a lightweight header-only C++17 library of numerical optimization methods for (un-)constrained nonlinear functions and expression templates

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

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

Generalized and Efficient Blackbox Optimization System.

Generic Constraint Development Environment

Incremental Potential Contact (IPC) is for robust and accurate time stepping of nonlinear elastodynamics. IPC guarantees intersection- and inversion-free trajectories regardless of materials, time-step sizes, velocities, or deformation severity.

Robotics tools in C++11. Implements soft real time arm drivers for Kuka LBR iiwa plus V-REP, ROS, Constrained Optimization based planning, Hand Eye Calibration and Inverse Kinematics integration.

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

OptCuts, a new parameterization algorithm, jointly optimizes arbitrary embeddings for seam quality and distortion. OptCuts requires no parameter tuning; automatically generating mappings that minimize seam-lengths while satisfying user-requested distortion bounds.

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.

HPC solver for nonlinear optimization problems

Source Codes for Codimensional Incremental Potential Contact (C-IPC)

A highly customizable SQP & barrier solver for nonlinearly constrained optimization

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.

High-performance metaheuristics for optimization coded purely in Julia.

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

Powell's Derivative-Free Optimization solvers.

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)