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299 lines
19 KiB
Markdown
299 lines
19 KiB
Markdown
# JavaScript Algorithms and Data Structures
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[](https://travis-ci.org/trekhleb/javascript-algorithms)
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[](https://codecov.io/gh/trekhleb/javascript-algorithms)
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This repository contains JavaScript based examples of many
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popular algorithms and data structures.
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Each algorithm and data structure has its own separate README
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with related explanations and links for further reading (including ones
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to YouTube videos).
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_Read this in other languages:_
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[_简体中文_](README.zh-CN.md),
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[_繁體中文_](README.zh-TW.md),
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[_한국어_](README.ko-KR.md),
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[_Polski_](README.pl-PL.md),
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[_Français_](README.fr-FR.md),
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[_Español_](README.es-ES.md),
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[_Português_](README.pt-BR.md)
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## Data Structures
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A data structure is a particular way of organizing and storing data in a computer so that it can
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be accessed and modified efficiently. More precisely, a data structure is a collection of data
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values, the relationships among them, and the functions or operations that can be applied to
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the data.
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`B` - Beginner, `A` - Advanced
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* `B` [Linked List](src/data-structures/linked-list)
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* `B` [Doubly Linked List](src/data-structures/doubly-linked-list)
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* `B` [Queue](src/data-structures/queue)
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* `B` [Stack](src/data-structures/stack)
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* `B` [Hash Table](src/data-structures/hash-table)
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* `B` [Heap](src/data-structures/heap) - max and min heap versions
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* `B` [Priority Queue](src/data-structures/priority-queue)
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* `A` [Trie](src/data-structures/trie)
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* `A` [Tree](src/data-structures/tree)
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* `A` [Binary Search Tree](src/data-structures/tree/binary-search-tree)
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* `A` [AVL Tree](src/data-structures/tree/avl-tree)
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* `A` [Red-Black Tree](src/data-structures/tree/red-black-tree)
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* `A` [Segment Tree](src/data-structures/tree/segment-tree) - with min/max/sum range queries examples
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* `A` [Fenwick Tree](src/data-structures/tree/fenwick-tree) (Binary Indexed Tree)
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* `A` [Graph](src/data-structures/graph) (both directed and undirected)
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* `A` [Disjoint Set](src/data-structures/disjoint-set)
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* `A` [Bloom Filter](src/data-structures/bloom-filter)
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## Algorithms
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An algorithm is an unambiguous specification of how to solve a class of problems. It is
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a set of rules that precisely define a sequence of operations.
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`B` - Beginner, `A` - Advanced
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### Algorithms by Topic
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* **Math**
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* `B` [Bit Manipulation](src/algorithms/math/bits) - set/get/update/clear bits, multiplication/division by two, make negative etc.
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* `B` [Factorial](src/algorithms/math/factorial)
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* `B` [Fibonacci Number](src/algorithms/math/fibonacci)
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* `B` [Primality Test](src/algorithms/math/primality-test) (trial division method)
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* `B` [Euclidean Algorithm](src/algorithms/math/euclidean-algorithm) - calculate the Greatest Common Divisor (GCD)
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* `B` [Least Common Multiple](src/algorithms/math/least-common-multiple) (LCM)
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* `B` [Sieve of Eratosthenes](src/algorithms/math/sieve-of-eratosthenes) - finding all prime numbers up to any given limit
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* `B` [Is Power of Two](src/algorithms/math/is-power-of-two) - check if the number is power of two (naive and bitwise algorithms)
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* `B` [Pascal's Triangle](src/algorithms/math/pascal-triangle)
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* `B` [Complex Number](src/algorithms/math/complex-number) - complex numbers and basic operations with them
