Lecture 1 - Introduction to Optimization

Lecture 2 - Assumptions and Mathematical Modeling of LPP

Lecture 3 - Geometrey of LPP

Lecture 4 - Graphical Solution of LPP - I

Lecture 5 - Graphical Solution of LPP - II

Lecture 6 - Solution of LPP: Simplex Method

Lecture 7 - Simplex Method

Lecture 8 - Introduction to BIG-M Method

Lecture 9 - Algorithm of BIG-M Method

Lecture 10 - Problems on BIG-M Method

Lecture 11 - Two Phase Method: Introduction

Lecture 12 - Two Phase Method: Problem Solution

Lecture 13 - Special Cases of LPP

Lecture 14 - Degeneracy in LPP

Lecture 15 - Sensitivity Analysis - I

Lecture 16 - Sensitivity Analysis - II

Lecture 17 - Problems on Sensitivity Analysis

Lecture 18 - Introduction to Duality Theory - I

Lecture 19 - Introduction to Duality Theory - II

Lecture 20 - Dual Simplex Method

Lecture 21 - Examples on Dual Simplex Method

Lecture 22 - Interger Linear Programming

Lecture 23 - Interger Linear Programming

Lecture 24 - IPP: Branch and BBound Method

Lecture 25 - Mixed Integer Programming Problem

Lecture 26

Lecture 27

Lecture 28

Lecture 29

Lecture 30

Lecture 31 - Introduction to Nonlinear programming

Lecture 32 - Graphical Solution of NLP

Lecture 33 - Types of NLP

Lecture 34 - One dimentional unconstrained optimization

Lecture 35 - Unconstrained Optimization

Lecture 36 - Region Elimination Technique - 1

Lecture 37 - Region Elimination Technique - 2

Lecture 38 - Region Elimination Technique - 3

Lecture 39 - Unconstrained Optimization

Lecture 40 - Unconstrained Optimization

Lecture 41 - Multivariate Unconstrained Optimization - 1

Lecture 42 - Multivariate Unconstrained Optimization - 2

Lecture 43 - Unconstrained Optimization

Lecture 44 - NLP with Equality Constrained - 1

Lecture 45 - NLP with Equality Constrained - 2

Lecture 46 - Constrained NLP - 1

Lecture 47 - Constrained NLP - 2

Lecture 48 - Constrained Optimization

Lecture 49 - Constrained Optimization

Lecture 50 - KKT

Lecture 51 - Constrained Optimization

Lecture 52 - Constrained Optimization

Lecture 53 - Feasible Direction

Lecture 54 - Penalty and barrier method

Lecture 55 - Penalty method

Lecture 56 - Penalty and barrier method

Lecture 57 - Penalty and barrier method

Lecture 58 - Dynamic programming

Lecture 59 - Multi-Objective decision making

Lecture 60 - Multi-Attribute decision making