index(target) if target in nums else -1 n = len(nums) left, right = 0, n-1 while left <= right: mid = left + (right - left) // 2 if nums[mid] > nums[-1]: left = mid + 1 else: right = mid - 1 def binarySearch(left_boundary: int, right_boundary: int, target: int): left, right = left_boundary, right_boundary while left <= right: mid = left + (right - left) // 2 if nums[mid] == target: return mid elif nums[mid] > target: right = mid - 1 else: left = mid + 1 return -1 if (answer := binarySearch(0, left-1, target)) != -1: return answer return binarySearch( left, n-1, target)