Newton and Quasi-Newton optimization with PyTorch
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Updated
Mar 10, 2024 - Python
Newton and Quasi-Newton optimization with PyTorch
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
A Unified Pytorch Optimizer for Numerical Optimization
Solving problems from the course on the basics of computational physics
Implementation and visualization (some demos) of search and optimization algorithms.
Implementation of Unconstrained minimization algorithms. These are listed below:
Finds first root in the given range using newton, simple iterations or scanning methods
Newton’s second-order optimization methods in python
Dynamics of Newton's Method for a Nonanalytical Perturbation on a Complex Quadratic Function
Compilation of the assignments of the course of COL726: Numerical Algorithms (Spring 2021) and their solutions
Multidimensional optimization methods with using derivative of the objective function
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.
Numerical analysis methods in Python
Global Optmization with Gradients: Python implementation of an experimental non-local Newton method
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.
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