An open source library for the GPU-implementation of L-BFGS-B algorithm

Julia wrapper for L-BFGS-B Nonlinear Optimization Code

Linear regression with the LBFGSB (Limited-memory Broyden-Fletcher-Goldfarb-Shanno BFGS) solver method is a numerical optimization method used to find the minimum of an objective function. It is a gradient descent algorithm that uses an approximation of the Hessian matrix to minimize the function.

Modern Fortran Refactoring of L-BFGS-B Nonlinear Optimization Code

L-BFGS-B as a C++ header-only library

Lbfgsb.rb provides Ruby bindings for L-BFGS-B.

Analyze and debug LBFGS linesearch behavior inside GRAPE

Numo::Optimize provides functions for minimizing objective functions.

a number of optimisation tests for lbfgsb

a dll wrapper for lbfgsb to be called from python using ctypes library

c++ implemenatation of l-bfgs-b

Hybrid metaheuristic and gradient-based solver for the Electric Capacitated Travelling Salesman Problem (EC‑TSP). Implements random‑keys PSO with a smooth, analytically-differentiable surrogate and periodic L‑BFGS‑B refinement; includes data generation, instance loader, experiments and report generation for e‑cargo bike delivery instances.