Summary of Big O Notation

00:00:00 - 00:15:00

The video discusses the concept of Big O notation, which is a way of measuring the runtime of an algorithm. The video provides a few examples of how Big O notation can be used to understand the runtime of a code.

• 00:00:00 Big O notation is a way of measuring the runtime of an algorithm or code, and is important for programmers who work with algorithms that may have high scalability. The most common values associated with Big O notation are o(1), o(n), and o(n squared).
• 00:05:00 The video discusses how the logarithmic function grows faster than the x squared function. This is called the big O notation. It can be helpful when designing algorithms to account for how they will scale as input size increases.
• 00:10:00 The video discusses the concept of Big O notation, which is a way of measuring the runtime of an algorithm. The video then provides a few examples of how Big O notation can be used to understand the runtime of a code.
• 00:15:00 The video explains the Big O notation, which is a way of measuring the runtime of an algorithm. The video explains that the while loop in the code above is running "O of n" times, where "n" is the length of the list being searched. The while loop is checking to see if the value of the first variable, "first," is less than or equal to the value of the last variable, "last." If it is, the code inside the while loop is executed; otherwise, the code inside the while loop is skipped. The code inside the while loop is doing this "O of 1" times.