Unconstrained optimization algorithms in python, line search and trust region methods
-
Updated
Dec 19, 2018 - Jupyter Notebook
Unconstrained optimization algorithms in python, line search and trust region methods
Implementation of approximate free-energy minimization in PyTorch
A Matlab/Octave package for oscillatory integration
Implementation of our paper entitiled FAMINet: Learning Real-time Semi-supervised Video Object Segmentation with Steepest Optimized Optical Flow published in TIM.
A matlab function for steepest descent optimization using Quasi Newton's method : BGFS & DFP
Implementation of unconstrained optimization techniques in Matlab
A Unified Pytorch Optimizer for Numerical Optimization
Fortran/Python linear algebra utilities
Demonstration of gradient descent methods
Implementations of various Algorithms used in Numerical Analysis, from root-finding up to gradient descent and numerically solving PDEs.
Implementation of Unconstrained minimization algorithms. These are listed below:
This repository consists of Lab Assignments for course Machine Learning.
This contains three programs written in python. Gauss-Seidel and Successive Over Relaxation to solve system of equations and Steepest-Descent to minimize a function of 2 or 3 variables.
Pseudo-Inverse, Gradient-Stochastic-Steepest Descent, Logistic Regression and LDA-QDA
Contains a mathematical optimization project implemented in Python
The implementation of advanced mathematical optimization methods
Numerical optimization algorithms with examples in Python.
Example Code for numerical optimization. Written in python.
Implementation of Steepest Descent Algorithm in TypeScript
Add a description, image, and links to the steepest-descent topic page so that developers can more easily learn about it.
To associate your repository with the steepest-descent topic, visit your repo's landing page and select "manage topics."