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Lecture 10: Problems on BIG-M Method
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Classroom Contents
Constrained and Unconstrained Optimization
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- 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
