JuliaManifolds/Manopt.jl

Manifolds.jl provides a library of manifolds aiming for an easy-to-use and fast implementation.

Riemannian Adaptive Optimization Methods with pytorch optim

Optimization functions for Julia

Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.

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

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, Matlab/Octave, Julia or Javascript

Computations and statistics on manifolds with geometric structures.

g2o: A General Framework for Graph Optimization

Hardware accelerated, batchable and differentiable optimizers in JAX.