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Advanced Numerical Analysis
Advanced Numerical Analysis
Curriculum
1 Section
49 Lessons
10 Weeks
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Advanced Numerical Analysis
49
2.1
Lecture 1: Introduction and Overview
2.2
Lecture -2 Fundamentals of Vector Spaces
2.3
Lecture 3 : Basic Dimension and Sub-space of a Vector Space
2.4
Lecture 4 : Introduction to Normed Vector Spaces
2.5
Lecture 5 : Examples of Norms,Cauchy Sequence and Convergence, Introduction to Banach Spaces
2.6
Lecture 6 : Introduction to Inner Product Spaces
2.7
Lecture 7 : Cauchy Schwaz Inequality and Orthogonal Sets
2.8
Lecture 8 : Gram-Schmidt Process and Generation of Orthogonal Sets
2.9
Lecture 9 : Problem Discretization Using Appropriation Theory
2.10
Lecture 10 : Weierstrass Theorem and Polynomial Approximation
2.11
Lecture 11 : Taylor Series Approximation and Newton’s Method
2.12
Lecture 12 : Solving ODE – BVPs Using Firute Difference Method
2.13
Lecture 13 :Solving ODE – BVPs and PDEs Using Finite Difference Method
2.14
Lecture 14 : Finite Difference Method (contd.) and Polynomial Interpolations
2.15
Lecture 15 : Polynomial and Function Interpolations,Orthogonal Collocations Method for Solving ODE -BVPs
2.16
Lecture 16 : Orthogonal Collocations Method for Solving ODE – BVPs and PDEs
2.17
Lecture 17 :Least Square Approximations, Necessary and Sufficient Conditions for Unconstrained Optimization
2.18
Lecture 18 : Least Square Approximations :Necessary and Sufficient Conditions for Unconstrained Optimization Least Square Approximations ( contd..)
2.19
Lecture 19 :Linear Least Square Estimation and Geometric Interpretation of the Least Square Solution
2.20
Lecture 20 : Geometric Interpretation of the Least Square Solution (Contd.) and Projection Theorem in a Hilbert Spaces
2.21
Lecture 21 : Projection Theorem in a Hilbert Spaces (Contd.) and Approximation Using Orthogonal Basis
2.22
Lecture 22 :Discretization of ODE-BVP using Least Square Approximation
2.23
Lecture 23 : Discretization of ODE-BVP using Least Square Approximation and Gelarkin Method
2.24
Lecture 24 : Model Parameter Estimation using Gauss-Newton Method
2.25
Lecture 25 : Solving Linear Algebraic Equations and Methods of Sparse Linear Systems
2.26
Lecture 26 : Methods of Sparse Linear Systems (Contd.) and Iterative Methods for Solving Linear Algebraic Equations
2.27
Lecture 27 : Iterative Methods for Solving Linear Algebraic Equations
2.28
Lecture 28 : Iterative Methods for Solving Linear Algebraic Equations: Convergence Analysis using Eigenvalues
2.29
Lecture 29 :Iterative Methods for Solving Linear Algebraic Equations: Convergence Analysis using Matrix Norms
2.30
Lecture 30 : Iterative Methods for Solving Linear Algebraic Equations: Convergence Analysis using Matrix Norms (Contd.)
2.31
Lecture 31 : Iterative Methods for Solving Linear Algebraic Equations: Convergence Analysis (Contd.)
2.32
Lecture 32 :Optimization Based Methods for Solving Linear Algebraic Equations: Gradient Method
2.33
Lecture 33 : Conjugate Gradient Method, Matrix Conditioning and Solutions of Linear Algebraic Equations
2.34
Lecture 34 : Matrix Conditioning and Solutions and Linear Algebraic Equations (Contd.)
2.35
Lecture 35 : Matrix Conditioning (Contd.) and Solving Nonlinear Algebraic Equations
2.36
Lecture 36 : Solving Nonlinear Algebraic Equations: Wegstein Method and Variants of Newton’s Method
2.37
Lecture 37 : Solving Nonlinear Algebraic Equations: Optimization Based Methods
2.38
Lecture 38 : Solving Nonlinear Algebraic Equations: Introduction to Convergence analysis of Iterative Solution Techniques
2.39
Lecture 39 : Solving Nonlinear Algebraic Equations: Introduction to Convergence analysis (Contd.) and Solving ODE-IVPs
2.40
Lecture 40 :Solving Ordinary Differential Equations – Initial Value Problems (ODE-IVPs) : Basic Concepts
2.41
Lecture 41 :Solving Ordinary Differential Equations – Initial Value Problems (ODE-IVPs) : Runge Kutta Methods
2.42
Lecture 42 :Solving ODE-IVPs : Runge Kutta Methods (contd.) and Multi-step Methods
2.43
Lecture 43 :Solving ODE-IVPs : Generalized Formulation of Multi-step Methods
2.44
Lecture 44 : Solving ODE-IVPs : Multi-step Methods (contd.) and Orthogonal Collocations Method
2.45
Lecture 45 : Solving ODE-IVPs: Selection of Integration Interval and Convergence Analysis of Solution Schemes
2.46
Lecture 46 : Solving ODE-IVPs: Convergence Analysis of Solution Schemes (contd.)
2.47
Lecture 47 :Solving ODE-IVPs: Convergence Analysis of Solution Schemes (contd.) and Solving ODE-BVP using Single Shooting Method
2.48
Lecture 48 : Methods for Solving System of Differential Algebraic Equations
2.49
Lecture 49 : Methods for Solving System of Differential Algebraic Equations (contd.) and Concluding Remarks
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