Optimization in ML

Nonlinear optimization algorithms implemented in Python with demo programs

Implementation of methods for unconstrained search for the minima of the univariate and multivariate functions

keywords: nonlinear optimization, pattern search, augmented lagrangian, karush-kuhn-tucker, constrained optimization, conjugate gradient methods, quasi newton methods, line search descent methods, onedimensional and multidimensional optimazation

This repository is a collection of mathematical optimization algorithms and solutions for a variety of optimization problems. It provides a toolkit of algorithms and techniques for tackling optimization challenges in different domains.

My algorithms for Gradient descent minimum search, using Sven, DSK-Powell\Golden section and simple const step with some visualization examples

The purpose of optimization is to achieve the “best” design relative to a set of prioritized criteria or constraints. These include maximizing factors such as productivity, strength, reliability, longevity, efficiency, and utilization. This decision-making process is known as optimization. This repository discusses some of the matchematical tech…