Explain the concept of a binary search algorithm and its time complexity

Explain the concept of a binary search algorithm and its time complexity.

Hey Matthew

Binary search is an efficient search algorithm that works on sorted arrays by repeatedly dividing the search space in half. It has a time complexity of O(log n), where n is the number of elements in the array. Here’s an explanation of the concept and its time complexity:


  1. Initial Setup: Start with the entire sorted array as the search space. Define three pointers: low (beginning of the array), high (end of the array), and mid (middle of the current search space).

  2. Determine Middle: Calculate the middle index of the current search space using the formula: mid = (low + high) / 2.

  3. Comparison: Compare the element at the mid index with the target value:

    • If it’s a match, return the mid index.
    • If the target value is greater than the element at mid, it means the desired element must be in the right half of the array. Update low to mid + 1 and repeat the process.
    • If the target value is less than the element at mid, it indicates the desired element must be in the left half. Update high to mid - 1 and continue.
  4. Repeat: Continue the process of halving the search space and comparing until the value is found or the low pointer exceeds the high pointer, indicating the value isn’t in the array.

Time Complexity:

The beauty of binary search lies in its efficiency. With each comparison, it halves the search space. Thus, for a list of size n, it takes at most log₂(n) comparisons to find an element (or determine its absence).

  • Best Case: O(1) - This happens if the middle element of the first search is the target value.

  • Average and Worst Case: O(log n) - On average and in the worst case, the time taken will be proportional to the logarithm of the array’s size to the base 2.

It’s worth noting that binary search can only be applied to sorted arrays/lists. If the array isn’t sorted, you’d need to sort it first, which would add to the overall time complexity. However, once sorted, subsequent searches are very efficient with binary search.