Computer scienceAlgorithms and Data StructuresIntro to algorithms and data structuresUnderstanding algorithms and complexity analysis

Big O: how to count it

Statements

Report a typo

Select all the correct statements about the big O notation.

Select one or more options from the list

It describes the upper bound or worst-case scenario of an algorithm's growth rate concerning input size.

It depends on an algorithm's implementation details such as programming language and operating system.

It can be used to measure the efficiency of algorithms.

As a rule, an algorithm works even worse than big O shows.

___

Create a free account to access the full topic