Analytical and Numerical Approximation of functions
-
Updated
Oct 23, 2022 - Jupyter Notebook
Analytical and Numerical Approximation of functions
Project for the Numerical Linear Algebra course @{cse.uoi.gr, math.uoi.gr}
MATLAB Numerical Optimization Methods
This repository will comprise primary optimization algorithms in Python language. Optimization is an extremely important part of machine learning.
A fun side project to perform AI algorithms using plain java code.
Steepest Descent Method is studied for Rosenbrock function.
Gradient descent approximation of orthogonal projection of a point on a convex periodic curve
Through this project we will try to understand working of Steepest-Descent and Gradient-Descent method and the differences between them.
Course assignments for CL 663: IIT Bombay
Implementation of a few optimization algorithms
The project involves a practical optimization problem that is modelled and solved using some mathematical optimization methods and software.
This repo contain implementation of Steepest Descent algorithm using inexact line search and Newton's method on Functions like Tried Function, Three Hump Camel, Styblinski-Tang Function, Rosenbrock Function, etc.
Diffuse Optical Tomography (DOT) is an non-invasive optical imaging technique that measures the optical properties of physiological tissue using near infrared spectrum light. Optical properties are extracted from the measurement using reconstruction algorithm. This project uses the steepest descent method for reconstruction of optical data.
Non Linear Mathematical Optimization for objective functions f: ℝn→ ℝ.
This repository contains an implementation of the Gradient Descent Algorithm in C++, utilizing the Armijo condition to optimize step sizes.
This project summarizes the learning process of optimization methods, attempting to start from the original mathematical formulas and write Python code to understand the principles of the methods.
assignments and projects of advanced optimization course
Pseudo-Inverse, Gradient-Stochastic-Steepest Descent, Logistic Regression and LDA-QDA
Numerical optimization algorithms with examples in Python.
Contains a mathematical optimization project implemented in Python
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."