The Basics of Big O Runtimes Explained

💡Big O time complexity measures the runtime of an algorithm as the input size grows.

🚀Big O(1) represents constant time algorithms that execute in the same amount of time, regardless of input size.

🔎Binary search is an example of an algorithm with a log(n) time complexity, where each iteration eliminates half of the remaining elements.

🔢Big O(n) represents linear time algorithms, where the runtime grows proportionately with the input size.

💥Big O(n^2) represents algorithms with nested loops, resulting in quadratic time complexity.

What does Big O time complexity measure?

—Big O time complexity measures the runtime of an algorithm as the input size grows.

What is Big O(1) time complexity?

—Big O(1) represents constant time algorithms that execute in the same amount of time, regardless of input size.

What is log(n) time complexity?

—Log(n) time complexity is commonly used in binary search algorithms, where each iteration eliminates half of the remaining elements.

What is Big O(n) time complexity?

—Big O(n) represents linear time algorithms, where the runtime grows proportionately with the input size.

What is Big O(n^2) time complexity?

—Big O(n^2) represents algorithms with nested loops, resulting in quadratic time complexity.

00:00Introduction and the basics of Big O runtime complexities.

02:20Explanation of Big O(1) and constant time algorithms.

03:58Exploration of log(n) time complexity and its application in binary search.

06:40Definition and examples of Big O(n) time complexity.

08:40Description of Big O(n^2) and algorithms with nested loops.