Big O Notation

1. Purpose: Describes the growth rate of an algorithm's runtime.

   • Example: Linear search is O(n), binary search is O(log n).

2. Common Notations:

   • O(1): Constant time (e.g., accessing an array element).
   • O(n): Linear time (e.g., traversing a list).
   • O(n²): Quadratic time (e.g., nested loops).
   • O(log n): Logarithmic time (e.g., binary search).

3. Importance: Helps choose efficient algorithms for large datasets.