JavaScript Algorithms and Data Structures
This repository contains JavaScript based examples of many popular algorithms and data structures.
Each algorithm and data structure has its own separate README with related explanations and links for further reading (including ones to YouTube videos).
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☝ Note that this project is meant to be used for learning and researching purposes only, and it is not meant to be used for production.
A data structure is a particular way of organizing and storing data in a computer so that it can be accessed and modified efficiently. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.
- Beginner, A
- Advanced
B
B
Doubly Linked ListB
B
StackB
B
Heap - max and min heap versionsB
A
TrieA
A
Binary Search TreeA
A
Red-Black TreeA
- with min/max/sum range queries examplesA
Fenwick Tree (Binary Indexed Tree)
A
(both directed and undirected)A
Disjoint SetA
An algorithm is an unambiguous specification of how to solve a class of problems. It is a set of rules that precisely define a sequence of operations.
- Math
B
Bit Manipulation - set/get/update/clear bits, multiplication/division by two, make negative etc.B
B
Fibonacci Number - classic and closed-form versionsB
- finding prime factors and counting them using Hardy-Ramanujan’s theoremB
Primality Test (trial division method)B
- calculate the Greatest Common Divisor (GCD)B
Least Common Multiple (LCM)B
- finding all prime numbers up to any given limitB
Is Power of Two - check if the number is power of two (naive and bitwise algorithms)B
B
Complex Number - complex numbers and basic operations with themB
- radians to degree and backwards conversionB
Fast PoweringB
- polynomial evaluationB
Matrices - matrices and basic matrix operations (multiplication, transposition, etc.)B
- distance between two points/vectors/matricesA
Integer PartitionA
- Newton’s methodA
Liu Hui π Algorithm - approximate π calculations based on N-gonsA
- decompose a function of time (a signal) into the frequencies that make it up
- Sets
B
Cartesian Product - product of multiple setsB
- random permutation of a finite sequenceA
Power Set - all subsets of a set (bitwise and backtracking solutions)A
(with and without repetitions)A
Combinations (with and without repetitions)A
(LCS)A
Longest Increasing SubsequenceA
(SCS)A
Knapsack Problem - “0/1” and “Unbound” onesA
- find all combinations that form specific sum
- Strings
- Hamming Distance - number of positions at which the symbols are different
A
- minimum edit distance between two sequencesA
Knuth–Morris–Pratt Algorithm (KMP Algorithm) - substring search (pattern matching)A
- substring search (pattern matching)A
Rabin Karp Algorithm - substring searchA
A
Regular Expression Matching
- Searches
B
B
Jump Search (or Block Search) - search in sorted arrayB
- search in sorted arrayB
Interpolation Search - search in uniformly distributed sorted array
- Sorting
B
B
Selection SortB
B
Heap SortB
B
Quicksort - in-place and non-in-place implementationsB
B
Counting SortB
- Linked Lists
- Trees
B
Depth-First Search (DFS)B
(BFS)
- Graphs
B
Depth-First Search (DFS)B
(BFS)B
Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graphA
- finding the shortest paths to all graph vertices from single vertexA
Bellman-Ford Algorithm - finding the shortest paths to all graph vertices from single vertexA
- find the shortest paths between all pairs of verticesA
Detect Cycle - for both directed and undirected graphs (DFS and Disjoint Set based versions)A
- finding Minimum Spanning Tree (MST) for weighted undirected graphA
Topological Sorting - DFS methodA
- Tarjan’s algorithm (DFS based)A
Bridges - DFS based algorithmA
- Fleury’s algorithm - Visit every edge exactly onceA
Hamiltonian Cycle - Visit every vertex exactly onceA
- Kosaraju’s algorithmA
Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin city
- Cryptography
B
- rolling hash function based on polynomialB
Rail Fence Cipher - a transposition cipher algorithm for encoding messagesB
- simple substitution cipherB
Hill Cipher - substitution cipher based on linear algebra
- Machine Learning
B
- 7 simple JS functions that illustrate how machines can actually learn (forward/backward propagation)B
k-NN - k-nearest neighbors classification algorithmB
- k-Means clustering algorithm
- Uncategorized
B
Tower of HanoiB
- in-place algorithmB
Jump Game - backtracking, dynamic programming (top-down + bottom-up) and greedy examplesB
- trapping rain water problem (dynamic programming and brute force versions)B
Recursive Staircase - count the number of ways to reach to the top (4 solutions)- - divide and conquer and one-pass examples
A
N-Queens ProblemA
Algorithms by Paradigm
An algorithmic paradigm is a generic method or approach which underlies the design of a class of algorithms. It is an abstraction higher than the notion of an algorithm, just as an algorithm is an abstraction higher than a computer program.
- Brute Force - look at all the possibilities and selects the best solution
B
Linear SearchB
- trapping rain water problemB
Recursive Staircase - count the number of ways to reach to the topA
A
Travelling Salesman Problem - shortest possible route that visits each city and returns to the origin cityA
- decompose a function of time (a signal) into the frequencies that make it up
- Greedy - choose the best option at the current time, without any consideration for the future
B
Jump GameA
A
Dijkstra Algorithm - finding the shortest path to all graph verticesA
- finding Minimum Spanning Tree (MST) for weighted undirected graphA
Kruskal’s Algorithm - finding Minimum Spanning Tree (MST) for weighted undirected graph
- Divide and Conquer - divide the problem into smaller parts and then solve those parts
B
B
Tower of HanoiB
B
Euclidean Algorithm - calculate the Greatest Common Divisor (GCD)B
B
QuicksortB
(DFS)B
Graph Depth-First Search (DFS)B
- generating and traversing the matrices of different shapesB
Jump GameB
B
Best Time To Buy Sell Stocks - divide and conquer and one-pass examplesA
(with and without repetitions)A
Combinations (with and without repetitions)
- Dynamic Programming - build up a solution using previously found sub-solutions
B
B
Jump GameB
B
Rain Terraces - trapping rain water problemB
- count the number of ways to reach to the topA
Levenshtein Distance - minimum edit distance between two sequencesA
(LCS)A
Longest Common SubstringA
A
Shortest Common SupersequenceA
A
Integer PartitionA
A
Bellman-Ford Algorithm - finding the shortest path to all graph verticesA
- find the shortest paths between all pairs of verticesA
Regular Expression Matching
- Backtracking - similarly to brute force, try to generate all possible solutions, but each time you generate next solution you test if it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on a different path of finding a solution. Normally the DFS traversal of state-space is being used.
B
B
Unique PathsB
- all subsets of a setA
Hamiltonian Cycle - Visit every vertex exactly onceA
A
Knight’s TourA
- find all combinations that form specific sum
- Branch & Bound - remember the lowest-cost solution found at each stage of the backtracking search, and use the cost of the lowest-cost solution found so far as a lower bound on the cost of a least-cost solution to the problem, in order to discard partial solutions with costs larger than the lowest-cost solution found so far. Normally BFS traversal in combination with DFS traversal of state-space tree is being used.
Install all dependencies
Run ESLint
You may want to run it to check code quality.
Run all tests
Run tests by name
Playground
Then just simply run the following command to test if your playground code works as expected:
▶ Data Structures and Algorithms on YouTube
Big O Notation
Big O notation is used to classify algorithms according to how their running time or space requirements grow as the input size grows. On the chart below you may find most common orders 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.