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

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 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.

Powell's Derivative-Free Optimization solvers.

The Constrained and Unconstrained Testing Environment with safe threads (CUTEst) for optimization software

Optimization methods for science and engineering.

Models (and data) of constrained problems developped with the library PyCSP3

Tidy constraint-programming in tree hierarchies

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.

Constrained Differential Dynamic Programming Solver for Trajectory Optimization and Model Predictive Control

Benchmarking optimization solvers.

HPC solver for nonlinear optimization problems

Constraint Solver ACE

High-performance metaheuristics for optimization coded purely in Julia.

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

A Python Library for modeling combinatorial constrained problems

Constrained optimization for Pytorch using the SQP-GS algorithm

This repository provides the codes for simulating a data-driven safety preserving control architecture for constrained cyber physical systems under cyber attacks.

[ICML 2024] Boundary Exploration for Bayesian Optimization With Unknown Physical Constraints

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.