Linear Programming for Optimization

Course Curriculum

Chapter 1: Introduction

Lecture 1: Machine Learning and Linear Programming

Lecture 2: What is Linear

Lecture 3: Linear and Non-linear

Lecture 4: What is Linear Programming

Lecture 5: Formulating Linear Program

Lecture 6: Linear Program Formulation – A Loan Policy Model

Lecture 7: Feasibility and Optimality

Lecture 8: Some Practice Questions on Feasibility, Optimality and Sensitivity

Chapter 2: Graphical Method of Solution

Lecture 1: Graphical Method Overview

Lecture 2: Feasible Solution Space

Lecture 3: Optimal Solution at Extreme Point

Lecture 4: Multiple Optimal Solutions in LP – Graphical Understanding

Lecture 5: Moving Towards Feasibility from Imfeasiblity – Graphical Method

Lecture 6: Practice Question Set A

Lecture 7: Solution to Practice Set A Questions

Chapter 3: Simplex Method

Lecture 1: An LP Example for Simplex Method

Lecture 2: Initial Basic Feasible Solution Table

Lecture 3: Feasibility and Optimality Check

Lecture 4: Entering and Leaving Variables to/from Basis

Lecture 5: Simplex Iteration

Lecture 6: Big M Method

Chapter 4: Post-optimality Analysis

Lecture 1: Changes in Profit on Product (Coefficient in Objective Function)

Lecture 2: Changes in Resource Availability (Right Side Values in Constraints)

Lecture 3: Answers to Revision Qustions Set B

Chapter 5: Duality

Lecture 1: Writing Dual

Lecture 2: Primal and Dual LP Formulation

Lecture 3: Optimal Solutions of Primal and Dual

Lecture 4: Where to Use Additional Resources – An Insight through Dual Solution

Chapter 6: Extra

Lecture 1: Unbounded Feasible Solution Space

Lecture 2: Multiple Optimal Solution

Lecture 3: Infeasible Solution – Special Case

Lecture 4: Revision Questions Set C with Solutions

Chapter 7: Examination Questions with Answers

Lecture 1: Multiple Optimal Solutions

Lecture 2: Sensitivity Analysis – Technical Data

Lecture 3: Feasibility and Resource Availability