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Search in Rotated Sorted Array

Problem Statement:​

There is an integer array nums sorted in ascending order (with distinct values).

Prior to being passed to your function, nums is possibly left rotated at an unknown 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 left rotated by 3 indices 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:

    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

Solution: Binary Search on Rotated Sorted Array​

class Solution {
public:
int search(vector<int>& nums, int target) {
int n = nums.size();
int low = 0;
int high = n - 1;

while (low <= high) {
int mid = low + (high - low) / 2;

// Found the target
if (nums[mid] == target) return mid;

// Check if the left half is sorted
if (nums[low] <= nums[mid]) {
// Target lies in the left half
if (nums[low] <= target && target < nums[mid]) {
high = mid - 1;
}
// Otherwise, search right half
else {
low = mid + 1;
}
}
// Otherwise, right half must be sorted
else {
// Target lies in the right half
if (nums[mid] < target && target <= nums[high]) {
low = mid + 1;
}
// Otherwise, search left half
else {
high = mid - 1;
}
}
}
return -1; // Not found
}
};


📝 How It Works​

  1. This is a binary search adaptation for rotated sorted arrays.
  2. At each step:
    • Check if the current middle element is the target.
    • Determine which half of the array is sorted:
      • If left half is sorted (nums[low] <= nums[mid]), check if the target lies between nums[low] and nums[mid].
      • Otherwise, it must lie in the right half.
    • Narrow down the search range accordingly.
  3. Repeat until the element is found or the search range becomes invalid.

🧩 Key Formula / Recurrence​

Binary search narrowing logic:

  • If nums[low] <= nums[mid]: left half is sorted
    • If nums[low] <= target < nums[mid] → search left
    • Else → search right
  • Else: right half is sorted
    • If nums[mid] < target <= nums[high] → search right
    • Else → search left

⏱️ Time & Space Complexity​

  • Time Complexity: O(log N) (binary search halving each step).
  • Space Complexity: O(1) (only variables used, no extra data structures).

⚠️ Edge Cases​

  • Array with only one element.
  • Target at the very beginning or end of array.
  • No rotation (normal sorted array).
  • Fully rotated (back to original sorted order).
  • Target not present at all.

💡 Other Approaches​

  • Linear Search: O(N) → trivial but inefficient.
  • Find Pivot + Binary Search: First locate rotation pivot, then perform binary search in the correct half. Same time complexity but requires extra steps.

  • LeetCode 153: Find Minimum in Rotated Sorted Array
  • LeetCode 81: Search in Rotated Sorted Array II (with duplicates)
  • LeetCode 162: Find Peak Element

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