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* `B` [Radian & Degree](src/algorithms/math/radian) - radians to degree and backwards conversion
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* `B` [Fast Powering](src/algorithms/math/fast-powering)
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* `A` [Integer Partition](src/algorithms/math/integer-partition)
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* `A` [Liu Hui π Algorithm](src/algorithms/math/liu-hui) - approximate π calculations based on N-gons
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* `A` [Discrete Fourier Transform](src/algorithms/math/fourier-transform) - decompose a function of time (a signal) into the frequencies that make it up
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* **Sets**
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* `B` [Cartesian Product](src/algorithms/sets/cartesian-product) - product of multiple sets
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* `B` [Fisher–Yates Shuffle](src/algorithms/sets/fisher-yates) - random permutation of a finite sequence
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* `A` [Power Set](src/algorithms/sets/power-set) - all subsets of a set (bitwise and backtracking solutions)
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* `A` [Permutations](src/algorithms/sets/permutations) (with and without repetitions)
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* `A` [Combinations](src/algorithms/sets/combinations) (with and without repetitions)
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* `A` [Longest Common Subsequence](src/algorithms/sets/longest-common-subsequence) (LCS)
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* `A` [Longest Increasing Subsequence](src/algorithms/sets/longest-increasing-subsequence)
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* `A` [Shortest Common Supersequence](src/algorithms/sets/shortest-common-supersequence) (SCS)
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* `A` [Knapsack Problem](src/algorithms/sets/knapsack-problem) - "0/1" and "Unbound" ones
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* `A` [Maximum Subarray](src/algorithms/sets/maximum-subarray) - "Brute Force" and "Dynamic Programming" (Kadane's) versions
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* `A` [Combination Sum](src/algorithms/sets/combination-sum) - find all combinations that form specific sum
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* **Strings**
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* `B` [Hamming Distance](src/algorithms/string/hamming-distance) - number of positions at which the symbols are different
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* `A` [Levenshtein Distance](src/algorithms/string/levenshtein-distance) - minimum edit distance between two sequences
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* `A` [Knuth–Morris–Pratt Algorithm](src/algorithms/string/knuth-morris-pratt) (KMP Algorithm) - substring search (pattern matching)
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* `A` [Z Algorithm](src/algorithms/string/z-algorithm) - substring search (pattern matching)
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* `A` [Rabin Karp Algorithm](src/algorithms/string/rabin-karp) - substring search
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* `A` [Longest Common Substring](src/algorithms/string/longest-common-substring)
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* `A` [Regular Expression Matching](src/algorithms/string/regular-expression-matching)
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* **Searches**
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* `B` [Linear Search](src/algorithms/search/linear-search)
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* `B` [Jump Search](src/algorithms/search/jump-search) (or Block Search) - search in sorted array
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* `B` [Binary Search](src/algorithms/search/binary-search) - search in sorted array
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* `B` [Interpolation Search](src/algorithms/search/interpolation-search) - search in uniformly distributed sorted array
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* **Sorting**
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* `B` [Bubble Sort](src/algorithms/sorting/bubble-sort)
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* `B` [Selection Sort](src/algorithms/sorting/selection-sort)
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* `B` [Insertion Sort](src/algorithms/sorting/insertion-sort)
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* `B` [Heap Sort](src/algorithms/sorting/heap-sort)
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* `B` [Merge Sort](src/algorithms/sorting/merge-sort)
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* `B` [Quicksort](src/algorithms/sorting/quick-sort) - in-place and non-in-place implementations
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* `B` [Shellsort](src/algorithms/sorting/shell-sort)
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* `B` [Counting Sort](src/algorithms/sorting/counting-sort)
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* `B` [Radix Sort](src/algorithms/sorting/radix-sort)
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* **Trees**
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* `B` [Depth-First Search](src/algorithms/tree/depth-first-search) (DFS)
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* `B` [Breadth-First Search](src/algorithms/tree/breadth-first-search) (BFS)
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* **Graphs**
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* `B` [Depth-First Search](src/algorithms/graph/depth-first-search) (DFS)
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* `B` [Breadth-First Search](src/algorithms/graph/breadth-first-search) (BFS)
