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Quick TrainerBig-O Time Complexity of Common Operations & Algorithms
Test your knowledge of the time complexity of common data structure operations and classic algorithms.
Code35 items4 levelsDifficulty 3/5
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Levels
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1
Basics
6 items
2
Sorting & Searching
9 items
3
Data Structures
10 items
4
Advanced
10 items
All 35 cards
Accessing an element by index in an array
O(1)
Linear search through an unsorted array
O(n)
Binary search on a sorted array
O(log n)
Iterating through all elements of an array once
O(n)
Nested loop comparing every pair of n elements
O(n^2)
Appending to the end of a dynamic array (amortized)
O(1)
Bubble sort (worst case)
O(n^2)
Insertion sort (worst case)
O(n^2)
Selection sort (all cases)
O(n^2)
Merge sort (worst case)
O(n log n)
Quicksort (average case)
O(n log n)
Quicksort (worst case)
O(n^2)
Heap sort (worst case)
O(n log n)
Counting sort
O(n + k)
Best possible worst-case for a comparison-based sort
O(n log n)
Hash table lookup (average case)
O(1)
Hash table lookup (worst case)
O(n)
Insertion at the head of a singly linked list
O(1)
Searching for a value in a singly linked list
O(n)
Push or pop on a stack
O(1)
Enqueue or dequeue on a queue
O(1)
Search in a balanced binary search tree
O(log n)
Search in an unbalanced (degenerate) binary search tree
O(n)
Insert or extract-min on a binary heap
O(log n)
Peek at the minimum of a binary min-heap
O(1)
Building a heap from an unsorted array (heapify)
O(n)
Breadth-first or depth-first search of a graph
O(V + E)
Dijkstra's algorithm with a binary heap
O((V + E) log V)
Floyd-Warshall all-pairs shortest paths
O(V^3)
Naive recursive Fibonacci
O(2^n)
Generating all permutations of n elements
O(n!)
Generating all subsets of a set of n elements
O(2^n)
Naive (brute-force) traveling salesman problem
O(n!)
Matrix multiplication (standard naive algorithm)
O(n^3)
Kruskal's algorithm for minimum spanning tree
O(E log E)
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