Open Source Algorithms - Page 16

AlgART Java libraries for processing arrays and matrices are open-source product, distributed under MIT license. So, anyone can use them for free without any restrictions. Main features: 63-bit addressing of array elements (64-bit long int indexes), memory model concept (allowing storing data in different schemes from RAM to mapped disk files), wide usage of lazy evaluations, built-in multithreading optimization for multi-core processors, wide set of image processing algorithms over matrices, etc. - please see at the site. Almost all classes and methods are thoroughly documented via JavaDoc (you may read full JavaDoc at the site).

This project is no longer supported. We are working on a new algorithmic trading platform with extreme fast execution speed and lots of cool features. Please visit http://www.algospace.com/ for more detail.

AlgoExplorer is a dynamic data structures viewer for Java. It allows the visualization of data structure of a Java program, allowing you to see how objects, handled by the algorithms, interact with each other and how they are related and linked. The project has been moved on: http://code.google.com/p/algoexplorer/

The Aim of this project is to provide a collection of reusable Algorithms which can be used effectively in different scenarios. Algorithm implementations are in Java. Objective here is to implement algorithms which should be more efficient than the JDK implementation and also to create a platform for the researchers who works on Data Mining, Encryption algorithms, ect.. to collaborate and contribute to this project.

An Algorithm Library written in .Net 2.0 which includes common algorithms.

The goal: to provide implementations of as many algorithms (sorting, searching, ...) and data structures (lists, trees, ...) as possible, accompanied by discussion of big O run time and memory limits and references to sources of further study.

An algorithm is represented by a .h file and placed under include. (one .header file per algorithm.) The demo program for the algorithm demonstration is placed under src. (one demo per algorithm.) After the program passes correctly, please initiate Pull Requests, the code is verified and stored in the library, and the new algorithm implementation is published in the README. TAB = 4 space. set ts=4 in vim. Graph output format is Graphviz Dot format. (the output format of the graph is in dot of graphviz.)

Algorithms in Python is a collection of algorithm and data structure implementations (primarily in Python) meant to serve as both learning material and reference code for engineers. It includes code for graph algorithms, heap data structures, stacks, queues, and more — each implemented cleanly so learners can trace logic and adapt for their problems. The repository is particularly useful for people preparing for competitive programming, job interviews, or building a foundational understanding of algorithmic patterns. Because it’s openly maintained, you can browse through issues, see test cases, and observe coding style in a “learning through code” fashion. It also serves as a playground where you can add problems, measure performance, and compare different algorithmic approaches. For anyone striving to move from “I know the syntax” to “I know how to use the right algorithm at the right time,” this repository is a practical asset.

This will help a new data structure learner to learn data structures quickly.

All RL Algorithms from Scratch is an educational reinforcement learning repository built around readable Python and Jupyter Notebook implementations. Its goal is to help learners understand how major reinforcement learning algorithms work under the hood instead of hiding the logic behind large frameworks. The project includes notebooks for value-based methods, policy-gradient methods, actor-critic algorithms, model-based learning, multi-agent reinforcement learning, planning, and hierarchical approaches. Implemented topics include Q-learning, SARSA, Expected SARSA, Dyna-Q, REINFORCE, PPO, A2C, A3C, DDPG, SAC, TRPO, DQN, MADDPG, QMIX, HAC, MCTS, and PlaNet. The code prioritizes clarity, experimentation, and mathematical intuition over production speed. A companion cheat sheet gives learners a quick reference for formulas, pseudocode, and key concepts.

This code is the translation into C++ of part of the Matlab code from Mark Veillette available at: http://www.mathworks.com/matlabcentral/fileexchange/37514-stbl--alpha-stable-distributions-for-matlab

AlphaTensor, developed by Google DeepMind, is the research codebase accompanying the 2022 Nature publication “Discovering faster matrix multiplication algorithms with reinforcement learning.” The project demonstrates how reinforcement learning can be used to automatically discover efficient algorithms for matrix multiplication — a fundamental operation in computer science and numerical computation. The repository is organized into four main components: algorithms, benchmarking, nonequivalence, and recombination. These contain implementations of the discovered matrix multiplication algorithms, tools to benchmark their real-world performance, proofs of nonequivalence among thousands of solutions, and methods for decomposing larger problems into smaller factorizations. Users can explore AlphaTensor’s discovered algorithms interactively using Colab notebooks or Python scripts.

The Gene Expression Programming Framework in Java. It separates the process of evolution from the process of interpretation of the chromosome, allowing the use of various schemes in the chromosome.

A complete library of high-speed java collection classes to complement existing classes with new and advanced algorithms. Complements standard J2SE classes and Jakarta commons classes

AIS is an Development Platform providing a high-speed JIT compiler for LISP and JavaScript, web server, object repositories, MySQL integration and libraries supporting a wide variety of advanced genetic programming and symbolic regression techniques.

The purpose of the project is summarising effort from a number of analytic libraries, adding interactive web-based user interface and making a free open source solution for risk analytics and stress testing. Feb 8, 2012 Paul Glasserman's Importance Sampling and Tail Approximations as well as plain Monte Carlo have been implemented for for the widely used normal copula model of portfolio credit risk. The package includes source code, examples, spreadsheet with results and references to the papers.

The main goal of the Analytical Customer Relationship Management system is to improve services provided to customers, and to use customer contact information for targeted marketing.

Este proyecto constituye una adaptacion y mejora del codigo ANFIS de dominio público de Roger Jang. / This project is an adaptation and improvement of the original public domain ANFIS code of Roger Jang.

Animal is a Java-based system for visualizing and animating algorithms and data structures. Available since 1999, it is one of the established AV systems, and provides one of the largest collections of supported algorithms. Animations can be generated on the fly based on user input - for a large selection of algorithms and topic areas, including, but not limited to, the topic areas of backtracking algorithms, compression, cryptography, basing, searching and sorting.

Animants are virtual ants. Their name comes from the combination of "ants" and "animats", an animat being "a sofware approximation of a living creature". Animants aim to simulate the behaviour of living animals.

An interactive binary search tree. The user may interact with the tree by performing rotations, balancing, insertions, and deletions. For educational purposes

Delphi library of standard containers for keeping ordinal and structured datatypes.

Our aim is to create a collection of useful and free Ansi C libraries of code that are portable around systems.

The Arbitrarily-Decomposed MPI FFT library is a C subroutine library for the computation of the multidimensional Fast Fourier Transform (FFT) of a distributed grid using the Message-Passing Interface (MPI).