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* `B` [Kruskal’s Algorithm](src/algorithms/graph/kruskal) - finding Minimum Spanning Tree (MST) for weighted undirected graph
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* `A` [Dijkstra Algorithm](src/algorithms/graph/dijkstra) - finding shortest paths to all graph vertices from single vertex
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* `A` [Bellman-Ford Algorithm](src/algorithms/graph/bellman-ford) - finding shortest paths to all graph vertices from single vertex
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* `A` [Floyd-Warshall Algorithm](src/algorithms/graph/floyd-warshall) - find shortest paths between all pairs of vertices
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* `A` [Detect Cycle](src/algorithms/graph/detect-cycle) - for both directed and undirected graphs (DFS and Disjoint Set based versions)
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* `A` [Prim’s Algorithm](src/algorithms/graph/prim) - finding Minimum Spanning Tree (MST) for weighted undirected graph
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* `A` [Topological Sorting](src/algorithms/graph/topological-sorting) - DFS method
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* `A` [Articulation Points](src/algorithms/graph/articulation-points) - Tarjan's algorithm (DFS based)
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* `A` [Bridges](src/algorithms/graph/bridges) - DFS based algorithm
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* `A` [Eulerian Path and Eulerian Circuit](src/algorithms/graph/eulerian-path) - Fleury's algorithm - Visit every edge exactly once
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* `A` [Hamiltonian Cycle](src/algorithms/graph/hamiltonian-cycle) - Visit every vertex exactly once
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* `A` [Strongly Connected Components](src/algorithms/graph/strongly-connected-components) - Kosaraju's algorithm
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* `A` [Travelling Salesman Problem](src/algorithms/graph/travelling-salesman) - shortest possible route that visits each city and returns to the origin city
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* **Cryptography**
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* `B` [Polynomial Hash](src/algorithms/cryptography/polynomial-hash) - rolling hash function based on polynomial
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* **Uncategorized**
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* `B` [Tower of Hanoi](src/algorithms/uncategorized/hanoi-tower)
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* `B` [Square Matrix Rotation](src/algorithms/uncategorized/square-matrix-rotation) - in-place algorithm
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* `B` [Jump Game](src/algorithms/uncategorized/jump-game) - backtracking, dynamic programming (top-down + bottom-up) and greedy examples
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* `B` [Unique Paths](src/algorithms/uncategorized/unique-paths) - backtracking, dynamic programming and Pascal's Triangle based examples
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* `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem (dynamic programming and brute force versions)
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* `A` [N-Queens Problem](src/algorithms/uncategorized/n-queens)
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* `A` [Knight's Tour](src/algorithms/uncategorized/knight-tour)
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### Algorithms by Paradigm
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An algorithmic paradigm is a generic method or approach which underlies the design of a class
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of algorithms. It is an abstraction higher than the notion of an algorithm, just as an
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algorithm is an abstraction higher than a computer program.
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* **Brute Force** - look at all the possibilities and selects the best solution
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* `B` [Linear Search](src/algorithms/search/linear-search)
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* `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem
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* `A` [Maximum Subarray](src/algorithms/sets/maximum-subarray)
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* `A` [Travelling Salesman Problem](src/algorithms/graph/travelling-salesman) - shortest possible route that visits each city and returns to the origin city
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* `A` [Discrete Fourier Transform](src/algorithms/math/fourier-transform) - decompose a function of time (a signal) into the frequencies that make it up
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* **Greedy** - choose the best option at the current time, without any consideration for the future
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* `B` [Jump Game](src/algorithms/uncategorized/jump-game)
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* `A` [Unbound Knapsack Problem](src/algorithms/sets/knapsack-problem)
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* `A` [Dijkstra Algorithm](src/algorithms/graph/dijkstra) - finding shortest path to all graph vertices
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* `A` [Prim’s Algorithm](src/algorithms/graph/prim) - finding Minimum Spanning Tree (MST) for weighted undirected graph
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* `A` [Kruskal’s Algorithm](src/algorithms/graph/kruskal) - finding Minimum Spanning Tree (MST) for weighted undirected graph
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* **Divide and Conquer** - divide the problem into smaller parts and then solve those parts
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* `B` [Binary Search](src/algorithms/search/binary-search)
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* `B` [Tower of Hanoi](src/algorithms/uncategorized/hanoi-tower)
