Today, we're delving into the important topic of advanced interview problems revolving around Binary Search. You're likely familiar with the concept of Binary Search – it's an efficient algorithm for finding a specific target in a sorted list by repetitively dividing the search interval in half. Today, we are going to reinforce our understanding by tackling complex data science interview problems using Binary Search.
Imagine a sorted array of integers that has been rotated at an unknown pivot point. This list maintains its sorted order but now starts from a random position. Your task is to find a specific target value within this array and return its index. If the target isn't present, return -1.
For example, the initial sorted array could be [1, 2, 4, 5, 8, 9, 11, 15], but after a single rotation it'd become [8, 9, 11, 15, 1, 2, 4, 5].
Example Application: Picture a server system where processes are listed in ascending order based on their IDs. Suppose a disruption rotates this list. Now, the system needs to find a process using a specific ID. A standard binary search isn't sufficient as the list, though sorted, starts at an arbitrary point.
Naive Approach: A straightforward solution involves scanning each element in the array until we find a match or exhaust the list. This linear search approach is simple but computationally expensive for large lists - its time complexity is .
