Weekly-Challenge-04-Binary-Search-Iterative-

For this week's coding challenge, I implemented the Binary Search algorithm using an iterative approach.

Week 4: Iterative Binary Search

Description

For this week's coding challenge, I implemented the Binary Search algorithm using an iterative approach.

Binary Search is a classic "Divide and Conquer" algorithm that finds the position of a target value within a sorted array. It compares the target value to the middle element of the array and eliminates the half in which the target cannot lie.

How it works (The Code Logic)

1. Data Generation: I used NumPy to generate a random list size (n) and random integers.
2. Sorting: The list is sorted using np.sort() (Crucial step: Binary Search only works on sorted data).
3. The Loop: - We define low and high pointers.
   • We calculate the mid point: (low + high) // 2.
   • If arr[mid] == target, we return the index.
   • If arr[mid] > target, we discard the right half.
   • If arr[mid] < target, we discard the left half.
4. Tracking: The script prints every attempt to demonstrate how quickly the algorithm narrows down the search.

Complexity Analysis

• Time Complexity: $O(\log n)$ - Logarithmic time. Even with a large n, the number of attempts grows very slowly.
• Space Complexity: $O(1)$ - Iterative implementation (uses constant extra space).

🛠 Dependencies

• Python 3.14.2
• NumPy