Open Source Linux Algorithms
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Algorithms for Linux
View 1273 business solutionsBrowse free open source Algorithms and projects for Linux below. Use the toggles on the left to filter open source Algorithms by OS, license, language, programming language, and project status.
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Technical analysis library with indicators like ADX, MACD, RSI, Stochastic, TRIX... includes also candlestick pattern recognition. Useful for trading application developpers using either Excel, .NET, Mono, Java, Perl or C/C++.
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Clipper
Polygon and line clipping and offsetting library (C++, C#, Delphi)
This library is now obsolete and no longer being maintained. It has been superceded by my Clipper2 library - https://github.com/AngusJohnson/Clipper2. -
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lpsolve
Mixed Integer Linear Programming (MILP) solver.
Mixed Integer Linear Programming (MILP) solver lp_solve solves pure linear, (mixed) integer/binary, semi-cont and special ordered sets (SOS) models.lp_solve is written in ANSI C and can be compiled on many different platforms like Linux and WINDOWS This project is moved to github: https://lp-solve.github.io/ -
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Real-ESRGAN
Real-ESRGAN aims at developing Practical Algorithms
Real-ESRGAN is a highly popular open-source project that provides practical algorithms for general image and video restoration using deep learning-based super-resolution techniques. It extends the original Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) approach by training on synthetic degradations to make results more robust on real-world images, effectively enhancing resolution, reducing noise/artifacts, and reconstructing fine detail in low-quality imagery. The repository includes inference and training scripts, a model zoo with different pretrained models (including general and anime-oriented variants), and support for batch and arbitrary scaling, making it adaptable for diverse enhancement tasks. It emphasizes usability with utilities that handle alpha channels, gray/16-bit images, and tiled inference for large inputs, and can be run via Python scripts or portable executables. -
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RHash (Recursive Hasher) is a console utility for computing and verifying hash sums of files. It supports CRC32, CRC32C, MD4, MD5, SHA1, SHA256, SHA512, SHA3, AICH, ED2K, DC++ TTH, BTIH, Tiger, GOST R 34.11-2012, RIPEMD-160, HAS-160, EDON-R, and Whirlpool.
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GNSS-SDR
An open source software-defined GNSS receiver
An open source software-defined Global Navigation Satellite Systems (GNSS) receiver written in C++ and based on the GNU Radio framework. -
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Real-ESRGAN ncnn Vulkan
NCNN implementation of Real-ESRGAN
Real-ESRGAN ncnn Vulkan is an optimized, cross-platform implementation of Real-ESRGAN using the ncnn neural network inference engine and Vulkan for hardware acceleration. Unlike the standard PyTorch-based Real-ESRGAN code, this variant is written in C/C++ and designed to run efficiently on many platforms (including Windows, Linux, and possibly Android) without requiring heavy frameworks like CUDA or Python. It provides command-line tools for upscaling images with selected models, allowing users to specify input/output paths, scaling factors, tile sizes, and model names from a compressed model set, which is particularly helpful for larger images or automated workflows. The Vulkan backend enables fast execution on GPUs from different vendors (Intel/AMD/Nvidia) with broad support, making it suitable for non-Python environments, production systems, or performance-constrained setups. -
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Armadillo
fast C++ library for linear algebra & scientific computing
* Fast C++ library for linear algebra (matrix maths) and scientific computing * Easy to use functions and syntax, deliberately similar to Matlab / Octave * Uses template meta-programming techniques to increase efficiency * Provides user-friendly wrappers for OpenBLAS, Intel MKL, LAPACK, ATLAS, ARPACK, SuperLU and FFTW libraries * Useful for machine learning, pattern recognition, signal processing, bioinformatics, statistics, finance, etc. * Downloads: http://arma.sourceforge.net/download.html * Documentation: http://arma.sourceforge.net/docs.html * Bug reports: http://arma.sourceforge.net/faq.html * Git repo: https://gitlab.com/conradsnicta/armadillo-code -
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Zstandard
Zstandard - Fast real-time compression algorithm
