002-time-and-space-complexity.js

// What is time complexity?
// Time complexity is a way to analyze the performance of an algorithm by describing the amount of time it takes to run as a function of the size of the input. It is commonly expressed using big O notation, which describes the upper bound on the number of operations the algorithm will perform. For example, an algorithm with a time complexity of O(n) will take proportionally longer to run as the size of the input (n) increases.

// What is space complexity?
// Space complexity is a way to analyze the storage requirements of an algorithm by describing the amount of memory it uses as a function of the size of the input. Like time complexity, it is commonly expressed using big O notation. An algorithm with a space complexity of O(1) uses a constant amount of memory regardless of the size of the input, while an algorithm with a space complexity of O(n) uses proportionally more memory as the input size (n) increases.

// What is asymptotic notation?
// Asymptotic notation is a way to describe the performance of an algorithm as the input size grows towards infinity. The three most commonly used asymptotic notations are:

// 01. Big O notation: describes the upper bound on the number of operations the algorithm will perform. For example, an algorithm with a time complexity of O(n) will take at most a certain multiple of n operations.

// 02. Big Ω notation: describes the lower bound on the number of operations the algorithm will perform. For example, an algorithm with a time complexity of Ω(n) will take at least a certain multiple of n operations.

// 03. Big Θ notation: describes the tight bound on the number of operations the algorithm will perform. For example, an algorithm with a time complexity of Θ(n) will take exactly a certain multiple of n operations.