Computational Complexity Analysis

Complexity Analysis

Computational complexity analysis is a theoretical assessment that measures the computational resources required by an algorithm to solve a computational problem.
It provides a quantitative understanding of the speed and spacing requirements of the operations, enabling informed decisions when writing code.

Time Complexity: Measures the amount of time an algorithm takes to complete, typically expressed as a function of the input size (n).
Space Complexity: Measures the amount of memory an algorithm requires, also expressed as a function of the input size (n).
Big-O Notation: A shorthand way to describe the upper bound of an algorithm’s time or space complexity, often used to classify algorithms into categories (e.g., O(1), O(log n), O(n), O(n log n), etc.).

Big-O notation gives an upper bound of the complexity in the worst case, helping to quantify performance as the input size becomes arbitrarily large.
The size of input is represented by 'n'.

Complexities ordered from smallest to largest

Big O really cares about when the input is reaching almost infinity, so in a expression input size with highest order of degree will be considered, as only it will be having substantial impact on the running time of the algorithm.
O(n + c) = O(n)
O(nc) = O(n), c> 0
Let f be a function, describing running time of a particular algorithm and input size be n: f(n) = 7log(n^3) + 15(n^2) + 2(n ^ 3) + 8 Therefore, O(f(n)) = O(n^3)
Examples of Big O time complexities :
- Binary Search - O(log(n))
- Finding all subsets of a set - O(n^2)
- Finding all permutations of a string - O(n!)
- Sorting using mergesort - O(nlog(n))
- Iterating over all the cells in a matrix of size n by m - O(nm)