Zstandard is a fast compression algorithm, providing high compression ratios. It also offers a special mode for small data, called dictionary compression. The reference library offers a very wide range of speed / compression trade-off, and is backed by an extremely fast decoder (see benchmarks below). Zstandard library is provided as open source software using a BSD license. Its format is stable and published as IETF RFC 8478. The negative compression levels, specified with --fast=#, offer faster compression and decompression speed in exchange for some loss in compression ratio compared to level 1, as seen in the table above. Zstd can trade compression speed for stronger compression ratios. It is configurable by small increment. Decompression speed is preserved and remain roughly the same at all settings, a property shared by most LZ compression algorithms, such as zlib or lzma. -
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Arduino
Open-source electronics platform
Arduino is an open-source physical computing platform based on a simple I/O board and a development environment that implements the Processing/Wiring language. Arduino can be used to develop stand-alone interactive objects or can be connected to software on your computer (e.g. Flash, Processing and MaxMSP). The boards can be assembled by hand or purchased preassembled. Arduino is a popular tool for IoT product development as well as one of the most successful tools for STEM/STEAM education. Hundreds of thousands of designers, engineers, students, developers and makers around the world are using Arduino to innovate in music, games, toys, smart homes, farming, autonomous vehicles, and more. Arduino is the first widespread Open Source Hardware project and was set up to build a community that could help spread the use of the tool and benefit from contributions from hundreds of people who helped debug the code, write examples, create tutorials, etc. -
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GFPGAN
GFPGAN aims at developing Practical Algorithms
GFPGAN aims at developing Practical Algorithms for Real-world Face Restoration. Colab Demo for GFPGAN; (Another Colab Demo for the original paper model) Online demo: Huggingface (return only the cropped face) Online demo: Replicate.ai (may need to sign in, return the whole image). Online demo: Baseten.co (backed by GPU, returns the whole image). We provide a clean version of GFPGAN, which can run without CUDA extensions. So that it can run in Windows or on CPU mode. GFPGAN aims at developing a Practical Algorithm for Real-world Face Restoration. It leverages rich and diverse priors encapsulated in a pretrained face GAN (e.g., StyleGAN2) for blind face restoration. Add V1.3 model, which produces more natural restoration results, and better results on very low-quality / high-quality inputs. -
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libmng -THE reference library for reading, displaying, writing and examining Multiple-Image Network Graphics. MNG is the animation extension to the popular PNG image-format.
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AlphaZero.jl
A generic, simple and fast implementation of Deepmind's AlphaZero
Beyond its much publicized success in attaining superhuman level at games such as Chess and Go, DeepMind's AlphaZero algorithm illustrates a more general methodology of combining learning and search to explore large combinatorial spaces effectively. We believe that this methodology can have exciting applications in many different research areas. Because AlphaZero is resource-hungry, successful open-source implementations (such as Leela Zero) are written in low-level languages (such as C++) and optimized for highly distributed computing environments. This makes them hardly accessible for students, researchers and hackers. Many simple Python implementations can be found on Github, but none of them is able to beat a reasonable baseline on games such as Othello or Connect Four. As an illustration, the benchmark in the README of the most popular of them only features a random baseline, along with a greedy baseline that does not appear to be significantly stronger. -
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ImageAI
A python library built to empower developers
ImageAI is an easy-to-use Computer Vision Python library that empowers developers to easily integrate state-of-the-art Artificial Intelligence features into their new and existing applications and systems. It is used by thousands of developers, students, researchers, tutors and experts in corporate organizations around the world. You will find features supported, links to official documentation as well as articles on ImageAI. ImageAI is widely used around the world by professionals, students, research groups and businesses. ImageAI provides API to recognize 1000 different objects in a picture using pre-trained models that were trained on the ImageNet-1000 dataset. The model implementations provided are SqueezeNet, ResNet, InceptionV3 and DenseNet. ImageAI provides API to detect, locate and identify 80 most common objects in everyday life in a picture using pre-trained models that were trained on the COCO Dataset. -
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Dlib is a C++ toolkit containing machine learning algorithms and tools for creating complex software in C++ to solve real world problems.
