# Algorithms

A curated list of awesome places to learn and/or practice algorithms. Inspired by [awesome-awesomeness](https://github.com/bayandin/awesome-awesomeness) and all the other awesome Awesome libraries.

If you want to contribute, please read the [contribution guidelines](https://github.com/tayllan/awesome-algorithms/blob/master/CONTRIBUTING.md).

* [Awesome Algorithms](https://github.com/tayllan/awesome-algorithms#awesome-algorithms)
  * [Websites](https://github.com/tayllan/awesome-algorithms#websites)
  * [Online Courses](https://github.com/tayllan/awesome-algorithms#online-courses)
  * [Books](https://github.com/tayllan/awesome-algorithms#books)
  * [Github Libraries](https://github.com/tayllan/awesome-algorithms#github-libraries)
  * [Online Judges](https://github.com/tayllan/awesome-algorithms#online-judges)
  * [Tools](https://github.com/tayllan/awesome-algorithms#tools)

### Websites

*Websites you should use to learn classic algorithms*

* [A Visual Guide to Graph Traversal Algorithms](https://workshape.github.io/visual-graph-algorithms/) - Interactive visualisations for learning how graph traversal algorithms work
* [Algomation](http://www.algomation.com/) - A didactic, animated, exposition of algorithms.
* [Algorithm Visualizer](http://algo-visualizer.jasonpark.me/) - Dozens of animated algorithms (with code), and you can also create your own.
* [Algorithms Visualization](http://bost.ocks.org/mike/algorithms/) - A dense article on Algorithms Visualization.
* [Big-O Cheat Sheet](http://bigocheatsheet.com/) - Big-O complexities of common algorithms used in Computer Science.
* [Code-Drills](https://code-drills.com/tools/comparator) - Practice problems recommender (includes Codeforces, Codechef and Spoj).
* [Data Structure Visualizations](http://www.cs.usfca.edu/~galles/visualization/Algorithms.html) - Visualize the behavior of Data Structures and play with its operations.
* [Geeks for Geeks](http://www.geeksforgeeks.org/fundamentals-of-algorithms/) - Lots and lots of well explained and implemented algorithms.
* [Path Finding](https://qiao.github.io/PathFinding.js/visual/) - A visual representation on how algorithms such as A\*, IDA\*, Breadth-First-Search, Best-First-Search and others describe a path between two points A and B.
* [Rosetta Code](http://rosettacode.org/wiki/Rosetta_Code) - A programming chrestomathy site which aims to present implementations of many algorithms and data structures in different programming languages.
* [Sorting Algorithms](http://www.sorting-algorithms.com/) - Nice and simple animations of sorting algorithms. With short codes and discussions.
* [Stoimen's web log](http://www.stoimen.com/blog/) - Some algorithms nicely explained.
* [The Sound of Sorting](http://panthema.net/2013/sound-of-sorting/) - The Sound of Sorting - "Audibilization" and Visualization of Sorting Algorithms
* [VisuAlgo](http://visualgo.net/) - Visualising data structures and algorithms through animation.
* [Wikipedia - Algorithms](https://en.wikipedia.org/wiki/List_of_algorithms) - Of course!!
* [Wikipedia - Data Structures](https://en.wikipedia.org/wiki/List_of_data_structures) - and why not ?!!

### Online Courses

*Free and High Quality Courses Online*

* [Algorithms: Divide and Conquer, Sorting and Searching, and Randomized Algorithms](https://www.coursera.org/learn/algorithms-divide-conquer) - The primary topics are: asymptotic ("Big-oh") notation, sorting and searching, divide and conquer, and randomized algorithms.
* [Algorithms: Graph Search, Shortest Paths, and Data Structures](https://www.coursera.org/learn/algorithms-graphs-data-structures) - The primary topics are: data structures, graph primitives, and their applications.
* [Algorithms: Greedy Algorithms, Minimum Spanning Trees, and Dynamic Programming](https://www.coursera.org/learn/algorithms-greedy) - The primary topics are: greedy algorithms and dynamic programming.
* [Algorithms: Shortest Paths Revisited, NP-Complete Problems and What To Do About Them](https://www.coursera.org/learn/algorithms-npcomplete) - The primary topics are: shortest paths, NP-completeness and what it means for the algorithm designer, and strategies for coping with computationally intractable problems.
* [Algorithms, Part I](https://www.coursera.org/learn/algorithms-part1/home/welcome) - This course covers the essential information that every serious programmer needs to know about algorithms and data structures.Part I covers elementary data structures, sorting, and searching algorithms.
* [Algorithms, Part II](https://www.coursera.org/learn/algorithms-part2) - Part II focuses on graph- and string-processing algorithms.
* [Khan Academy Algorithms](https://www.khanacademy.org/computing/computer-science/algorithms) - Algorithm course ministred by Tomas Cormen and Devin Balkcom.
* [MIT - 6-006](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/lecture-videos/) - Well explained algorithms.
* [MIT - 6-046j](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-introduction-to-algorithms-sma-5503-fall-2005/video-lectures/) - Similar to the previous one, but with different algorithms.
* [MIT - 6-00sc](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-00sc-introduction-to-computer-science-and-programming-spring-2011/index.htm) - An easy and well explained introduction to algorithms.
* [Udacity Intro to Algorithms](https://www.udacity.com/course/intro-to-algorithms--cs215) - Python-based Algorithms course.
* [Algorithms in Motion](https://www.manning.com/livevideo/algorithms-in-motion) - Beginner's algorithms course with fun illustrations, based on the book Grokking Algorithms
* [~~YogiBearian YouTube Channel~~](https://www.youtube.com/channel/UCv3Kd0guxD5KWQtP---9D6g) ~~- Lots of well explained vidoes on various computer science subjects.~~ *Account terminated due to violations of Youtube Policies.*

