Open Source Algorithms - Page 4
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KACTL
KTH algorithm competition template library
KACTL (the KTH Algorithmic Contest Template Library) is an extensively curated and high-performance C++ algorithms library created by the competitive programming team at the Royal Institute of Technology (KTH) to serve as a trusted, battle-tested codebase for algorithmic contests, programming competitions, and general algorithm development. The repository aggregates dozens of concise implementations of essential data structures, numerical methods, graph algorithms, string processing tools, computational geometry routines, and optimization techniques, all designed with speed, correctness, and compactness in mind. Instead of reinventing algorithms on the fly during contests like ACM-ICPC or Codeforces rounds, competitors can import exactly the component they need — whether a segment tree with lazy propagation, a minimum cost flow solver, or a fast Fourier transform — and focus their energy on problem logic and strategy. -
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Kalibr Allan
IMU Allan standard deviation charts
kalibr_allan is a utility repository that provides scripts and tools for calculating IMU noise parameters for use in Kalibr and other IMU filtering systems. While manufacturers typically provide “white noise” values in IMU datasheets, the bias instability and random walk parameters must be determined experimentally. This project enables users to compute those values using Allan variance analysis from recorded IMU data. The workflow involves recording IMU measurements with the device stationary, converting ROS bag files into MATLAB-compatible formats, and then running MATLAB scripts to generate Allan deviation plots. These plots are analyzed to determine noise density and random walk parameters for both gyroscopes and accelerometers. The repository also includes example data and plots from real sensors such as the XSENS MTI-G-700, Tango Yellowstone Tablet, and ASL-ETH VI-Sensor, providing reference points for interpretation. -
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Kalman and Bayesian Filters in Python
Kalman Filter book using Jupyter Notebook
Kalman Filter book using Jupyter Notebook. Focuses on building intuition and experience, not formal proofs. Includes Kalman filters,extended Kalman filters, unscented Kalman filters, particle filters, and more. All exercises include solutions. Introductory text for Kalman and Bayesian filters. All code is written in Python, and the book itself is written using Juptyer Notebook so that you can run and modify the code in your browser. What better way to learn? This book teaches you how to solve all sorts of filtering problems. Use many different algorithms, all based on Bayesian probability. In simple terms Bayesian probability determines what is likely to be true based on past information. This book is interactive. While you can read it online as static content, it's better to use it as intended. It is written using Jupyter Notebook, which allows you to combine text, math, Python, and Python output in one place. -
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LEAN
Lean algorithmic trading engine by QuantConnect
Automated accounting for splits, dividends, and corporate events like delistings and mergers. Avoid selection bias with dynamically generated assets. Create and select asset universes on proprietary data and indicators. Automatically track portfolio performance, profit and loss, and holdings across multiple asset classes and margin models in the same strategy. Trigger regular functions to occur at desired times, during market hours, on certain days of the week, or at specific times of day. Backtest on almost any time series and import your proprietary signal data into your strategy. Everything is configurable and pluggable. LEAN's highly modular foundation can easily be extended for your fund focus. Use combinations of margin, fill, and slippage models to simulate a liquidity endpoint. 100+ popular technical indicators built, tested, and ready for use. Applicable to any data source. -
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MADDPG
Code for the MADDPG algorithm from a paper
MADDPG (Multi-Agent Deep Deterministic Policy Gradient) is the official code release from OpenAI’s paper Multi-Agent Actor-Critic for Mixed Cooperative-Competitive Environments. The repository implements a multi-agent reinforcement learning algorithm that extends DDPG to scenarios where multiple agents interact in shared environments. Each agent has its own policy, but training uses centralized critics conditioned on the observations and actions of all agents, enabling learning in cooperative, competitive, and mixed settings. The code is built on top of TensorFlow and integrates with the Multiagent Particle Environments (MPE) for benchmarking. Researchers can use it to reproduce the experiments presented in the paper, which demonstrate how agents learn behaviors such as coordination, competition, and communication. Although archived, MADDPG remains a widely cited baseline in multi-agent reinforcement learning research and has inspired further algorithmic developments. -
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Smart Algorithm
Repository implementing a variety of intelligent algorithms
Smart-Algorithm is a repository implementing a variety of intelligent / metaheuristic optimization algorithms (e.g. Genetic Algorithm, Ant Colony, Particle Swarm, Immune Algorithm). The implementations are provided in multiple languages (Java, Python, MATLAB). The repository’s aim is to offer reference implementations of “smart” algorithms for tasks like route planning, optimization, or algorithm learning. Particle Swarm Optimization (PSO) implementations in multiple languages. Immune Algorithm (or immune-inspired optimization) implementations. Multiple versions/language compatibility (Java, Python, MATLAB). -
