Optimization
Showing 36 of 175 projects
A C/C++ memory profiler and leak finder with time-based history tracking for Windows, PlayStation, Nintendo Switch, Android, and other platforms.
A comprehensive checklist and collection of Search Engine Optimization (SEO) tips, techniques, and recommended tools.
A Rack-based A/B testing framework for Ruby web applications, designed to work with Rails, Sinatra, or any Rack-based app.
A high-performance large list component for React Native with optimized memory usage and smooth animations.
A gradient processing and optimization library for JAX, designed for research with composable building blocks.
A gradient processing and optimization library for JAX, designed for research with composable building blocks.
A symbolic framework for numeric optimization with automatic differentiation and code generation capabilities.
PyGAD is a Python library for building genetic algorithms and optimizing machine learning models with Keras and PyTorch support.
A high-performance JSON serializer and deserializer for .NET, built on Sigil with extensive optimization.
A framework for running deep neural network models directly in web browsers using ONNX format with WebGPU, WebGL, and WebAssembly backends.
A comprehensive Go scientific library for numerical simulations, linear algebra, differential equations, and computational geometry.
A Model Predictive Contouring Controller (MPCC) for autonomous racing, enabling high-speed path following and obstacle avoidance.
A Ruby command-line tool that suggests performance improvements by analyzing code patterns against faster alternatives.
A Java collections framework with rich APIs, optimized implementations, and additional container types like Bag and Multimap.
A collection of models, callbacks, and datasets to extend PyTorch Lightning for applied AI/ML research and production.
MLBox is a powerful Automated Machine Learning python library.
A fast and simple benchmarking library for Rust projects with ergonomic macros and detailed performance reports.
A collection of reusable scientific computing software components for solving large-scale, complex multi-physics engineering problems.
A Python research toolkit for implementing and visualizing particle swarm optimization algorithms.
A fast, extensible, multi-platform C# library for implementing genetic algorithms in .NET applications.
A dedicated OCaml system for scientific and engineering computing, providing n-dimensional arrays, linear algebra, algorithmic differentiation, and neural networks.
Implementation of hyperparameter optimization methods for ML/DL models with sample code for regression and classification tasks.
A pure Rust numerical optimization library offering a wide range of algorithms with a consistent, type-agnostic interface.
Source code and tutorials for learning genetic algorithms and genetic programming in Python through hands-on example projects.
A Python library for parallel active learning of mathematical functions, intelligently sampling parameter spaces to minimize evaluations.
A Ruby gem that detects inconsistencies between ActiveRecord models and database schema to prevent data issues.
Hardware-accelerated, batchable, and differentiable optimization algorithms implemented in JAX for machine learning research.
A ROS-based method for extrinsic calibration between a 3D LiDAR and a 6-DOF pose sensor using point cloud crispness optimization.
A cargo subcommand to display assembly, LLVM-IR, MIR, and WASM generated for Rust code.
An evolutionary optimization library for Go implementing genetic algorithms, particle swarm optimization, differential evolution, and other algorithms.
A family of extremely fast, high-quality, platform-independent hash functions optimized for different performance profiles.
A C/C++ header file that eliminates platform-specific #ifdefs by providing portable macros for static analysis, optimizations, and API management.
A comprehensive, high-performance library implementing 30+ Evolution Strategies in JAX for scalable optimization on modern hardware.
An open-source high-performance computing platform for systems analysis and multidisciplinary optimization, written in Python.
High-performance LINQ-like extension methods for arrays, Span<T>, and List<T> with SIMD and parallel optimizations.
An intrusive flamegraph profiling library for Rust that lets developers instrument specific code sections for performance analysis.
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