Linear Programming With Python

Learn linear programming step by step with Python – build models, solve optimization problems, and apply in real cases.

What you'll learn

Formulate real-world problems as linear programming models

Write objective functions and constraints in mathematical form

Implement and solve optimization models using Python libraries

Interpret solutions and apply results to decision-making scenarios

Requirements

No prior experience in optimization is required. Basic Python knowledge is helpful but not mandatory, as all steps are explained in detail.

Description

This course is designed to teach you linear programming from the ground up, using Python as a practical tool to model and solve optimization problems. Whether you are a student, an engineer, or someone interested in decision science, you will find clear explanations and hands-on coding examples that connect theory to application.We begin with the fundamentals: what linear programming is, how objective functions and constraints work, and why these models are so widely used in industries such as logistics, manufacturing, and operations management. Each concept is explained in plain language, and mathematical expressions are read out naturally, for example, ‘three x plus two y is less than or equal to one hundred.’After understanding the basics, you will move into Python implementation. We use libraries that make it easy to define and solve optimization problems. You will learn how to translate a real situation into a mathematical model, write it in Python, and interpret the solution. Along the way, we will address common mistakes and clarify points that usually confuse beginners.By the end of this course, you will have the ability to set up your own optimization models, test them with data, and use Python to find the best solutions. The skills you gain here are practical, transferable, and highly useful for anyone interested in optimization and applied problem solving.

Overview

Section 1: Introduction

Lecture 1 Intro

Section 2: What is Linear Programming?

Lecture 2 Introduction to LP

Section 3: Linear Algebra Basics

Lecture 3 Matrix Operations

Lecture 4 Determinant Calculations

Lecture 5 Matrix Inverse

Lecture 6 Linear Equation Systems

Section 4: Linear Programming Examples in Python

Lecture 7 Problem Formulation

Lecture 8 Standard Form Conversion

Lecture 9 Graphical Method

Lecture 10 Simplex Algorithm

Lecture 11 Two-Phase Simplex

Lecture 12 Big M Method

Section 5: Linear Programming with Pyomo

Lecture 13 Bakery Optimization

Section 6: Linear Programming Recap

Lecture 14 Information About Simplex Lessons

Lecture 15 Intro to Linear Programming

Lecture 16 Formulating LP Problem

Lecture 17 Standard Form of LP

Lecture 18 Basic Example of LP

Lecture 19 Canonical Form of LP

Lecture 20 Fundamentals of the Simplex Method

Lecture 21 Steps - Simplex

Lecture 22 Manual Example - Simplex

This course is for students, engineers, analysts, and anyone curious about optimization and decision science. It is also suitable for Python learners who want to see how programming can be applied to solve real business and engineering problems.