lbfgs

The fastest open-source C++17 optimization library for unconstrained nonlinear problems. Header-only, single dependency. Highest reliability, composable function expressions, unconstrained and constrained solvers.

OptimLib: a lightweight C++ library of numerical optimization methods for nonlinear functions

LBFGS-Lite: A header-only L-BFGS unconstrained optimizer.

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

A collection of numerical methods written in Nim

Improved LBFGS and LBFGS-B optimizers in PyTorch.

A C++ toolkit for Convex Optimization (Logistic Loss, SVM, SVR, Least Squares etc.), Convex Optimization algorithms (LBFGS, TRON, SGD, AdsGrad, CG, Nesterov etc.) and Classifiers/Regressors (Logistic Regression, SVMs, Least Squares Regression etc.)

Type-safe modelling DSL, symbolic transformation, and code generation for solving optimization problems.

Federated learning with PyTorch (federated averaging and consensus optimization): with 'reduced' bandwidth

Perform open-loop optimization of continuous control pulses using fast, high-order timestepping based on Hermite interpolation to find optimal control pulses for implementing quantum gates.

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.

Composable numerical solvers for unconstrained and simple-bounds constrained convex optimization problems in Rust. WASM compatible

LBFGS optimization algorithm ported from liblbfgs

(Python, Tensorflow, R, C, C++) Stochastic, limited-memory quasi-Newton optimizers (adaQN, SQN, oLBFGS)

The code for vector transport free LBFGS quasi-Newton's optimization on the Riemannian manifolds

Library in Haskell for Dynamically Storing Expressions and Code Generator for Various Non-Linear Optimization Solvers

Basic Implementation of "Developability Of TriangleMeshes" from K. Crane, O. Stein, E. Grinspun

Adversarial Attacks on MNIST

Implementation of numerical optimization algorithms for logistic regression problem.