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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

  • Updated Jan 21, 2024
  • Python

A set of Jupyter notebooks that investigate and compare the performance of several numerical optimization techniques, both unconstrained (univariate search, Powell's method and Gradient Descent (fixed step and optimal step)) and constrained (Exterior Penalty method).

  • Updated Mar 12, 2024
  • Jupyter Notebook

MATLAB code implementations for Nonlinear Programming problems, covering methods like KKT conditions, optimization algorithms, genetic algorithms and penalty function approaches.

  • Updated May 9, 2024
  • MATLAB

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.

  • Updated Jun 9, 2023
  • Python

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…

  • Updated Jul 14, 2021
  • Python

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