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Anime4K
Anime4K is an open-source, high-quality anime upscaling algorithm
SISR algorithm designed to work with Japanese animation and cartoons to generate high-resolution images from a low-resolution input. -
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WOFF2
This document documents how to run the compression reference code
woff2 is Google’s reference implementation of the WOFF2 webfont format, the modern, highly compressed container used by browsers to ship OpenType/TrueType fonts efficiently over the network. It integrates specialized transforms for font tables (like glyf/loca and variations data) with Brotli compression to squeeze out as many bytes as possible while preserving exact font fidelity on decode. The repository includes a compact C/C++ library and small command-line tools so you can convert existing TTF/OTF files to WOFF2 and back for testing or build pipelines. Its encoder applies deterministic, spec-compliant transformations that maximize compressibility without altering rendering results, making it safe for production web delivery. The decoder is just as strict, validating headers and table checksums to guard against malformed inputs. Because WOFF2 is now ubiquitous across browsers and CDNs, this repo often serves as the canonical baseline for tooling. -
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threadpool is a cross-platform C++ thread pool library. It provides a convenient way for dispatching asynchronous tasks and can be easily customized. threadpool is based on the high-quality Boost source libraries.
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CRC RevEng
Arbitrary-precision CRC calculator and algorithm finder
CRC RevEng is a portable, arbitrary-precision CRC calculator and algorithm finder. It calculates CRCs using any of the 113 preset algorithms, or a user-specified algorithm to any width. It calculates reversed CRCs to give the bit pattern that produces a desired forward CRC. CRC RevEng also reverse-engineers any CRC algorithm from sufficient correctly formatted message-CRC pairs and optional known parameters. It comprises powerful input interpretation options. Compliant with Ross Williams' Rocksoft(tm) model of parametrised CRC algorithms. -
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Thrust
The C++ parallel algorithms library
Thrust is the C++ parallel algorithms library which inspired the introduction of parallel algorithms to the C++ Standard Library. Thrust's high-level interface greatly enhances programmer productivity while enabling performance portability between GPUs and multicore CPUs. It builds on top of established parallel programming frameworks (such as CUDA, TBB, and OpenMP). It also provides a number of general-purpose facilities similar to those found in the C++ Standard Library. The NVIDIA C++ Standard Library is an open-source project; it is available on GitHub and included in the NVIDIA HPC SDK and CUDA Toolkit. If you have one of those SDKs installed, no additional installation or compiler flags are needed to use libcu++. Thrust is a header-only library; there is no need to build or install the project unless you want to run the Thrust unit tests. -
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xxHash
Extremely fast non-cryptographic hash algorithm
xxHash is an extremely fast non-cryptographic hash algorithm, working at RAM speed limit. It is proposed in four flavors (XXH32, XXH64, XXH3_64bits and XXH3_128bits). The latest variant, XXH3, offers improved performance across the board, especially on small data. It successfully completes the SMHasher test suite which evaluates collision, dispersion and randomness qualities of hash functions. Code is highly portable, and hashes are identical across all platforms (little / big endian). Performance on large data is only one part of the picture. Hashing is also very useful in constructions like hash tables and bloom filters. In these use cases, it's frequent to hash a lot of small data (starting at a few bytes). Algorithm's performance can be very different for such scenarios, since parts of the algorithm, such as initialization or finalization, become fixed cost. The impact of branch misprediction also becomes much more present. -
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MOA - Massive Online Analysis
Big Data Stream Analytics Framework.
A framework for learning from a continuous supply of examples, a data stream. Includes classification, regression, clustering, outlier detection and recommender systems. Related to the WEKA project, also written in Java, while scaling to adaptive large scale machine learning. -
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FileVerifier++ is a Windows utility for calculating hashes using a number of algorithms including CRC32, MD5, SHA-1, SHA-256/224/384/512, WHIRLPOOL, and RIPEMD-128/160/256/320. Supported hash file formats include MD5SUM .MD5, SFV, BSD CKSUM, and others.
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The Algorithms Python
All Algorithms implemented in Python
The Algorithms-Python project is a comprehensive collection of Python implementations for a wide range of algorithms and data structures. It serves primarily as an educational resource for learners and developers who want to understand how algorithms work under the hood. Each implementation is designed with clarity in mind, favoring readability and comprehension over performance optimization. The project covers various domains including mathematics, cryptography, machine learning, sorting, graph theory, and more. With contributions from a large global community, it continually grows and improves through collaboration and peer review. This repository is an ideal reference for students, educators, and developers seeking hands-on experience with algorithmic concepts in Python.
