Simple and reliable optimization with local, global, population-based and sequential techniques in numerical discrete search spaces.

Pytorch-based framework for solving parametric constrained optimization problems, physics-informed system identification, and parametric model predictive control.

Constrained optimization toolkit for PyTorch

Generalized and Efficient Blackbox Optimization System

A curated collection of Python examples for optimization-based solid simulation, emphasizing algorithmic convergence, penetration-free, and inversion-free conditions, designed for readability and understanding.

A general-purpose, deep learning-first library for constrained optimization in PyTorch

Generalized and Efficient Blackbox Optimization System.

An interior-point method written in python for solving constrained and unconstrained nonlinear optimization problems.

Library for Multi-objective optimization in Gradient Boosted Trees

Safe Pontryagin Differentiable Programming (Safe PDP) is a new theoretical and algorithmic safe differentiable framework to solve a broad class of safety-critical learning and control tasks.

A Python Library for modeling combinatorial constrained problems

A dependency free library of standardized optimization test functions written in pure Python.

Synchronizing pong to music

The Modified Differential Multiplier Method (MDMM) for PyTorch

PyTorch implementation of Constrained Policy Optimization

Python library to implement advanced trading strategies using machine learning and perform backtesting.

A SciPy compatible super fast Python implementation for Particle Swarm Optimization.

Constrained optimization for Pytorch using the SQP-GS algorithm

LAMBDA is a model-based reinforcement learning agent that uses Bayesian world models for safe policy optimization

A PyTorch implementation of constrained optimization and modeling techniques