I’d like to deepen my understanding of Big-O notation and how it measures algorithm performance. Specifically, I want to clarify:

1. What is Big-O notation?

   • I understand it represents the upper bound of an algorithm’s time complexity, but I’m unsure how to interpret it practically. Does it always indicate the worst-case scenario?

2. Examples of different complexities:

   • Could you provide real-world examples of algorithms or operations that fall into these categories:

     • O(1): Constant time complexity, where the execution time does not depend on the input size.

     • O(n): Linear time complexity, where the execution time increases proportionally with the input size.

     • O(log n): Logarithmic time complexity, where the execution time increases slower than the input size, often seen in divide-and-conquer algorithms like binary search.

Additionally, I’d like to know how Big-O compares to other notations like Omega (Ω) and Theta (Θ). Any insights or resources would be greatly appreciated