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Lec 32 Branch Bound Method for MILP

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Lessons List | 38 Lesson
Computer Aided Applied Single Objective Optimization Introduction Video
00:04:34

Computer Aided Applied Single Objective Optimization Introduction Video
Lec 1 Introduction to Optimization
02:04:22

Lec 1 Introduction to Optimization
Lec 2 Linear Regression
00:38:22

Lec 2 Linear Regression
Lec 3 Multiple Polynomial and General Linear Least Square Regression
01:01:19

Lec 3 Multiple Polynomial and General Linear Least Square Regression
Lec 4 Nonlinear Regression
00:20:32

Lec 4 Nonlinear Regression
Lec 5 Regression MATLAB Implementation
00:47:33

Lec 5 Regression MATLAB Implementation
Lec 6 Teaching Learning Based Optimization
01:18:31

Lec 6 Teaching Learning Based Optimization
Lec 7 Implementation of TLBO in MATLAB
01:06:43

Lec 7 Implementation of TLBO in MATLAB
Lec 8 Preliminary Statistical analysis for metaheuristic techniques
00:26:06

Lec 8 Preliminary Statistical analysis for metaheuristic techniques
Lec 9 Preliminary Statistical analysis MATLAB implementation
00:19:25

Lec 9 Preliminary Statistical analysis MATLAB implementation
Lec 10 Particle Swarm Optimization
01:11:36

Lec 10 Particle Swarm Optimization
Lec 11 Implementation of Particle Swarm Optimization using MATLAB
00:35:34

Lec 11 Implementation of Particle Swarm Optimization using MATLAB
Lec 12 Differential Evolution
01:01:40

Lec 12 Differential Evolution
Lec 13 Implementation of Differential Evolution using MATLAB
00:43:57

Lec 13 Implementation of Differential Evolution using MATLAB
Lec 14 Binary Coded Genetic Algorithm
01:03:40

Lec 14 Binary Coded Genetic Algorithm
Lec 15 Real Coded Genetic Algorithm
01:18:40

Lec 15 Real Coded Genetic Algorithm
Lec 16 Implementation of Real Coded Genetic Algorithm using MATLAB
01:03:52

Lec 16 Implementation of Real Coded Genetic Algorithm using MATLAB
Lec 17 Artificial Bee Colony Algorithm
00:45:37

Lec 17 Artificial Bee Colony Algorithm
Lec 18 Working of Artificial Bee Colony Algorithm
00:45:04

Lec 18 Working of Artificial Bee Colony Algorithm
Lec 19 Implementation of Artificial Bee Colony using MATLAB
01:09:18

Lec 19 Implementation of Artificial Bee Colony using MATLAB
Lec 20 Comparison of Variation Operators and Survival Strategies
00:15:54

Lec 20 Comparison of Variation Operators and Survival Strategies
Lec 21 Black Box Optimization Problems
00:14:41

Lec 21 Black Box Optimization Problems
Lec 22 Constraint Handling in Metaheuristic Techniques
01:13:37

Lec 22 Constraint Handling in Metaheuristic Techniques
Lec 23 Case Study Production planning
01:25:45

Lec 23 Case Study Production planning
Lec 24 Case Study Production planning MATLAB Implementation
01:18:02

Lec 24 Case Study Production planning MATLAB Implementation
Lec 25 Parallelization and Vectorization of Fitness Function
00:33:40

Lec 25 Parallelization and Vectorization of Fitness Function
Lec 26 Constraint Handling using Correction Approach
00:52:03

Lec 26 Constraint Handling using Correction Approach
Lec 27 MATLAB inbuilt functions Linear Mixed Integer Linear Programming
00:51:55

Lec 27 MATLAB inbuilt functions Linear Mixed Integer Linear Programming
Lec 28 MATLAB inbuilt functions Nonlinear Mixed Integer Nonlinear Programming
01:07:38

Lec 28 MATLAB inbuilt functions Nonlinear Mixed Integer Nonlinear Programming
Lec 29 MATLAB Optimization Tool Options Output Function Vectorization Parallelization
00:33:04

Lec 29 MATLAB Optimization Tool Options Output Function Vectorization Parallelization
Lec 30 MATLAB inbuilt functions Multi objective Optimization
00:27:10

Lec 30 MATLAB inbuilt functions Multi objective Optimization
Lec 31 Simplex Method for LP
00:36:17

Lec 31 Simplex Method for LP
Lec 32 Branch Bound Method for MILP
00:26:48

Lec 32 Branch Bound Method for MILP
Lec 33 MILP formulation of Production Planning Problem
00:35:39

Lec 33 MILP formulation of Production Planning Problem
Lec 34 Generalized Algebraic Modelling System
01:10:24

Lec 34 Generalized Algebraic Modelling System
Lec 35 Solution of Production Planning Problem using GAMS NEOS MIRO
00:50:29

Lec 35 Solution of Production Planning Problem using GAMS NEOS MIRO
Lec 36 IBM ILOG CPLEX Optimization Studio
01:05:20

Lec 36 IBM ILOG CPLEX Optimization Studio
Lec 37 Constraint Programming Applications in IBM ILOG CPLEX Optimization Studio
00:41:40

Lec 37 Constraint Programming Applications in IBM ILOG CPLEX Optimization Studio

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Course Description

Constraint Programming Applications course in hindi, in this course explore diverse applications of constraint programming techniques in solving complex real-world problems.
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