second-order-optimization

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

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

Compatible Intrinsic Triangulations (SIGGRAPH 2022)

PyTorch implementation of the Hessian-free optimizer

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.

Second-Order Convergence of Alternating Minimizations