Open Source Algorithms - Page 15
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A library for fixed point calculation in s15.16, s7.24 and s7.8 format, entirely written in ANSI C for embedded software (with main focus on the Atmel AVR platforms).
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This is a program for compute the dimension of the Hochschild homology k-vector spaces of a k-algebra A.
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This is an Ada implementation of the Dancing Links X Algorithm as in Donald E. Knuth paper. AdaDLX is used to solve the Eight-Queens puzzle, Sudoku grids and Blocs in Box puzzle.
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A Java implementation of a cubic B-spline curve smoothing function. Allows an arbitary number of points with a variable number of dimensions to have a cubic B-spline curve "fitted". Useful in finance for analysing bond or swap yield and discount curves.
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Adaptive Simulated Annealing (ASA)
simulated annealing optimization and importance-sampling
Adaptive Simulated Annealing (ASA) is a C-language code that finds the best global fit of a nonlinear cost-function over a D-dimensional space. ASA has over 100 OPTIONS to provide robust tuning over many classes of nonlinear stochastic systems. -
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Agenda desenvolvida em Java, utilizando banco PostGreSQL. Itens: Cadastro de Contatos Cadastro de Tarefas
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AhoCorasickDoubleArrayTrie
An extremely fast implementation of Aho Corasick algorithm
AhoCorasickDoubleArrayTrie is a Java implementation of the Aho–Corasick multi-pattern matching algorithm that is optimized using a Double-Array Trie data structure. It is designed for fast keyword scanning across large texts, where you want to search for many patterns simultaneously and efficiently. The core idea is to build an automaton from a dictionary of patterns, then stream through input text to emit matches with minimal overhead. By using a double-array trie representation, the project emphasizes performance and memory efficiency compared to simpler pointer-heavy trie structures, which can matter a lot for large dictionaries or latency-sensitive services. This makes it a strong fit for tasks like content filtering, entity/term spotting, dictionary-based annotation, or high-throughput log/text processing. In short, it’s a specialized, speed-focused library for industrial-strength multi-keyword matching in Java. -
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The Airs GPT project experiments with Steady State Genetic Programming create agents that play tag. It emphasises co-evolution through competition, and provides the user with some tools to help them visualize what is taking place.
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Alcinoe
compoments suite for Delphi
Project description and support moved to : https://github.com/Zeus64/alcinoe However you can use the source you prefer, the SVN here or the GIT on github, both are synchronized (just that GIT don't support large file > 100 mo so some minor files used only as references are missing from GIT) -
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Alef++, is a new programming language like Perl and Lisp syntax, with a many changing in classical languages designs, her specification designed to be implemented for any VM, by default can access to any private/protected/default Java fields or methods.
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Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.
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AlgART Java Libraries
Open source library for processing arrays and matrices
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). -
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Algo Trader (moved to AlgoSpace)
Algorithmic trading platform
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. -
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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/
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Algorithm Database
Efficient Algorithms implemented in Java
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. -
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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.
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Algorithms & Data Structures in C++
Algorithms & Data structures in C++
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.) -
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Algorithms in Python
Data Structures and Algorithms in Python
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. -
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All About Data Structures
Sorting Searching List Template All
This will help a new data structure learner to learn data structures quickly. -
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All RL Algorithms from Scratch
Implementation of all RL algorithms in a simpler way
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. -
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Alpha stable distributions for C++
C++ code to perform Alpha stable fit
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 -
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AlphaTensor
AI discovers faster, efficient algorithms for matrix multiplication
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. -
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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.
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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