### Books

*The most highly regarded books to learn algorithms*

* [Algorithm Design](https://www.pearsonhighered.com/program/Kleinberg-Algorithm-Design/PGM319216.html) - Pretty straight-forward.
* [Algorithms](http://algs4.cs.princeton.edu/home/) - Problems explained with Java, OO good practices, visualizations, and free online resources.
* [Data Structures Using C](http://www.amazon.com/Data-Structures-Using-Aaron-Tenenbaum/dp/0131997467) - The basic concepts and usages of data structures.
* [Elementary Algorithms](https://github.com/liuxinyu95/AlgoXY) - An awesome book about algorithms and data structures.
* [Grokking Algorithms](http://www.manning.com/bhargava) - An illustrated book on algorithms with practical examples.
* [Introduction to Algorithms](http://mitpress.mit.edu/books/introduction-algorithms) - Essential!
* [Swift Algorithms & Data Structures](http://shop.waynewbishop.com/) - A practical guide to concepts, theory and code.
* [The Algorithm Design Manual](http://www.algorist.com/) - Easy to read and full of real world examples.
* [The Art of Computer Programming](http://www-cs-faculty.stanford.edu/~uno/taocp.html) - The Book.

### Github Libraries

*Implementations of the most classic algorithms in a wide variety of programming languages*

* [C](https://github.com/fragglet/c-algorithms)
* [CoffeeScript](https://github.com/BrunoRB/algorithms.coffee)
* C#
  * [by @shkolovy](https://github.com/shkolovy/classic-algorithms)
  * [by @aalhour](https://github.com/aalhour/C-Sharp-Algorithms)
  * [by @justcoding121](https://github.com/justcoding121/Advanced-Algorithms)
* C++
  * [by @xtaci](https://github.com/xtaci/algorithms)
  * [by @PetarV-](https://github.com/PetarV-/Algorithms)
  * [by @faheel](https://github.com/faheel/Algos)
* [Erlang](https://github.com/aggelgian/erlang-algorithms)
* [Go](https://github.com/arnauddri/algorithms)
* Java
  * [by @jpa99](https://github.com/jpa99/Algorithms)
  * [by @phishman3579](https://github.com/phishman3579/java-algorithms-implementation)
  * [by @asmolich](https://github.com/asmolich/algorithms)
  * [by @psjava](https://github.com/psjava/psjava)
  * [by @jeandersonbc](https://github.com/jeandersonbc/algorithms-and-ds)
  * [by @pedrovgs](https://github.com/pedrovgs/Algorithms)
  * [by @Erdos-Graph-Framework](https://github.com/Erdos-Graph-Framework/Erdos)
  * [by @deepak-malik](https://github.com/deepak-malik/Data-Structures-In-Java)
  * [by @yusufcakal](https://github.com/yusufcakal/algorithms)
* JavaScript
  * [by @jiayihu](https://github.com/jiayihu/pretty-algorithms)
  * [by @felipernb](https://github.com/felipernb/algorithms.js)
  * [by @nzakas](https://github.com/nzakas/computer-science-in-javascript)
  * [by @duereg](https://github.com/duereg/js-algorithms)
  * [by @mgechev](https://github.com/mgechev/javascript-algorithms)
  * [by @trekhleb](https://github.com/trekhleb/javascript-algorithms)
  * [by @ManrajGrover](https://github.com/ManrajGrover/algorithms-js)
* Objective-C
  * [by @ EvgenyKarkan](https://github.com/EvgenyKarkan/EKAlgorithms)
* Python
  * [by @nryoung](https://github.com/nryoung/algorithms)
  * [by @prakhar1989](https://github.com/prakhar1989/Algorithms)
  * [by @laurentluce](https://github.com/laurentluce/python-algorithms)
  * [by @nbro](https://github.com/nbro/ands)
  * [by @keon](https://github.com/keon/algorithms)
* Ruby
  * [by @kanwei](https://github.com/kanwei/algorithms)
  * [by @sagivo](https://github.com/sagivo/algorithms)
  * [by @kumar91gopi](https://github.com/kumar91gopi/Algorithms-and-Data-Structures-in-Ruby)
* [Scala](https://github.com/vkostyukov/scalacaster)
* Swift
  * [by @kingreza](https://github.com/kingreza/Swift-Algorithms-Strings-)
  * [by @waynewbishop](https://github.com/waynewbishop/SwiftStructures)
  * [by @hollance](https://github.com/hollance/swift-algorithm-club)
* Language agnostic
  * [by @kennyledet](https://github.com/kennyledet/Algorithm-Implementations)
  * [by @indy256](https://github.com/indy256/codelibrary)
  * [by @sagivo](https://github.com/sagivo/algorithms)
  * [by @patmorin](https://github.com/patmorin/ods)

