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

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

Generalized and Efficient Blackbox Optimization System.

Towards Generalized and Efficient Blackbox Optimization System/Package (KDD 2021 & JMLR 2024)

Constrained optimization toolkit for PyTorch

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.

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

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

Library for Multi-objective optimization in Gradient Boosted Trees

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

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

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

The Modified Differential Multiplier Method (MDMM) for PyTorch

PyTorch implementation of Constrained Policy Optimization

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

A Python Library for modeling combinatorial constrained problems

A python package for consensus-based optimization

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.

Implementation of the Stochastic Frank Wolfe algorithm in TensorFlow and Pytorch.

Constrained optimization for Pytorch using the SQP-GS algorithm