Open Source Mac Algorithms - Page 8

The Automatic Model Optimization Reference Implementation, AMORI, is a framework that integrates the modelling and the optimization processes by providing a plug-in interface for both. A genetic algorithm and Markov simulations are currently implemented.

Some data structures developed in c++ for the 'Algorithms & Data Structures' exam at the Computer Science Department of the University of Bari by Tafuni Vito & Nicola Perrini.

A branch-and-prune method to find all solutions to systems of nonlinear equations. The number of variables must equal the number of equations. The variables must have proper lower and upper bounds. The solver has an interface to the AMPL environment.

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).

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.

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.

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.

Aleph is both a multi-platform machine learning framework aimed at simplicity and performance, and a library of selected state-of-the-art algorithms.

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).

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.

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

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

The subgraph matching problem (subgraph isomorphism) is NP-complete. Previously, we designed an exact subgraph matching (ESM) algorithm for dependency graphs using a backtracking approach (http://esmalgorithm.sourceforge.net). We further designed an approximate subgraph matching (ASM) algorithm that is capable of detecting approximate subgraph matching based on a subgraph distance. Assume that the graph G and the subgraph Gs have m and n vertices, and km and kn edges respectively, the total worst-case algorithm complexity is O(m^n * n(n-1)/2 * km * log m). This Java implementation implements our ASM algorithm. See README file: https://sourceforge.net/projects/asmalgorithm/files/ If you use our ASM implementation to support academic research, please cite the following paper: Haibin Liu, Lawrence Hunter, Vlado Keselj, and Karin Verspoor. Approximate Subgraph Matching-based Literature Mining for Biomedical Events and Relations. PLOS ONE, 8:4 e60954, 2013.

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).

We live in very exciting times 😃! BLDC motors are entering the hobby community more and more and many great projects have already emerged leveraging their far superior dynamics and power capabilities. BLDC motors have numerous advantages over regular DC motors but they have one big disadvantage, the complexity of control. Even though it has become relatively easy to design and manufacture PCBs and create our own hardware solutions for driving BLDC motors the proper low-cost solutions are yet to come. One of the reasons for this is the apparent complexity of writing the BLDC driving algorithms, Field oriented control (FOC) being an example of one of the most efficient ones. The solutions that can be found online are almost exclusively very specific for certain hardware configurations and the microcontroller architecture used. Additionally, most of the efforts at this moment are still channeled towards the high-power applications of the BLDC motors and proper low-cost FOC.

Dieser Parser, soll arithmetische Ausdrücke, die als String vorliegen, ausrechnen, bzw. diverse Funktionen darauf anwenden können. Bis jetzt kann er nur Audrücke wie "4^(5+2)-23*4/cos(3)" ausrechnen. er unterstützt auch vergleichsoperatoren

Aim is to develop a library of utility functions to efficiently simulate division, multiplication, mod, finding first 1/0 bit etc. (using shift and add/subtract) for software development on low cost dsp systems lacking these.

High-performance computing code and libraries used in the High Performance Computing subject in Telecommunications Engineering at the University of Granada (http://geneura.ugr.es/~jmerelo/asignaturas/AAP)

Toolbox for Java developers. Includes right now only a multi-layered extensible cache system but will grow depending on requirements.

The Asterisk Config PHP-Parser claims to be a simple but effective function writen in PHP non-OOP that is capable to parse any standard .conf Asterisk configuration file an drop the data into a multi-dimensional array.

Asteroid has been merged with Scampi, to give rise to OScar. Follow us on https://bitbucket.org/oscarlib/oscar/wiki/Home Asteroid offers a powerful framework for developing constraint-based local search solution to combinatorial problems. This technique provides good scalability to real-world problems. It includes a library of standard constraints and invariants to declaratively define the problem you want to solve, and it also provides powerful search mechanisms.