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This repository contains JavaScript based examples of many popular algorithms and data structures.
Each algorithm and data structure have its own separate README with related explanations and link for further reading and YouTube videos.
- Linked List
- Queue
- Stack
- Hash Table
- Heap
- Priority Queue
- Trie
- Tree
- Binary Search Tree
- AVL Tree
- Red-Black Tree
- Suffix Tree
- Segment Tree or Interval Tree
- Binary Indexed Tree or Fenwick Tree
- Graph (both directed and undirected)
- Disjoint Set
- Math
- Factorial
- Fibonacci Number
- Primality Test (trial division method)
- Euclidean Algorithm - calculate the Greatest Common Divisor (GCD)
- Least Common Multiple (LCM)
- Integer Partition
- Sets
- Cartesian Product - product of multiple sets
- Power Set - all subsets of the set
- Permutations (with and without repetitions)
- Combinations (with and without repetitions)
- Fisher–Yates Shuffle - random permutation of a finite sequence
- Longest Common Subsequence (LCS)
- Longest Increasing subsequence
- Shortest Common Supersequence (SCS)
- Knapsack Problem - "0/1" and "Unbound" ones
- Maximum Subarray - "Brute Force" and "Dynamic Programming" (Kadane's) versions
- String
- Levenshtein Distance - minimum edit distance between two sequences
- Hamming Distance - number of positions at which the symbols are different
- Knuth–Morris–Pratt Algorithm - substring search
- Rabin Karp Algorithm - substring search
- Longest Common Substring
- Search
- Sorting
- Tree
- Depth-First Search (DFS)
- Breadth-First Search (BFS)
- Graph
- Depth-First Search (DFS)
- Breadth-First Search (BFS)
- Dijkstra Algorithm - finding shortest path to all graph vertices
- Bellman-Ford Algorithm - finding shortest path to all graph vertices
- Detect Cycle - for both directed and undirected graphs (DFS and Disjoint Set based versions)
- Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph
- Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph
- Topological Sorting - DFS method
- Articulation Points - Tarjan's algorithm (DFS based)
- Bridges - DFS based algorithm
- Eulerian Path and Eulerian Circuit - Fleury's algorithm - Visit every edge exactly once
- Hamiltonian Cycle - Visit every vertex exactly once
- Strongly Connected Components - Kosaraju's algorithm
- Uncategorized
- Greedy
- Unbound Knapsack Problem
- Dijkstra Algorithm - finding shortest path to all graph vertices
- Prim’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph
- Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph
- Divide and Conquer
- Tower of Hanoi
- Euclidean Algorithm - calculate the Greatest Common Divisor (GCD)
- Permutations (with and without repetitions)
- Combinations (with and without repetitions)
- Merge Sort
- Quicksort
- Tree Depth-First Search (DFS)
- Graph Depth-First Search (DFS)
- Dynamic Programming
- Fibonacci Number
- Levenshtein Distance - minimum edit distance between two sequences
- Longest Common Subsequence (LCS)
- Longest Common Substring
- Longest Increasing subsequence
- Shortest Common Supersequence
- 0/1 Knapsack Problem
- Integer Partition
- Maximum Subarray
- Bellman-Ford Algorithm - finding shortest path to all graph vertices
- Backtracking
- Hamiltonian Cycle - Visit every vertex exactly once
- N-Queens Problem
- Knight's Tour
- Branch & Bound
Install all dependencies
npm install
Run all tests
npm test
Run tests by name
npm test -- -t 'LinkedList'
You may play with data-structures and algorithms in ./src/playground/playground.js file and write tests for it in ./src/playground/__test__/playground.test.js.
Then just simply run the following command to test if your playground code works as expected:
npm test -- -t 'playground'
▶ Data Structures and Algorithms on YouTube
Order of growth of algorithms specified in Big O notation.
Source: Big O Cheat Sheet.
Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data.
| Big O Notation | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements |
|---|---|---|---|
| O(1) | 1 | 1 | 1 |
| O(log N) | 3 | 6 | 9 |
| O(N) | 10 | 100 | 1000 |
| O(N log N) | 30 | 60 | 9000 |
| O(N^2) | 100 | 10000 | 1000000 |
| O(2^N) | 1024 | 1.26e+29 | 1.07e+301 |
| O(N!) | 3628800 | 9.3e+157 | 4.02e+2567 |
| Data Structure | Access | Search | Insertion | Deletion |
|---|---|---|---|---|
| Array | 1 | n | n | n |
| Stack | n | n | 1 | 1 |
| Queue | n | n | 1 | 1 |
| Linked List | n | n | 1 | 1 |
| Hash Table | - | n | n | n |
| Binary Search Tree | n | n | n | n |
| B-Tree | log(n) | log(n) | log(n) | log(n) |
| Red-Black Tree | log(n) | log(n) | log(n) | log(n) |
| AVL Tree | log(n) | log(n) | log(n) | log(n) |
| Name | Best | Average | Worst | Memory | Stable |
|---|---|---|---|---|---|
| Bubble sort | n | n^2 | n^2 | 1 | Yes |
| Insertion sort | n | n^2 | n^2 | 1 | Yes |
| Selection sort | n^2 | n^2 | n^2 | 1 | No |
| Heap sort | n log(n) | n log(n) | n log(n) | 1 | No |
| Merge sort | n log(n) | n log(n) | n log(n) | n | Yes |
| Quick sort | n log(n) | n log(n) | n^2 | log(n) | No |
| Shell sort | n log(n) | depends on gap sequence | n (log(n))^2 | 1 | No |