PyHessian is a Pytorch library for second-order based analysis and training of Neural Networks

ADAHESSIAN: An Adaptive Second Order Optimizer for Machine Learning

A C++ interface to formulate and solve linear, quadratic and second order cone problems.

Pytorch implementation of preconditioned stochastic gradient descent (affine group preconditioner, low-rank approximation preconditioner and more)

Distributed K-FAC Preconditioner for PyTorch

FEDL-Federated Learning algorithm using TensorFlow (Transaction on Networking 2021)

This repository implements FEDL using pytorch

Tensorflow implementation of preconditioned stochastic gradient descent

Hessian-based stochastic optimization in TensorFlow and keras

PyTorch implementation of the Hessian-free optimizer

Compatible Intrinsic Triangulations (SIGGRAPH 2022)

Federated Learning using PyTorch. Second-Order for Federated Learning. (IEEE Transactions on Parallel and Distributed Systems 2022)

This package is dedicated to high-order optimization methods. All the methods can be used similarly to standard PyTorch optimizers.

Minimalist deep learning library with first and second-order optimization algorithms made for educational purpose

LIBS2ML: A Library for Scalable Second Order Machine Learning Algorithms

Prototyping of matrix free Newton methods in Julia

Subsampled Riemannian trust-region (RTR) algorithms

An efficient and easy-to-use Theano implementation of the stochastic Gauss-Newton method for training deep neural networks.

FOSI library for improving first order optimizers with second order information