Constrained and unconstrained optimization

Lecture 1 : Introduction to Optimization.

Lecture 2 : Assumptions & 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 & BBound Method.

Lecture 25: Mixed Integer Programming Problem.

Lecture 26 : Introduction to Transportation Problem - I.

Lecture 27 : Transportation Problem - II.

Lecture 28 : Vogel Approximation Method.

Lecture 29 : Optimal Solution Generation for Transportation Problem.

Lecture 30 : Degeneracy in TP and Overview of Assignment Problem.

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 - I.

Lecture 47 : Constrained NLP - II.

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.