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bild
Image processing algorithms in pure Go
A collection of parallel image processing algorithms in pure Go. The aim of this project is simplicity in use and development over absolute high performance, but most algorithms are designed to be efficient and make use of parallelism when available. It uses packages from the standard library whenever possible to reduce dependency use and development abstractions. All operations return image types from the standard library. Package convolution provides the functionality to create and apply a kernel to an image. Package effect provides the functionality to manipulate images to achieve various looks. Package histogram provides basic histogram types and functions to analyze RGBA images. Package paint provides functions to edit a group of pixels on an image. Package parallel provides helper functions for the dispatching of parallel jobs. -
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code-nav
Professional programming navigation
Programming Navigation is not just a website, but a complete ecology with the goal of "helping everyone discover high-quality programming resources and improve learning efficiency". It contains multiple sub-projects, and the technology stack includes React, Java SpringBoot, Tencent Cloud Development, etc., all of which are open source for everyone to learn, so that you can easily develop beautiful information navigation websites! Most of the programming navigation websites are in disrepair and have good navigation, but they are limited in search and classification, and they do not have functions such as self-recommendation and liking, so they are not sustainable. -
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frontend-interview
The strongest front-end interview guide in the universe
This is a review summary project about preparing for front-end interviews that I have summarized, and the project is updated from time to time. This is not only a strategy for job interviews, but also a collection of front-end ers to examine themselves and achieve breakthroughs. I hope that through this guide, everyone can open up their own lines of Ren and Du, and go further on the road to the front. This warehouse uses a large number of diagrams to convey knowledge. The so-called picture is worth a thousand words. I hope that this kind of graphic expression will make it easier for you to remember some abstract and incomprehensible concepts. "Graphic Front End" is my subtitle to it. Interviewing is a very subjective and difficult thing. Just because you don't pass the interview doesn't mean you're unqualified, just like passing the interview doesn't mean you're qualified. As a candidate, you are usually given 45 minutes to demonstrate your skills. -
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interactive-coding-challenges
120+ interactive Python coding interview challenges
Interactive Coding Challenges is a collection of practice problems designed to strengthen data structures, algorithms, and problem-solving skills. The repository emphasizes a learn-by-doing approach: you read a prompt, attempt a solution, and verify behavior with tests, often within notebooks or scripts. Problems span arrays, strings, stacks, queues, linked lists, trees, graphs, dynamic programming, and more, mirroring common interview themes. Many challenges include hints and reference solutions so you can compare approaches and learn idiomatic patterns. The structure encourages incremental improvement—start with a brute-force idea, then refine to optimal time and space complexity. It serves both as a self-study path and as a warm-up bank for interview prep or coding katas. -
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jsprit
Open source toolkit for solving rich vehicle routing problems
jsprit is a java based, open-source toolkit for solving rich Traveling Salesman Problems(TSP) and Vehicle Routing Problems(VRP). It is lightweight, flexible and easy-to-use, and based on a single all-purpose meta-heuristic. Setting up the problem, defining additional constraints, modifying the algorithms and visualizing the discovered solutions is as easy and handy as reading classical VRP instances to benchmark your algorithm. It is fit for change and extension due to its modular design and a comprehensive set of unit and integration tests. Possibility to define additional stateless and stateful constraints/conditions to account for the richness of your problem. GraphHopper invests in an active open source community. Our flagships are the GraphHopper routing engine and jsprit, the toolkit for solving rich vehicle routing problems. We promote a fair & diverse mindset. -
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trackers
Multi-object tracking algorithms
trackers is a plug-and-play multi-object tracking library designed to work with virtually any object detection model, enabling developers to follow objects across video frames with minimal setup. The library provides clean, modular implementations of leading tracking algorithms and can be used either from the command line or embedded directly into Python pipelines. It supports inputs such as videos, webcams, RTSP streams, or image directories and produces annotated tracking outputs that include labels and trajectories. Trackers is built for flexibility and benchmarking, allowing users to evaluate performance using standard multi-object tracking metrics and compare algorithms easily. Its architecture emphasizes interoperability so developers can combine their preferred detection models with different trackers without rewriting core logic. -
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tracking.js
A modern approach for Computer Vision on the web
