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Quadratic Approximation Method

Track :

Mathematics

Lessons no : 39

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What will you learn in this course?
  • Master quadratic approximation techniques for non-linear functions in optimization and analysis contexts
  • Apply quadratic models to simplify complex functions for practical problem-solving in engineering and data science
  • Evaluate the accuracy of quadratic approximations in various mathematical and real-world scenarios
  • Implement quadratic approximation methods to improve the efficiency of numerical algorithms and computations
  • Analyze the convergence properties of quadratic approximation methods in iterative processes
  • Utilize quadratic approximation to enhance the performance of machine learning models and predictive analytics
  • Develop skills to derive quadratic forms from non-linear functions for use in optimization problems
  • Integrate quadratic approximation techniques into software tools for engineering and scientific applications
  • Compare quadratic approximation with other methods like Taylor series for function analysis
  • Design optimization strategies leveraging quadratic models for better solution accuracy and speed
  • Interpret the results of quadratic approximations to inform decision-making in technical projects
  • Apply quadratic approximation methods to real-world data sets for improved modeling and analysis

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  • You must have an account Register
  • Watch All Lessons
  • Watch at least 50% of Lesson Duration
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  • The Certificate is free !
Lessons | 39
Optimization methods for Civil engineering Intro Video
00:17:52

Optimization methods for Civil engineering Intro Video
Lec 1 Introduction to Optimization
00:43:44

Lec 1 Introduction to Optimization
Lec 2 Classical Optimization
00:54:23

Lec 2 Classical Optimization
Lec 3 Introduction to Linear Problem
00:58:40

Lec 3 Introduction to Linear Problem
Lec 4 General system of equations
00:48:42

Lec 4 General system of equations
Lec 5 Simplex Method
00:55:19

Lec 5 Simplex Method
Lec 6 Solution of Linear Problem using Excel Solver
00:43:40

Lec 6 Solution of Linear Problem using Excel Solver
Lec 7 Bracketing Method
00:26:06

Lec 7 Bracketing Method
Lec 8 Region Elimination Methods
00:40:07

Lec 8 Region Elimination Methods
Lec 9 Gradient Based Method and Examples
00:46:13

Lec 9 Gradient Based Method and Examples
Lec 10 Convex Function
00:48:03

Lec 10 Convex Function
Lec 11 Line Search Methods for Multi Variable Problems
00:36:43

Lec 11 Line Search Methods for Multi Variable Problems
Lec 12 Quadratic Approximation Method
00:25:32

Lec 12 Quadratic Approximation Method
Lec 13 Constrained Optimization I Equality constraints
00:40:01

Lec 13 Constrained Optimization I Equality constraints
Lec 14 Constrained Optimization II Inequality constraints
00:42:10

Lec 14 Constrained Optimization II Inequality constraints
Lec 15 Constrained Optimization III Penalty function methods
00:33:49

Lec 15 Constrained Optimization III Penalty function methods
Lec 16 Introduction to Metaheuristic Optimization
00:48:30

Lec 16 Introduction to Metaheuristic Optimization
Lec 17 Genetic Algorithms Part I
01:00:04

Lec 17 Genetic Algorithms Part I
Lec 18 Genetic Algorithms Part II
00:56:08

Lec 18 Genetic Algorithms Part II
Lec 19 Genetic Algorithms Part III
00:37:46

Lec 19 Genetic Algorithms Part III
Lec 20 Real Coded Genetic Algorithms
00:32:34

Lec 20 Real Coded Genetic Algorithms
Lec 21 Multi modal optimization
00:21:22

Lec 21 Multi modal optimization
Lec 22 Introductioin to R
01:11:45

Lec 22 Introductioin to R
Lec 23 GA using R Unconstrained problem
00:53:40

Lec 23 GA using R Unconstrained problem
Lec 24 GA using R Constrained problem
00:45:22

Lec 24 GA using R Constrained problem
Lec 25 Constraint Handling in GAs
00:41:17

Lec 25 Constraint Handling in GAs
Lec 26 Evolution Strategies ESs
00:29:32

Lec 26 Evolution Strategies ESs
Lec 27 Particle swarm optimization
00:33:11

Lec 27 Particle swarm optimization
Lec 28 Introduction to R Part II
00:35:45

Lec 28 Introduction to R Part II
Lec 29 Multi objective Genetic Algorithms
00:44:55

Lec 29 Multi objective Genetic Algorithms
Lec 30 Introduction to Differential Evolution
00:40:55

Lec 30 Introduction to Differential Evolution
Lec 31 Introduction to Matlab
01:06:40

Lec 31 Introduction to Matlab
Lec 32 Optimization using Matlab Classical methods
00:51:30

Lec 32 Optimization using Matlab Classical methods
Lec 33 A tutorial on Differential Evolution
00:20:40

Lec 33 A tutorial on Differential Evolution
Lec 34 NSGA II Using R
00:39:32

Lec 34 NSGA II Using R
Lec 35 Optimization using MATLAB
00:56:34

Lec 35 Optimization using MATLAB
Lec 36 Optimization using Excel Solver
00:52:05

Lec 36 Optimization using Excel Solver
Lec 37 Multi objective Genetic Algorithms using MATLAB
00:38:26

Lec 37 Multi objective Genetic Algorithms using MATLAB
Lec 38 Solution of a Design Problem Using MATLAB
00:50:36

Lec 38 Solution of a Design Problem Using MATLAB


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Quadratic Approximation Method course, In this course we delve into the technique of approximating non-linear functions with simpler quadratic forms for optimization and analysis.