Newton and Quasi-Newton optimization with PyTorch

A next-gen solver for nonconvex optimization. Uno is a Lagrange-Newton solver that unifies barrier and SQP methods in a modern and generic way, and implements different globalization flavors (line search/trust region and merit function/filter method/funnel method). Competitive against filterSQP, IPOPT, SNOPT, MINOS and CONOPT.

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

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…

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

Basic Machine Learning implementation with python

Hessian-based stochastic optimization in TensorFlow and keras

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

Python and MATLAB code for Stein Variational sampling methods

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

Implementation and analysis of convex optimization algorithms

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

Optimization course for MSAI at MIPT

ForSolver - linear and nonlinear solvers

Prototyping of matrix free Newton methods in Julia

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

This is a Numerical Analysis course project, implementing numerical analysis methods.

Optimization course assignments under the supervision of Dr. Maryam Amirmazlaghani

Newton’s second-order optimization methods in python

Polynomial essentials for Golang including real root isolation, complex root solving methods, root bounds, and derivatives.