NOC:Constrained and Unconstrained Optimization


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