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. Reimplements filterSQP (trust-region filter SQP) and IPOPT (line-search filter barrier) in a modern and abstracted way. Unlocks methods never seen before. Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.

A Julia interface to the Ipopt nonlinear solver

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