There are 13 repositories under constrained-optimization topic.
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.
NMFLibrary: Non-negative Matrix Factorization (NMF) Library: Version 2.1
PRIMA is a package for solving general nonlinear optimization problems without using derivatives. It provides the reference implementation for Powell's derivative-free optimization methods, i.e., COBYLA, UOBYQA, NEWUOA, BOBYQA, and LINCOA. PRIMA means Reference Implementation for Powell's methods with Modernization and Amelioration, P for Powell.
High-performance metaheuristics for optimization coded purely in Julia.
A next-gen solver for optimization with nonconvex objective and constraints. Reimplements filterSQP and IPOPT (barrier) in a modern and generic way, and unlocks a variety of novel methods. Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.
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
A compact Constrained Model Predictive Control (MPC) library with Active Set based Quadratic Programming (QP) solver for Teensy4/Arduino system (or any real time embedded system in general)
Modern Fortran Edition of the SLSQP Optimizer
Generalized and Efficient Blackbox Optimization System.
A curated set of C++ examples for optimization-based elastodynamic contact simulation using CUDA, emphasizing algorithmic convergence, penetration-free, and inversion-free conditions. Designed for readability and understanding, this tutorial helps beginners learn how to write simple GPU code for efficient solid simulations.
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.
Riemannian stochastic optimization algorithms: Version 1.0.3
A dependency free library of standardized optimization test functions written in pure Python.
Python implementation of the genetic algorithm MAP-Elites with applications in constrained optimization
PyTorch implementation of Constrained Policy Optimization
Optimization algorithms by M.J.D. Powell