Mod-01 Lec-29 Uni-modal function and search methods

Course Lectures

• Mod-01 Lec-01 Introduction to Optimization

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• Mod-01 Lec-02 System Design and Analysis

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• Mod-01 Lec-03 Workable system

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• Mod-01 Lec-04 System simulation

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• Mod-01 Lec-05 Information flow diagrams

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• Mod-01 Lec-06 Successive substitution method

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• Mod-01 Lec-07 Successive substitution method contd...

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• Mod-01 Lec-08 Successive substitution method and Newton-Raphson method

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• Mod-01 Lec-09 Newton-Raphson method contd...

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• Mod-01 Lec-10 Convergence characteristics of Newton-Raphson method

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• Mod-01 Lec-11 Newton-Raphson method for multiple variables

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• Mod-01 Lec-12 Solution of system of linear equations

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• Mod-01 Lec-13 Introduction to Curve fitting

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• Mod-01 Lec-14 Example for Lagrange interpolation

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• Mod-01 Lec-15 Lagrange interpolation contd...

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• Mod-01 Lec-16 Best fit

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• Mod-01 Lec-17 Least Square Regression

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• Mod-01 Lec-18 Least Square Regression contd.....

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• Mod-01 Lec-19 Least Square Regression contd.....

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• Mod-01 Lec-20 Non-linear Regression (Gauss - Newton Algorithm)

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• Mod-01 Lec-21 Optimization- Basic ideas

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• Mod-01 Lec-22 Properties of objective function and cardinal ideas in optimization

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• Mod-01 Lec-23 Unconstrained optimization

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• Mod-01 Lec-24 Constrained optimization problems

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• Mod-01 Lec-25 Mathematical proof of the Lagrange multiplier method

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• Mod-01 Lec-26 Test for Maxima/ Minima

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• Mod-01 Lec-27 Handling in-equality constraints

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• Mod-01 Lec-28 Kuhn-Tucker conditions contd...

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• Mod-01 Lec-29 Uni-modal function and search methods

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• Mod-01 Lec-30 Dichotomous search

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• Mod-01 Lec-31 Fibonacci search method

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• Mod-01 Lec-32 Reduction ratio of Fibonacci search method

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• Mod-01 Lec-33 Introduction to multi-variable optimization

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• Mod-01 Lec-34 The Conjugate gradient method

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• Mod-01 Lec-35 The Conjugate gradient method contd...

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• Mod-01 Lec-36 Linear programming

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• Mod-01 Lec-37 Dynamic programming

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• Mod-01 Lec-38 Genetic Algorithms

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• Mod-01 Lec-39 Genetic Algorithms contd...

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• Mod-01 Lec-40 Simulated Annealing and Summary

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