TheAlgorithms/C

Collection of various algorithms in mathematics, machine learning, computer science, physics, etc implemented in C for educational purposes.

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TheAlgorithms/C has 22k+ stars and 4.5k+ forks on GitHub, is written in C, and was last pushed on Aug 7, 2024. Data as of . Signed-in members get hourly updates — create a free account.

Summary Information

Updated 12 minutes ago
Type:Curated List / Learning ResourceCategory(s):Interview Prep & AlgorithmsLearning & Resources
Added to GitGenius on October 12th, 2026
Created on July 16th, 2016
Open Issues & Pull Requests: 25 (+0)
GitHub issues: Enabled
Number of forks: 4,779
Total Stargazers: 22,551 (+0)
Total Subscribers: 524 (+0)

Repository Insights (GitGenius)

Median issue/PR response: 3.2 days
Mean response time: 7.1 days
90th percentile: 20.9 days
Tracked items: 18

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Issue Activity (beta)

Open issues: 3
New in 7 days: 0
Closed in 7 days: 0
Avg open age: 1,012 days
Stale 30+ days: 0
Stale 90+ days: 0

Recent activity

Opened in 7 days: 0
Closed in 7 days: 0
Comments in 7 days: 2
Events in 7 days: 3

Top labels

  • Stale (22)
  • enhancement (12)
  • bug (7)
  • dont-close (2)

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Detailed Description

The Algorithms is a collection of algorithm implementations in C covering mathematics, machine learning, computer science, physics, and related domains, designed for educational purposes.

The project addresses the need for accessible, well-organized algorithm references by gathering implementations across multiple domains into a single repository. Rather than focusing on a single specialized area, it takes a broad approach by curating algorithms from diverse fields and presenting them in C, making the underlying logic visible through a lower-level language that exposes computational details often abstracted away in higher-level implementations.

The collection suits students learning algorithms, people preparing for technical interviews, and developers seeking to understand how standard algorithms work at the implementation level. It works best as a reference and learning resource rather than as a production library. The breadth across mathematics, machine learning, data structures, and search algorithms means someone can explore multiple domains within one repository, though any given algorithm may be one of many possible implementations rather than the most optimized version.

The project maintains active community engagement through structured contribution processes and welcomes participation in algorithm challenges and competitions. Development shows consistent activity with regular additions across multiple algorithm categories, indicating sustained interest in expanding coverage. The codebase reflects educational intent, with implementations prioritized for clarity and learning value over production performance characteristics.