Big O(n) Complexity

The video tutorial explains why time is not used to measure code performance and introduces Big O notation as an alternative method. It covers the concept of complexity analysis, using examples to illustrate how Big O notation helps in understanding code efficiency. The tutorial includes a code example of a linear search to demonstrate Big O of N, emphasizing the relationship between the number of operations and inputs. The video concludes with a promise to explore other Big O notations in future lectures.

Why is time not used as a measure to compare code performance?

• Because time is not a reliable metric.

• Because time is not related to code complexity.

• Because time is too difficult to measure.

• Because time is not consistent across different systems.

What does the X-axis represent in the Big O graph?

• Number of operations

• Complexity level

• Number of inputs

• Time taken

In the code example, what is the Big O notation for the search function?

• Big O of 1

• Big O of N

• Big O of N^2

• Big O of log N

What is the pattern followed by the code example in terms of complexity?

• Exponential

• Quadratic

• Linear

• Logarithmic

What is the most common Big O complexity found in practice problems?

• Big O of N

• Big O of 1

• Big O of N^2

• Big O of log N

What does Big O of N indicate about the relationship between operations and inputs?

• Operations decrease as inputs increase.

• Operations are constant regardless of inputs.

• Operations increase linearly with inputs.

• Operations increase exponentially with inputs.

What will be discussed in the next lecture following this one?

• Big O of N^2

• Big O of log N

• Big O of 1

• Big O of N!