A large scale non-linear optimization library

Modeling language for Mathematical Optimization (linear, mixed-integer, conic, semidefinite, nonlinear)

An object-oriented algebraic modeling language in Python for structured optimization problems.

CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.

An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations

A light-weight, Eigen-based C++ library for trajectory optimization for legged robots.

Package to call the NLopt nonlinear-optimization library from the Julia language

Represent trained machine learning models as Pyomo optimization formulations

HPC solver for nonlinear optimization problems

Data Structures for Optimization Models

MATLAB implementations of a variety of nonlinear programming algorithms.

A next-gen solver for nonlinearly constrained nonconvex optimization. Modular and lightweight, it unifies iterative methods (SQP vs interior points) and globalization techniques (filter method vs merit function, line search vs trust region method) in a single framework. Competitive against IPOPT, filterSQP, SNOPT, MINOS and CONOPT

A solver for nonlinear programming

FelooPy: Efficient & Feature-Rich Integrated Decision Environment

Proximal operators for nonsmooth optimization in Julia

Proximal algorithms for nonsmooth optimization in Julia

Toolbox for gradient-based and derivative-free non-convex constrained optimization with continuous and/or discrete variables.

A set of lightweight header-only template functions implementing commonly-used optimization methods on Riemannian manifolds and convex spaces.

Optimization models using various solvers

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