Optimization Algorithms : Python, Julia, Matlab, R

Master Optimization Algorithms with Python, Julia, MATLAB & R – Linear, Integer, Nonlinear & Metaheuristic Methods

What you'll learn

nderstand fundamental optimization techniques, including Linear Programming (LP), Integer Programming (IP), and Nonlinear Programming

Develop practical coding skills by implementing optimization algorithms in Python, Julia, MATLAB, and R to solve complex decision-making problems

Explore and apply metaheuristic optimization methods such as Particle Swarm Optimization (PSO), Simulated Annealing, and Ant Colony Optimization

Integrate optimization techniques with machine learning and stochastic methods to enhance decision-making processes in industries such as finance, logistics

Requirements

A basic understanding of programming concepts will be helpful but is not required.

Familiarity with basic mathematics and linear algebra will make it easier to grasp optimization concepts, but I will explain everything in a way that is accessible to all learners.

No prior knowledge of optimization is necessary—you’ll learn everything step by step.

Description

Optimization is at the core of decision-making in engineering, business, finance, artificial intelligence, and operations research. If you want to solve complex problems efficiently, understanding optimization algorithms is essential.This course provides a thorough understanding of optimization techniques, from fundamental methods like Linear Programming (LP) and Integer Programming (IP) to advanced metaheuristic algorithms such as Particle Swarm Optimization (PSO), Simulated Annealing, and Ant Colony Optimization. We will implement these techniques using Python, Julia, MATLAB, and R, ensuring you can apply them across different platforms.Throughout the course, we will work with real-world optimization problems, covering essential topics like the Traveling Salesman Problem, Portfolio Optimization, Job Shop Scheduling, and more. You will gain hands-on experience with numerical optimization, stochastic optimization, and machine learning-based approaches.We will also explore key mathematical concepts behind optimization and discuss how these methods are applied across different industries. Whether you are an engineer, data scientist, researcher, or analyst, this course will provide the practical skills needed to optimize solutions effectively.No prior experience with optimization is required; we’ll start from the basics and gradually move into advanced topics. By the end of this course, you’ll be able to confidently apply optimization techniques in real-world applications.Join now and start learning!

Overview

Section 1: Introduction

Lecture 1 Introduction

Lecture 2 Course Guide

Section 2: Python Programming (Optional)

Lecture 3 What is Python?

Lecture 4 Anaconda & Jupyter & Visual Studio Code

Lecture 5 Google Colab

Lecture 6 Environment Setup

Lecture 7 Python Syntax & Basic Operations

Lecture 8 Data Structures: Lists, Tuples, Sets

Lecture 9 Control Structures & Looping

Lecture 10 Functions & Basic Functional Programming

Lecture 11 Intermediate Functions

Lecture 12 Dictionaries and Advanced Data Structures

Lecture 13 Modules, Packages & Importing Libraries

Lecture 14 File Handling

Lecture 15 Exception Handling & Robust Code

Lecture 16 Basic Object-Oriented Programming (OOP) Concepts

Lecture 17 Data Visualization Basics

Lecture 18 Advanced List Operations & Comprehensions

Section 3: Integer Programming

Lecture 19 Branch and Bound | Intro

Lecture 20 Branch and Bound | Diagram

Lecture 21 Branch and Bound | Knapsack

Lecture 22 Branch and Bound | Production Planning

Section 4: Nonlinear Programming

Lecture 23 Intro

Lecture 24 Karush-Kuhn-Tucker (KKT) Conditions

Section 5: Metaheuristic Optimization

Lecture 25 Particle Swarm Optimization

Lecture 26 Particle Swarm Optimization - Mathematical Model

Lecture 27 Particle Swarm Optimization - Python

Lecture 28 Simulated Annealing

Lecture 29 Simulated Annealing - Python

Lecture 30 Simulated Annealing - Python Output

Lecture 31 Ant Colony Optimization

Lecture 32 NSGA-II Algorithm

Lecture 33 NSGA-II Algorithm - Theory

Lecture 34 NSGA-II Algorithm - Python

Lecture 35 NSGA-II Algorithm - Python Output

Lecture 36 Tabu Search with Python

Section 6: Stochastic Optimization

Lecture 37 Probability Theory Review

Lecture 38 Robust Optimization with Julia

Section 7: Optimization with R Programming

Lecture 39 Linear Programming

Section 8: Optimization Projects with Julia

Lecture 40 Inventory Routing Problem with Julia

Lecture 41 Traveling Salesman Problem

Lecture 42 Job Shop Scheduling

Lecture 43 Portfolio Optimization

Section 9: Optimization with MATLAB

Lecture 44 Transportation Problem

Section 10: Machine Learning and Optimization

Lecture 45 RMSProp

Lecture 46 ADAGrad

Lecture 47 ADAGrad - Python

Lecture 48 Gradient Descent

Section 11: Sequential Decision Analytics

Lecture 49 SDA with Julia - Inventory Management

This course is designed for engineers, data scientists, researchers, and business analysts who want to apply optimization techniques to real-world problems.