Optimization
Showing 36 of 127 projects
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 Java collections framework with rich APIs, optimized implementations, and additional container types like Bag and Multimap.
A Ruby command-line tool that suggests performance improvements by analyzing code patterns against faster alternatives.
A collection of models, callbacks, and datasets to extend PyTorch Lightning for applied AI/ML research and production.
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 C/C++ header file that eliminates platform-specific #ifdefs by providing portable macros for static analysis, optimizations, and API management.
A family of extremely fast, high-quality, platform-independent hash functions optimized for different performance profiles.
A comprehensive, high-performance library implementing 30+ Evolution Strategies in JAX for scalable optimization on modern hardware.
High-performance LINQ-like extension methods for arrays, Span<T>, and List<T> with SIMD and parallel optimizations.
An open-source high-performance computing platform for systems analysis and multidisciplinary optimization, written in Python.
An intrusive flamegraph profiling library for Rust that lets developers instrument specific code sections for performance analysis.
Automated image compression tool that competitively optimizes noisy, high-resolution images into tiny files for web distribution.
Apache module that automatically rewrites web pages to reduce latency and bandwidth by applying performance optimizations.
A Python framework for multiobjective evolutionary algorithms (MOEAs) with support for NSGA-II, NSGA-III, MOEA/D, and other optimization methods.
A Python library implementing nature-inspired meta-heuristic optimization algorithms for solving complex problems.
A high-performance Ruby gem for memoizing instance, class, and module methods with thread safety and advanced features.
A tensor library for differentiable functional programming in F#, with PyTorch-like APIs and GPU support.
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