newton-method

Newton and Quasi-Newton optimization with PyTorch

Python machine learning applications in image processing, recommender system, matrix completion, netflix problem and algorithm implementations including Co-clustering, Funk SVD, SVD++, Non-negative Matrix Factorization, Koren Neighborhood Model, Koren Integrated Model, Dawid-Skene, Platt-Burges, Expectation Maximization, Factor Analysis, ISTA, F…

High-performance and differentiation-enabled nonlinear solvers (Newton methods), bracketed rootfinding (bisection, Falsi), with sparsity and Newton-Krylov support.

Drawing Newton's fractal using pure js, rust-wasm, SIMDs, threads and GPU

Basic Machine Learning implementation with python

Uno: a modular open-source solver for unifying nonlinear optimization

Hessian based stochastic optimization in TensorFlow and keras

Python and MATLAB code for Stein Variational sampling methods

Implementation and visualization (some demos) of search and optimization algorithms.

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

If you find any errors in the work of algorithms, you can fix them by creating a pull request

Collection of functions to find roots of functions float → float. Pure OCaml code.

Prototyping of matrix free Newton methods in Julia

Optimization course for MSAI at MIPT

Newton’s second-order optimization methods in python

Implementation and analysis of convex optimization algorithms

Optimization course assignments under the supervision of Dr. Maryam Amirmazlaghani

Implementation of Unconstrained minimization algorithms. These are listed below:

Optimization Techniques Lab Dump

Computation of periodic orbits of non-autonomous systems and fixed points of maps using Newton method