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* `B` [Pascal's Triangle](src/algorithms/math/pascal-triangle)
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* `B` [Euclidean Algorithm](src/algorithms/math/euclidean-algorithm) - calculate the Greatest Common Divisor (GCD)
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* `B` [Merge Sort](src/algorithms/sorting/merge-sort)
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* `B` [Quicksort](src/algorithms/sorting/quick-sort)
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* `B` [Tree Depth-First Search](src/algorithms/tree/depth-first-search) (DFS)
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* `B` [Graph Depth-First Search](src/algorithms/graph/depth-first-search) (DFS)
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* `B` [Jump Game](src/algorithms/uncategorized/jump-game)
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* `B` [Fast Powering](src/algorithms/math/fast-powering)
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* `A` [Permutations](src/algorithms/sets/permutations) (with and without repetitions)
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* `A` [Combinations](src/algorithms/sets/combinations) (with and without repetitions)
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* **Dynamic Programming** - build up a solution using previously found sub-solutions
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* `B` [Fibonacci Number](src/algorithms/math/fibonacci)
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* `B` [Jump Game](src/algorithms/uncategorized/jump-game)
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* `B` [Unique Paths](src/algorithms/uncategorized/unique-paths)
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* `B` [Rain Terraces](src/algorithms/uncategorized/rain-terraces) - trapping rain water problem
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* `A` [Levenshtein Distance](src/algorithms/string/levenshtein-distance) - minimum edit distance between two sequences
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* `A` [Longest Common Subsequence](src/algorithms/sets/longest-common-subsequence) (LCS)
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* `A` [Longest Common Substring](src/algorithms/string/longest-common-substring)
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* `A` [Longest Increasing Subsequence](src/algorithms/sets/longest-increasing-subsequence)
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* `A` [Shortest Common Supersequence](src/algorithms/sets/shortest-common-supersequence)
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* `A` [0/1 Knapsack Problem](src/algorithms/sets/knapsack-problem)
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* `A` [Integer Partition](src/algorithms/math/integer-partition)
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* `A` [Maximum Subarray](src/algorithms/sets/maximum-subarray)
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* `A` [Bellman-Ford Algorithm](src/algorithms/graph/bellman-ford) - finding shortest path to all graph vertices
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* `A` [Floyd-Warshall Algorithm](src/algorithms/graph/floyd-warshall) - find shortest paths between all pairs of vertices
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* `A` [Regular Expression Matching](src/algorithms/string/regular-expression-matching)
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* **Backtracking** - similarly to brute force, try to generate all possible solutions, but each time you generate next solution you test
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if it satisfies all conditions, and only then continue generating subsequent solutions. Otherwise, backtrack, and go on a
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different path of finding a solution. Normally the DFS traversal of state-space is being used.
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* `B` [Jump Game](src/algorithms/uncategorized/jump-game)
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* `B` [Unique Paths](src/algorithms/uncategorized/unique-paths)
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* `B` [Power Set](src/algorithms/sets/power-set) - all subsets of a set
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* `A` [Hamiltonian Cycle](src/algorithms/graph/hamiltonian-cycle) - Visit every vertex exactly once
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* `A` [N-Queens Problem](src/algorithms/uncategorized/n-queens)
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* `A` [Knight's Tour](src/algorithms/uncategorized/knight-tour)
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* `A` [Combination Sum](src/algorithms/sets/combination-sum) - find all combinations that form specific sum
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* **Branch & Bound** - remember the lowest-cost solution found at each stage of the backtracking
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search, and use the cost of the lowest-cost solution found so far as a lower bound on the cost of
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a least-cost solution to the problem, in order to discard partial solutions with costs larger than the
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lowest-cost solution found so far. Normally BFS traversal in combination with DFS traversal of state-space
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tree is being used.
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## How to use this repository
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**Install all dependencies**
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```
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npm install
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```
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**Run ESLint**
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You may want to run it to check code quality.
