📄️ Introduction to Big O Notation
"O" of "Big O" is from “order,” which is related to the rate of growth—for example, of time or space. Big O complexity is an asymptotic property that describes the theoretical limits of time and space—required to execute a program following a specific approach—as the size of the input arguments tends to infinity. Time complexity is therefore a limiting behavior associated with the total runtime of a program. Likewise, space complexity is associated with the theoretical limit to space/memory required for its successful execution.
📄️ Big O—Popular Data Structures
Table below shows the average time complexities related to different operations for different data structures. Also, it includes corresponding (worst) space complexities.
📄️ Big O—Popular Algorithms