github.com-trekhleb-javascript-algorithms_-_2019-11-30_03-23-52

github.com-trekhleb-javascript-algorithms_-_2019-11-30_03-23-52

Posted by

Download
ITEM TILE – File Size: 8.6K

📝 Algorithms and data structures implemented in JavaScript with explanations and links to further readings

JavaScript Algorithms and Data Structures

Build Statuscodecov

This repository contains JavaScript based examples of manypopular algorithms and data structures.

Each algorithm and data structure has its own separate READMEwith related explanations and links for further reading (including onesto YouTube videos).

Read this in other languages:简体中文,繁體中文,한국어,日本語,Polski,Français,Español,Português

☝ 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.

Data Structures

A data structure is a particular way of organizing and storing data in a computer so that it canbe accessed and modified efficiently. More precisely, a data structure is a collection of datavalues, the relationships among them, and the functions or operations that can be applied tothe data.

B – Beginner, A – Advanced

Algorithms

An algorithm is an unambiguous specification of how to solve a class of problems. It isa set of rules that precisely define a sequence of operations.

B – Beginner, A – Advanced

Algorithms by Topic

Algorithms by Paradigm

An algorithmic paradigm is a generic method or approach which underlies the design of a classof algorithms. It is an abstraction higher than the notion of an algorithm, just as analgorithm is an abstraction higher than a computer program.

How to use this repository

Install all dependenciesnpm install

Run ESLint

You may want to run it to check code quality.

npm run lint

Run all testsnpm test

Run tests by namenpm test -- 'LinkedList'

Playground

You may play with data-structures and algorithms in ./src/playground/playground.js file and writetests 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 -- 'playground'

Useful Information

References

▶ 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.

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 | 600 | 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 | Comments || ———————– | :——-: | :——-: | :——-: | :——-: | :——– || Array | 1 | n | n | n | || Stack | n | n | 1 | 1 | || Queue | n | n | 1 | 1 | || Linked List | n | n | 1 | n | || Hash Table | – | n | n | n | In case of perfect hash function costs would be O(1) || Binary Search Tree | n | n | n | n | In case of balanced tree costs would be O(log(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) | || Bloom Filter | – | 1 | 1 | – | False positives are possible while searching |

Array Sorting Algorithms Complexity

| Name | Best | Average | Worst | Memory | Stable | Comments || ——————— | :————-: | :—————–: | :—————–: | :——-: | :——-: | :——– || Bubble sort | n | n2 | n2 | 1 | Yes | || Insertion sort | n | n2 | n2 | 1 | Yes | || Selection sort | n2 | n2 | n2 | 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) | n2 | log(n) | No | Quicksort is usually done in-place with O(log(n)) stack space || Shell sort | n log(n) | depends on gap sequence | n (log(n))2 | 1 | No | || Counting sort | n + r | n + r | n + r | n + r | Yes | r – biggest number in array || Radix sort | n * k | n * k | n * k | n + k | Yes | k – length of longest key |

To restore the repository download the bundle

wget https://archive.org/download/github.com-trekhleb-javascript-algorithms_-_2019-11-30_03-23-52/trekhleb-javascript-algorithms_-_2019-11-30_03-23-52.bundle

and run:

 git clone trekhleb-javascript-algorithms_-_2019-11-30_03-23-52.bundle 

Source: https://github.com/trekhleb/javascript-algorithms
Uploader: trekhleb
Upload date: 2019-11-30