Optimization Algorithms
Showing 14 of 14 projects
A Bayesian optimization software package for automatically running experiments to minimize an objective in as few runs as possible.
A software implementation of factorization machines for estimating interactions between categorical variables in large datasets.
A Python research toolkit for implementing and visualizing particle swarm optimization algorithms.
A pure Rust numerical optimization library offering a wide range of algorithms with a consistent, type-agnostic interface.
A Python framework for gradient-free optimization, featuring common algorithms like genetic algorithms and simulated annealing.
A C++ toolkit with MATLAB interface for automatic control and dynamic optimization, including model predictive control and parameter estimation.
A Go library implementing feedforward/backpropagation neural networks with support for multiple activation functions, solvers, and classification modes.
A collection of optimization algorithms and logging utilities for Torch machine learning models.
A flexible Rust framework for building and running genetic algorithm simulations for optimization and search problems.
A Julia package providing algorithms for regression analysis via regularized empirical risk minimization.
An extensible .NET genetic algorithm library for optimization and AI, making evolutionary computation simple.
A Python library implementing biologically-inspired algorithms including neural networks, genetic programming, and particle swarm optimization.
A metaheuristic optimization framework for solving complex combinatorial problems like N-queens and protein folding.
A monadic implementation of fully-connected neural networks in OCaml with backpropagation and customizable hyperparameters.
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