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```
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npm run lint
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```
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**Run all tests**
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```
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npm test
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```
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**Run tests by name**
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```
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npm test -- 'LinkedList'
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```
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**Playground**
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You may play with data-structures and algorithms in `./src/playground/playground.js` file and write
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tests for it in `./src/playground/__test__/playground.test.js`.
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Then just simply run the following command to test if your playground code works as expected:
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```
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npm test -- 'playground'
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```
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## Useful Information
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### References
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[▶ Data Structures and Algorithms on YouTube](https://www.youtube.com/playlist?list=PLLXdhg_r2hKA7DPDsunoDZ-Z769jWn4R8)
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### Big O Notation
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*Big O notation* is used to classify algorithms according to how their running time or space requirements grow as the input size grows.
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On the chart below you may find most common orders of growth of algorithms specified in Big O notation.
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Source: [Big O Cheat Sheet](http://bigocheatsheet.com/).
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Below is the list of some of the most used Big O notations and their performance comparisons against different sizes of the input data.
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| Big O Notation | Computations for 10 elements | Computations for 100 elements | Computations for 1000 elements |
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| -------------- | ---------------------------- | ----------------------------- | ------------------------------- |
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| **O(1)** | 1 | 1 | 1 |
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| **O(log N)** | 3 | 6 | 9 |
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| **O(N)** | 10 | 100 | 1000 |
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| **O(N log N)** | 30 | 600 | 9000 |
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| **O(N^2)** | 100 | 10000 | 1000000 |
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| **O(2^N)** | 1024 | 1.26e+29 | 1.07e+301 |
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| **O(N!)** | 3628800 | 9.3e+157 | 4.02e+2567 |
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### Data Structure Operations Complexity
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| Data Structure | Access | Search | Insertion | Deletion | Comments |
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| ----------------------- | :-------: | :-------: | :-------: | :-------: | :-------- |
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| **Array** | 1 | n | n | n | |
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| **Stack** | n | n | 1 | 1 | |
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| **Queue** | n | n | 1 | 1 | |
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| **Linked List** | n | n | 1 | 1 | |
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| **Hash Table** | - | n | n | n | In case of perfect hash function costs would be O(1) |
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| **Binary Search Tree** | n | n | n | n | In case of balanced tree costs would be O(log(n)) |
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| **B-Tree** | log(n) | log(n) | log(n) | log(n) | |
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| **Red-Black Tree** | log(n) | log(n) | log(n) | log(n) | |
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| **AVL Tree** | log(n) | log(n) | log(n) | log(n) | |
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| **Bloom Filter** | - | 1 | 1 | - | False positives are possible while searching |
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### Array Sorting Algorithms Complexity
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| Name | Best | Average | Worst | Memory | Stable | Comments |
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| --------------------- | :-------------: | :-----------------: | :-----------------: | :-------: | :-------: | :-------- |
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| **Bubble sort** | n | n<sup>2</sup> | n<sup>2</sup> | 1 | Yes | |
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| **Insertion sort** | n | n<sup>2</sup> | n<sup>2</sup> | 1 | Yes | |
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| **Selection sort** | n<sup>2</sup> | n<sup>2</sup> | n<sup>2</sup> | 1 | No | |
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| **Heap sort** | n log(n) | n log(n) | n log(n) | 1 | No | |
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| **Merge sort** | n log(n) | n log(n) | n log(n) | n | Yes | |
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| **Quick sort** | n log(n) | n log(n) | n<sup>2</sup> | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space |
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| **Shell sort** | n log(n) | depends on gap sequence | n (log(n))<sup>2</sup> | 1 | No | |
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| **Counting sort** | n + r | n + r | n + r | n + r | Yes | r - biggest number in array |
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| **Radix sort** | n * k | n * k | n * k | n + k | Yes | k - length of longest key |
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## The Book
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We’re writing a book that will clearly explain, in detail, the main algorithms.
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If you’d like to be notified when the “JavaScript Algorithms” book
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launches, [click here](https://upscri.be/402324/).
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