### Online Judges

*Online Judges to practice what you learned above*

* [A2 Online Judge](https://a2oj.com/) - Online Judge and problem archive.
* [ACM-ICPC Live Archive](https://icpcarchive.ecs.baylor.edu/) - Hundreds of problems from previous ACM-ICPC Regionals and World Finals.
* [AIZU ONLINE JUDGE](http://judge.u-aizu.ac.jp/onlinejudge/) - Japanese Online Judge.
* [Algo Muse](http://www.algomuse.appspot.com/) - Research based algorithmic problems.
* [Baekjoon Online Judge](https://www.acmicpc.net/) - Korean Online Judge. 10000+ problems. Supports 60+ languages.
* [CS Academy](https://csacademy.com/) - Holds online contests and IOI practice contests
* [CodeChef](https://www.codechef.com/) - More problems and monthly online contests.
* [Codeforces ](http://codeforces.com/)- The only programming contests Web 2.0 platform
* [Codefights](https://codefights.com/) - Practive programming and tackle out your next tech interview
* [CodeMarshal](https://algo.codemarshal.org/) - Real world contests online!
* [CodeWars](http://www.codewars.com/) - A website that houses support to solve algorithms in many languages in varying difficulty.
* [CoderByte](http://www.coderbyte.com/) - A decent website with algorithm challenges from beginner to advanced levels. Supports most of the popular languages like C++, python, javascript, ruby.
* [HackerEarth ](https://www.hackerearth.com/)- Practice alogrithmic problems & challenges and participate in hiring challenges.
* [HackerRank](https://www.hackerrank.com/) - Featured algorithm and functional programming online judges
* [Infoarena](http://www.infoarena.ro/) - Romanian Online Judge. 1500+ algorithmic problems
* [Kattis](https://open.kattis.com/)- Online judge and problem archive
* [LavidaOnlineJudge](http://judge.lavida.us/) - Korean Online Judge(Half English). 1300+ problems.
* [Learneroo Algorithms Tutorials](https://www.learneroo.com/subjects/8) - Learn and practice algorithms by solving challenges online.
* [LeetCode](https://leetcode.com/) - Learn algorithms and prepare for interviews.
* [PKU JudgeOnline](http://poj.org/) - Chinese Online Judge.
* [ProjectEuler](https://projecteuler.net/) - Mathematical problems that can be solved using algorithms (or just a pencil, depends on how much you already know).
* [Rosalind](http://rosalind.info/problems/locations/) - A platform for learning bioinformatics and programming through problem solving.
* [ShareCode.io ](https://sharecode.io/)- Online Judge and contest host with a lot of algorithmic problems in the archive to practice.
* [Snakify](https://snakify.org/) - An introductory Python course with 100+ algorithmic problems and a step-by-step debugger (from Russia).
* [SPOJ](http://www.spoj.com/) - More problems.
* [TopCoder](https://www.topcoder.com/) - Lots of problems and real world/money worthy problems in Graphic Design, Data Science and Development.
* [URI](https://www.urionlinejudge.com.br/judge/login) - Brazilian Online Judge. Not so much problems, but it's growing and it has online contests.
* [UVA](https://uva.onlinejudge.org/) - Hundreds of problems (from previous ACM-ICPC Regionals, World Finals and others).

### Tools

*Some tools that can help you in the learning of algorithms*

* [interactive-coding-challenges](https://github.com/donnemartin/interactive-coding-challenges) - Interactive, test-driven coding challenges (algorithms and data structures).


---

# Agent Instructions: Querying This Documentation

If you need additional information that is not directly available in this page, you can query the documentation dynamically by asking a question.

Perform an HTTP GET request on the current page URL with the `ask` query parameter:

```
GET https://stephanosterburg.gitbook.io/scrapbook/math/algorithms.md?ask=<question>
```

The question should be specific, self-contained, and written in natural language.
The response will contain a direct answer to the question and relevant excerpts and sources from the documentation.

Use this mechanism when the answer is not explicitly present in the current page, you need clarification or additional context, or you want to retrieve related documentation sections.