The tracking.js library brings different computer vision algorithms and techniques into the browser environment. By using modern HTML5 specifications, we enable you to do real-time color tracking, face detection and much more, all that with a lightweight core (~7 KB) and intuitive interface. To get started, download the project. This project includes all of the tracking.js examples, source code dependencies you'll need to get started. Unzip the project somewhere on your local drive. The package includes an initial version of the project you'll be working with. While you're working, you'll need a basic HTTP server to serve your pages. Test out the web server by loading the finished version of the project. The main goal of tracking.js is to provide those complex techniques in a simple and intuitive way on the web. We believe computer vision is important to improve people's life, bringing it to the web will make this future a reality a lot faster. -
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xstrings
Implements string functions widely used in other languages
Go package xstrings is a collection of string functions, which are widely used in other languages but absent in Go package strings. All functions are well tested and carefully tuned for performance. Go functions have a unique naming style. One, who has experience in other languages but new in Go, may have difficulties finding out right string function to use. RightJustify returns a string with pad string at left side if str's rune length is smaller than length. If str's rune length is larger than length, str itself will be returned. If there is one alphanumeric rune is found in string, increase the rune by 1. If increment generates a "carry", the rune to the left of it is incremented. This process repeats until there is no carry, adding an additional rune if necessary. ToCamelCase is to convert words separated by space, underscore and hyphen to camel case. -
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TARQUIN
MRS/NMR analysis software
Analysis software for MRS/NMR data. Allows processing and fitting to be performed in a fully automatic workflow. -
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Genetic Algorithm File Fitter, GAFFitter for short, is a tool based on a genetic algorithm (GA) that tries to fit a collection of items, such as files/directories, into as few as possible volumes of a specific size (e.g. CDs or DVDs).
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The JTS Topology Suite is an API for modelling and manipulating 2-dimensional linear geometry. It provides numerous geometric predicates and functions. JTS conforms to the Simple Features Specification for SQL published by the Open GIS Consortium.
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STXXL is an implementation of the C++ standard template library STL for external memory (out-of-core) computations, containers, and algorithms that can process huge volumes of data that only fit on disks.
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Continuation Core and Toolboxes (COCO)
Toolboxes for parameter continuation and bifurcation analysis.
Development platform and toolboxes for parameter continuation, e.g., bifurcation analysis of dynamical systems and constrained design optimization. This material is based upon work partially supported by the National Science Foundation under Grant No. 1016467 and the Danish research council (FTP) under the project number 0602-00753B. Any opinions, findings, and conclusions or recommendations expressed on this site are those of the authors and do not necessarily reflect the views of the National Science Foundation or other funding sources. Documentation and tutorials are available for the following toolboxes: * ep : continuation and bifurcations of equilibrium points * coll : continuation of constrained collections of trajectory segments, including multi-segment boundary-value problems * po : continuation and bifurcations of periodic orbits in smooth and hybrid systems * recipes : collection of examples from the book Recipes for Continuation -
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La suite logicielle Monia est destinée à l'apprentissage de l'algorithmique en français. Elle permet de fabriquer un exécutable à partir d'un organigramme, en passant par un programme procédural écrit en pseudo-langage.
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ehcp for Ubuntu is a hosting control panel, for multiple domains on single machine. easily installable,easy usage, non-complex,functional. homepage:http://www.ehcp.net * automatically installs and works: dns, apache, mysql, ftp, email, domains,auto updat
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ArpON
ARP handler inspection
ArpON (ARP handler inspection) is a Host-based solution that make the ARP standardized protocol secure in order to avoid the Man In The Middle (MITM) attack through the ARP spoofing, ARP cache poisoning or ARP poison routing attack. -
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cds
Concurrent Data Structure library
CDS (Concurrent Data Structures) is a C++ template library of lock-free and fine-grained lock-based algorithms. It contains a collection of concurrent data structures: queues, stacks, sets, maps, etc, and safe memory reclamation schema for concurrent containers - Hazard Pointer and user-space RCU. See doxygen doc http://libcds.sourceforge.net/doc/cds-api/index.html. The source code repo for libcds 2.x has been moved to https://github.com/khizmax/libcds -
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basE91 is an advanced method for encoding binary data as ASCII characters. It is similar to UUencode or base64, but is more efficient. The overhead produced by basE91 depends on the input data. It amounts at most to 23% and can range down to 14%.
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The Adobe Source Libraries (ASL) are a collection of C++ libraries building foundation technology to allow the construction of commercial applications by assembling generic algorithms through declarative descriptions.
