Python 3.8 (Coding): Search in Rotated Array Test

The goal of the problem is to search the target value in a rotated sorted array.

Available in

  • English

Summarize this test and see how it helps assess top talent with:

1 Skills measured

  • binary search

Test Type

Coding Test

Duration

25 mins

Level

Intermediate

Questions

1

Use of Python 3.8 (Coding): Search in Rotated Array Test

The Search in Rotated Sorted Array problem involves finding a target value in a rotated sorted array. The solution approach is to start by initializing two pointers, left and right, which represent the boundaries of the search range. Initially, left = 0 and right = len(nums) - 1. a. Calculate the mid index as mid = (left + right) // 2. b. Check if the value at the mid index (nums[mid]) is equal to the target value. If it is, we have found the target and can return the mid index. c. If nums[left] <= nums[mid], it means the left half of the array is sorted in ascending order. Check if the target value lies within the sorted left half, i.e., nums[left] <= target < nums[mid]. If it does, update right = mid - 1 to search the left half. If the target value does not lie within the sorted left half, update left = mid + 1 to search the right half. d. If nums[mid] <= nums[right], it means the right half of the array is sorted in ascending order. Check if the target value lies within the sorted right half, i.e., nums[mid] < target <= nums[right]. If it does, update left = mid + 1 to search the right half. If the target value does not lie within the sorted right half, update right = mid - 1 to search the left half. Repeat steps 2b-2d until the target value is found or the search range is exhausted (left > right). If the target value is not found after the search range is exhausted, return -1 to indicate that the target is not present in the array.

Skills measured

Binary search is a crucial skill covered in the Search in Rotated Sorted Array problem as it allows us to efficiently search for a target element in a rotated sorted array. By repeatedly dividing the array in half and comparing the target element with the middle element, we can quickly narrow down the search space. This technique is important because it has a time complexity of O(log n), making it much faster than a linear search. Mastering binary search can greatly improve algorithm efficiency and problem-solving skills in computer science.

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