constrained-optimization

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

Lightweight optimization with local, global, population-based and sequential techniques across mixed search spaces

The fastest open-source C++17 optimization library for unconstrained nonlinear problems. Header-only, single dependency. Highest reliability, composable function expressions, unconstrained and constrained solvers.

Constrained optimization toolkit for PyTorch

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.

With Uno, finally take full control of your SQP/barrier solver for nonlinearly constrained optimization

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

NMFLibrary: Non-negative Matrix Factorization (NMF) Library: Version 2.1

Generic Constraint Development Environment

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.

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.

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

HPC solver for nonlinear optimization problems

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

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.

Model and solve optimal control problems in Julia, both on CPU and GPU.

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)

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.

Modern Fortran Edition of the SLSQP Optimizer