Newton and Quasi-Newton optimization with PyTorch

Hessian-based stochastic optimization in TensorFlow and keras

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.

Newton’s second-order optimization methods in python

A Unified Pytorch Optimizer for Numerical Optimization

Implementation of Unconstrained minimization algorithms. These are listed below:

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

In numerical analysis, Newton's method, also known as the Newton–Raphson method, named after Isaac Newton and Joseph Raphson, is a root-finding algorithm which produces successively better approximations to the roots (or zeroes) of a real-valued function.

Project II from CISC.820.01 - Quantitative Foundations

Code for the AISTATS 2024 paper: "Krylov Cubic Regularized Newton: A Subspace Second-Order Method with Dimension-Free Convergence Rate".

a Python implementation of various optimization methods for functions using Streamlit.

Nonlinear optimization course's project is about iterative methods due to finding an optimal point for a function in Rn.

Este repositorio contiene los archivos que crean una interfaz gráfica para el simulador de un movimiento parabólico amortiguado.

Global Optmization with Gradients: Python implementation of an experimental non-local Newton method

Solving problems from the course on the basics of computational physics

simple code to solve algebraic equation and system of equations

Dynamics of Newton's Method for a Nonanalytical Perturbation on a Complex Quadratic Function

This project is a graphical calculator for solving equations using the Newton Method and finding function minima using the Gradient Descent algorithm, utilizing PySide6, SymPy, and Matplotlib.

An application for visualizing and comparing the work of multidimensional optimization methods with using derivative of the objective function: Gradient Descent Method, Newton Method, Fletcher-Reeves Method.