1 Lecture 1 : Introduction to Optimization
2 Lecture 2 : Assumptions & Mathematical Modeling of LPP
3 Lecture 3 : Geometrey of LPP
4 Lecture 4 : Graphical Solution of LPP- I
5 Lecture 5 : Graphical Solution of LPP- II
6 Lecture 6: Solution of LPP: Simplex Method
7 Lecture 7: Simplex Method
8 Lecture 8: Introduction to BIG-M Method
9 Lecture 9: Algorithm of BIG-M Method
10 Lecture 10: Problems on BIG-M Method
11 Lecture 11: Two Phase Method: Introduction
12 Lecture 12: Two Phase Method: Problem Solution
13 Lecture 13: Special Cases of LPP
14 Lecture 14: Degeneracy in LPP
15 Lecture 15: Sensitivity Analysis- I
16 Lecture 16: Sensitivity Analysis- II
17 Lecture 17: Problems on Sensitivity Analysis
18 Lecture 18: Introduction to Duality Theory- I
19 Lecture 19: Introduction to Duality Theory- II
20 Lecture 20: Dual Simplex Method
21 Lecture 21: Examples on Dual Simplex Method
22 Lecture 22: Interger Linear Programming
23 Lecture 23: Interger Linear Programming
24 Lecture 24: IPP: Branch & BBound Method
25 Lecture 25: Mixed Integer Programming Problem
26 Lecture 26 : Introduction to Transportation Problem - I
27 Lecture 27 : Transportation Problem - II
28 Lecture 28 : Vogel Approximation Method
29 Lecture 29 : Optimal Solution Generation for Transportation Problem
30 Lecture 30 : Degeneracy in TP and Overview of Assignment Problem
31 Lecture 31 : Introduction to Nonlinear programming
32 Lecture 32 : Graphical Solution of NLP
33 Lecture 33 : Types of NLP
34 Lecture 34 : One dimentional unconstrained optimization
35 Lecture 35 : Unconstrained Optimization
36 Lecture 36 : Region Elimination Technique-1
37 Lecture 37 : Region Elimination Technique-2
38 Lecture 38 : Region Elimination Technique-3
39 Lecture 39 : Unconstrained Optimization
40 Lecture 40 : Unconstrained Optimization
41 Lecture 41 : Multivariate Unconstrained Optimization-1
42 Lecture 42 : Multivariate Unconstrained Optimization-2
43 Lecture 43 : Unconstrained Optimization
44 Lecture 44: NLP with Equality Constrained-1
45 Lecture 45 : NLP with Equality Constrained-2
46 Lecture 46 : Constrained NLP - I
47 Lecture 47 : Constrained NLP - II
48 Lecture 48 : Constrained Optimization
49 Lecture 49 : Constrained Optimization (Contd.)
50 Lecture 50 : KKT
51 Lecture 51 : Constrained Optimization
52 Lecture 52 : Constrained Optimization (Contd.)
53 Lecture 53 : Feasible Direction
54 Lecture 54 : Penalty and Barrier Method
55 Lecture 55 : Penalty Method
56 Lecture 56 : Penalty and Barrier Method
57 Lecture 57 : Penalty and Barrier Method (Contd.)
58 Lecture 58 : Dynamic Programming
59 Lecture 59 : Multi - Objective Decision Making
60 Lecture 60 : Multi-Attribute Decision Making