javascript-algorithms/README.md
2018-04-16 22:19:06 +03:00

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JavaScript Algorithms and Data Structures

Build Status codecov

Code Examples

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
    • B-Tree
    • 2-3 Tree
    • Red-Black Tree
    • Suffix Tree
    • Segment Tree or Interval Tree
    • Binary Indexed Tree or Fenwick Tree
  9. Graph

Algorithms

  • Math
    • Fibonacci Number
    • Cartesian Product
    • Power Set
    • Primality Test
    • Collatz Conjecture algorithm
    • Extended Euclidean algorithm
    • Euclidean algorithm to calculate the Greatest Common Divisor (GCD)
    • Find Divisors
    • Fisher-Yates
    • Greatest Difference
    • Least Common Multiple
    • Newton's square
    • Shannon Entropy
  • String
    • String Permutations
    • Combination
    • Minimum Edit distance (Levenshtein Distance)
    • Hamming
    • Huffman
    • Knuth Morris Pratt
    • Longest common subsequence
    • longest common substring
    • Rabin Karp
  • Search
  • Sorting
  • Graph
  • Tree
    • Depth-First Search (DFS)
    • Breadth-First Search (BFS)
  • Minimum Spanning Tree
    • Prims algorithm
    • Kruskals algorithm
  • Dynamic Programming
    • Increasing subsequence
    • Knapsack problem
    • Maximum subarray
    • Maximum sum path
    • Integer Partition
    • Longest common Subsequence
    • Longest Increasing subsequence
    • Shortest common supersequence
  • Uncategorized
    • Union-Find
    • Maze

Running Tests

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

Big O Notation

Order of growth of algorithms specified in Big O notation.

Big O graphs

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