So this is something I always tend to forget as I grow more grey hair, but a good source reference for **Big O Notation** as a cheatsheet, can be found at http://bigocheatsheet.com which I have included below. Of course this would be invaluable in job interviews, so read up and prep up beforehand and you should be fine:

## Searching

Algorithm Data Structure Time Complexity Space Complexity Average Worst Worst Depth First Search (DFS) Graph of |V| vertices and |E| edges `-`

`O(|E| + |V|)`

`O(|V|)`

Breadth First Search (BFS) Graph of |V| vertices and |E| edges `-`

`O(|E| + |V|)`

`O(|V|)`

Binary search Sorted array of n elements `O(log(n))`

`O(log(n))`

`O(1)`

Linear (Brute Force) Array `O(n)`

`O(n)`

`O(1)`

Shortest path by Dijkstra,

using a Min-heap as priority queueGraph with |V| vertices and |E| edges `O((|V| + |E|) log |V|)`

`O((|V| + |E|) log |V|)`

`O(|V|)`

Shortest path by Dijkstra,

using an unsorted array as priority queueGraph with |V| vertices and |E| edges `O(|V|^2)`

`O(|V|^2)`

`O(|V|)`

Shortest path by Bellman-Ford Graph with |V| vertices and |E| edges `O(|V||E|)`

`O(|V||E|)`

`O(|V|)`

## Sorting

Algorithm Data Structure Time Complexity Worst Case Auxiliary Space Complexity Best Average Worst Worst Quicksort Array `O(n log(n))`

`O(n log(n))`

`O(n^2)`

`O(n)`

Mergesort Array `O(n log(n))`

`O(n log(n))`

`O(n log(n))`

`O(n)`

Heapsort Array `O(n log(n))`

`O(n log(n))`

`O(n log(n))`

`O(1)`

Bubble Sort Array `O(n)`

`O(n^2)`

`O(n^2)`

`O(1)`

Insertion Sort Array `O(n)`

`O(n^2)`

`O(n^2)`

`O(1)`

Select Sort Array `O(n^2)`

`O(n^2)`

`O(n^2)`

`O(1)`

Bucket Sort Array `O(n+k)`

`O(n+k)`

`O(n^2)`

`O(nk)`

Radix Sort Array `O(nk)`

`O(nk)`

`O(nk)`

`O(n+k)`

## Data Structures

Data Structure Time Complexity Space Complexity Average Worst Worst Indexing Search Insertion Deletion Indexing Search Insertion Deletion Basic Array `O(1)`

`O(n)`

`-`

`-`

`O(1)`

`O(n)`

`-`

`-`

`O(n)`

Dynamic Array `O(1)`

`O(n)`

`O(n)`

`O(n)`

`O(1)`

`O(n)`

`O(n)`

`O(n)`

`O(n)`

Singly-Linked List `O(n)`

`O(n)`

`O(1)`

`O(1)`

`O(n)`

`O(n)`

`O(1)`

`O(1)`

`O(n)`

Doubly-Linked List `O(n)`

`O(n)`

`O(1)`

`O(1)`

`O(n)`

`O(n)`

`O(1)`

`O(1)`

`O(n)`

Skip List `O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(n)`

`O(n)`

`O(n)`

`O(n)`

`O(n log(n))`

Hash Table `-`

`O(1)`

`O(1)`

`O(1)`

`-`

`O(n)`

`O(n)`

`O(n)`

`O(n)`

Binary Search Tree `O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(n)`

`O(n)`

`O(n)`

`O(n)`

`O(n)`

Cartresian Tree `-`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`-`

`O(n)`

`O(n)`

`O(n)`

`O(n)`

B-Tree `O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(n)`

Red-Black Tree `O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(n)`

Splay Tree `-`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`-`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(n)`

AVL Tree `O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(n)`

## Heaps

Heaps Time Complexity Heapify Find Max Extract Max Increase Key Insert Delete Merge Linked List (sorted) `-`

`O(1)`

`O(1)`

`O(n)`

`O(n)`

`O(1)`

`O(m+n)`

Linked List (unsorted) `-`

`O(n)`

`O(n)`

`O(1)`

`O(1)`

`O(1)`

`O(1)`

Binary Heap `O(n)`

`O(1)`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(m+n)`

Binomial Heap `-`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

`O(log(n))`

Fibonacci Heap `-`

`O(1)`

`O(log(n))*`

`O(1)*`

`O(1)`

`O(log(n))*`

`O(1)`

## Graphs

Node / Edge Management Storage Add Vertex Add Edge Remove Vertex Remove Edge Query Adjacency list `O(|V|+|E|)`

`O(1)`

`O(1)`

`O(|V| + |E|)`

`O(|E|)`

`O(|V|)`

Incidence list `O(|V|+|E|)`

`O(1)`

`O(1)`

`O(|E|)`

`O(|E|)`

`O(|E|)`

Adjacency matrix `O(|V|^2)`

`O(|V|^2)`

`O(1)`

`O(|V|^2)`

`O(1)`

`O(1)`

Incidence matrix `O(|V| ⋅ |E|)`

`O(|V| ⋅ |E|)`

`O(|V| ⋅ |E|)`

`O(|V| ⋅ |E|)`

`O(|V| ⋅ |E|)`

`O(|E|)`

## Notation for asymptotic growth

letter bound growth (theta) Θ upper and lower, tight ^{[1]}equal ^{[2]}(big-oh) O upper, tightness unknown less than or equal ^{[3]}(small-oh) o upper, not tight less than (big omega) Ω lower, tightness unknown greater than or equal (small omega) ω lower, not tight greater than [1] Big O is the upper bound, while Omega is the lower bound. Theta requires both Big O and Omega, so that’s why it’s referred to as a tight bound (it must be both the upper and lower bound). For example, an algorithm taking Omega(n log n) takes at least n log n time but has no upper limit. An algorithm taking Theta(n log n) is far preferential since it takes AT LEAST n log n (Omega n log n) and NO MORE THAN n log n (Big O n log n).

^{SO}[2] f(x)=Θ(g(n)) means f (the running time of the algorithm) grows exactly like g when n (input size) gets larger. In other words, the growth rate of f(x) is asymptotically proportional to g(n).

[3] Same thing. Here the growth rate is no faster than g(n). big-oh is the most useful because represents the worst-case behavior.

In short, if algorithm is __ then its performance is __

algorithm performance o(n) < n O(n) ≤ n Θ(n) = n Ω(n) ≥ n ω(n) > n ## Big-O Complexity Chart

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