Monday, September 9, 2013

Know Thy Complexities!!!!

Hi there!  This webpage covers the space and time Big-O complexities of common algorithms used in Computer Science.  When preparing for technical interviews in the past, I found myself spending hours crawling the internet putting together the best, average, and worst case complexities for search and sorting algorithms so that I wouldn't be stumped when asked about them.  Over the last few years, I've interviewed at several Silicon Valley startups, and also some bigger companies, like Yahoo, eBay, LinkedIn, and Google, and each time that I prepared for an interview, I thought to msyelf "Why oh why hasn't someone created a nice Big-O cheat sheet?".  So, to save all of you fine folks a ton of time, I went ahead and created one.  Enjoy!


AlgorithmData StructureTime ComplexitySpace Complexity
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 searchSorted array of n elementsO(log(n))O(log(n))O(1)
Linear (Brute Force)ArrayO(n)O(n)O(1)
Shortest path by Dijkstra,
using a Min-heap as priority queue
Graph with |V| vertices and |E| edgesO((|V| + |E|) log |V|)O((|V| + |E|) log |V|)O(|V|)
Shortest path by Dijkstra,
using an unsorted array as priority queue
Graph with |V| vertices and |E| edgesO(|V|^2)O(|V|^2)O(|V|)
Shortest path by Bellman-FordGraph with |V| vertices and |E| edgesO(|V||E|)O(|V||E|)O(|V|)


AlgorithmData StructureTime ComplexityWorst Case Auxiliary Space Complexity
QuicksortArrayO(n log(n))O(n log(n))O(n^2)O(n)
MergesortArrayO(n log(n))O(n log(n))O(n log(n))O(n)
HeapsortArrayO(n log(n))O(n log(n))O(n log(n))O(1)
Bubble SortArrayO(n)O(n^2)O(n^2)O(1)
Insertion SortArrayO(n)O(n^2)O(n^2)O(1)
Select SortArrayO(n^2)O(n^2)O(n^2)O(1)
Bucket SortArrayO(n+k)O(n+k)O(n^2)O(nk)
Radix SortArrayO(nk)O(nk)O(nk)O(n+k)

Data Structures

Data StructureTime ComplexitySpace Complexity
Basic ArrayO(1)O(n)--O(1)O(n)--O(n)
Dynamic ArrayO(1)O(n)O(n)O(n)O(1)O(n)O(n)O(n)O(n)
Singly-Linked ListO(n)O(n)O(1)O(1)O(n)O(n)O(1)O(1)O(n)
Doubly-Linked ListO(n)O(n)O(1)O(1)O(n)O(n)O(1)O(1)O(n)
Skip ListO(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 TreeO(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)
Red-Black TreeO(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 TreeO(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)


HeapsTime Complexity
HeapifyFind MaxExtract MaxIncrease KeyInsertDeleteMerge
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 HeapO(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)


Node / Edge ManagementStorageAdd VertexAdd EdgeRemove VertexRemove EdgeQuery
Adjacency listO(|V|+|E|)O(1)O(1)O(|V| + |E|)O(|E|)O(|V|)
Incidence listO(|V|+|E|)O(1)O(1)O(|E|)O(|E|)O(|E|)
Adjacency matrixO(|V|^2)O(|V|^2)O(1)O(|V|^2)O(1)O(1)
Incidence matrixO(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|V| ⋅ |E|)O(|E|)

Notation for asymptotic growth

(theta) Θupper and lower, tight[1]equal[2]
(big-oh) Oupper, tightness unknownless than or equal[3]
(small-oh) oupper, not tightless than
(big omega) Ωlower, tightness unknowngreater than or equal
(small omega) ωlower, not tightgreater 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 __
o(n)< n
O(n)≤ n
Θ(n)= n
Ω(n)≥ n
ω(n)> n

Big-O Complexity Chart

Big O Complexity Graph

This article is a direct reproduction with no original work of the author of this page.

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