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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 (Contd.)

Lecture 50 : KKT

Lecture 51 : Constrained Optimization

Lecture 52 : Constrained Optimization (Contd.)

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 (Contd.)

Lecture 58 : Dynamic Programming

Lecture 59 : Multi - Objective Decision Making

Lecture 60 : Multi-Attribute Decision Making

Description:

PRE-REQUISITES: Basic Mathematics INTENDED AUDIENCE: Any engineering or mathematics student INDUSTRIES APPLICABLE TO: R&D companies COURSE OUTLINE: This course has been designed for postgraduate students. Operations research is not only important in its own right but also forms an integral part of applied sciences like economics, management science, engineering design problems etc. The course provides a systematic and thorough discussion on the subject matter with numerous examples.

Constrained and Unconstrained Optimization

NPTEL

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#Computer Science #Algorithms and Data Structures #Mathematics #Linear Programming #Data Science #Data Analysis #Sensitivity Analysis #Applied Mathematics #Constrained Optimization

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