With Uno, finally take full control of your SQP/barrier solver for nonlinearly constrained optimization

Newton and Quasi-Newton optimization with PyTorch

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, FISTA, ADMM, Gaussian Mixture Model, OPTICS, DBSCAN, Random Forest, Decision Tree, Support Vector Machine, Independent Component Analysis, Latent Semantic Indexing, Principal Component Analysis, Singular Value Decomposition, K Nearest Neighbors, K Means, Naïve Bayes Mixture Model, Gaussian Discriminant Analysis, Newton Method, Coordinate Descent, Gradient Descent, Elastic Net Regression, Ridge Regression, Lasso Regression, Least Squares, Logistic Regression, Linear Regression

Basic Machine Learning implementation with python

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

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.

Hessian-based stochastic optimization in TensorFlow and keras

Python and MATLAB code for Stein Variational sampling methods

Implementation and analysis of convex optimization algorithms

Code for "A Regularized Newton Method for Nonconvex Optimization with Global and Local Complexity Guarantees"

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.

Optimization course for MSAI at MIPT

ForSolver - linear and nonlinear solvers

Prototyping of matrix free Newton methods in Julia

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

A Unified Pytorch Optimizer for Numerical Optimization

Optimization course assignments under the supervision of Dr. Maryam Amirmazlaghani

A Python Implementation of Polynomials and algorithms associated with it

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

C++11 implementation of numerical algorithms described in Numerical Analysis by Richard L. Burden and J. Douglas Faires

Newton’s second-order optimization methods in python

Compilation of the assignments of the course of COL726: Numerical Algorithms (Spring 2021) and their solutions

Projects from MATH 555, Computational Algebraic Geometry taken Fall of 2021

Collection of methods for numerical analysis and scientific computing, including numerical root-finders, numerical integration, linear algebra, and data visualization. Created for APPM4600 at CU Boulder.

Numerical methods algorithms implementation

Optimization Techniques Lab Dump

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