JavaScript Algorithms and Data Structures

Data Structures

1. Linked List
2. Queue
3. Stack
4. Hash Table
5. Heap
6. Priority Queue
7. Trie
8. Tree
   • Binary Search Tree
   • AVL Tree
   • Red-Black Tree
   • Suffix Tree
   • Segment Tree or Interval Tree
   • Binary Indexed Tree or Fenwick Tree
9. Graph (both directed and undirected)
10. Disjoint Set

Algorithms

Algorithms by Topic

• 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
  • Binary Search
• Sorting
  • Bubble Sort
  • Selection Sort
  • Insertion Sort
  • Heap Sort
  • Merge Sort
  • Quicksort
  • Shellsort
• 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
  • Strongly Connected Components - Kosaraju's algorithm
• Uncategorized
  • Tower of Hanoi
  • N-Queens Problem
  • Union-Find
  • Maze
  • Sudoku

Algorithms by Paradigm

• 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
  • N-Queens Problem
• Branch & Bound

How to use this repository

Install all dependencies

npm i

Run all tests

npm test

Run tests by name

npm test -- -t 'LinkedList'

Playground

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'

Useful Information

References

▶ Data Structures and Algorithms on YouTube

Big O Notation

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 Operations Complexity

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)

Array Sorting Algorithms Complexity

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