Testing Your Knowledge of Algorithm Analysis and Big-O Notation

Algorithm analysis is the analysis of the complexity of different algorithms and finding the most efficient algorithm to solve the problem at hand.

Big-O notation is a statistical measure used to describe the complexity of the algorithm.

The key takeaway is - Big-O isn't interested in a particular instance in which you run an algorithm, such as fact(50), but rather, how well does it scale given increasing input.

To build intuition: anything worse than linear is considered a bad complexity.

An algorithm's complexity is said to be constant, linear, or quadratic, depending on how the steps required to complete the execution of the algorithm change linearly, quadratically, or exponentially with the number of inputs, respectively.

In the case of linear complexity, the number of steps performed remains the same regardless of the size of the input list.

In the case of quadratic complexity, the number of steps performed increases or decreases linearly with the size of the input list.

In the case of logarithmic complexity, the number of steps performed increases or decreases linearly with the size of the input list, but at a much slower rate.

The Big-O notation is a standard metric used to measure the complexity of an algorithm.

The complexity of an algorithm can be measured in terms of its time complexity (O(n)) or space complexity (O(n)).

The best case complexity of an algorithm is when the searched item is found in the first searched index. The worst case complexity is when the algorithm has to search through all the items in the list.

The average complexity of an algorithm is the time complexity divided by the number of inputs.

ons with the help of different Python examples. Finally, we briefly reviewed the worst and the best case complexity along with the space complexity.