Big O(1) Complexity

The video tutorial introduces Big O notation, focusing on constant time complexity (O(1)). It uses a student list example to demonstrate that operations remain constant regardless of input size. The tutorial explains that even if multiple operations are performed, they are still considered constant. The video emphasizes understanding the concept of constant time and how it is represented graphically. It concludes by stating that in Big O notation, constants are simplified to O(1), and prepares viewers for further exploration of complexity calculations.

What does O(1) complexity signify in terms of operations and input size?

• Operations increase with input size

• Operations depend on the square of input size

• Operations decrease with input size

• Operations remain constant regardless of input size

If a function prints the first element of a list, what is its time complexity?

• O(1)

• O(n^2)

• O(log n)

• O(n)

How does the number of operations change when a function prints the first two elements of a list?

• It becomes linear with input size

• It remains constant

• It becomes quadratic

• It doubles with input size

What is the significance of simplifying constants in Big O notation?

• It helps in understanding the worst-case scenario

• It simplifies the representation of constant time complexities

• It is only used for logarithmic complexities

• It makes calculations more complex

Why are constants like O(2) or O(3) not used in Big O notation?

• They are considered as linear time complexities

• They are considered as constant time complexities

• They are considered as exponential time complexities

• They are considered as logarithmic time complexities