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CPS615 Foils -- Master set G for Iterative Approachs to PDE Solution

Given by Geoffrey C. Fox at CPS615 Basic Simulation Track for Computational Science on Fall Semester 95. Foils prepared 8 November 1995

This Introduces the three fundamental types of PDE's -- Elliptic, Parabolic and Hyperbolic and studies the numerical solution of Elliptic Equations
The sparse matrix formulation is used and iterative approachs -- Jacobi, Gauss Seidel and SOR are defined
These are motivated by analogies between equilibrium of diffusive equations and elliptic systems
Eigenvalue analysis is used to discuss convergence of methods


Table of Contents for CPS615 Foils -- Master set G for Iterative Approachs to PDE Solution

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1 Iterative Solver Module
CPS 615 -- Computational Science in
Simulation Track
Solution of Simple Partial Differential Equations and Iterative Solvers
Fall Semester 1995
2 Abstract of PDE and Iterative Solver CPS615 Module
3 Partial Differential Equations: Use in Continuum Physics
Examples and basic Notation
4 Examples of Different Types of Partial Differential Equations:
The Wave Equation (Hyperbolic) and Typical One Dimensional Solution
5 Examples of Different Types of Partial Differential Equations:
The Parabolic Equation
6 Examples of Different Types of PDE's: Laplace and Poisson Elliptic Equations
7 What Conditions are sufficient for solution of PDE's -- Cauchy Boundary Conditions and Hyperbolic,Parabolic and Elliptic PDE's
8 Closed Boundaries; Dirichlet and Neumann Conditions
Summary of what PDE Types have What Boundary Conditions
9 Examples of Open(Diffusion) and Closed(Laplace) Boundary Conditions
10 Solutions to Elliptic Equations by Finite Differences
11 Central Difference Operator with error O(h2)
12 Difference Equation form of the Operator to solve Laplace's equation
13 The 12 by 12 Block Tridiagonal Equations Coming from Laplace's Equation on a Tiny 5 by 6 Grid
14 General Form of Sparse Matrix Coming from Laplace's Equation - I
15 General Form of Sparse Matrix Coming from Laplace's Equation in two dimensions - II
16 Iterative Methods and Analogy to Diffusion with an Artificial Time
17 Solution of Artificial Time Equation as a Diffusion System Discretized in Space and Time
18 General 2D Artificial Time Diffusion Equation in Iterative Form
19 Traditional Iterative Methods as Special Cases of Artificial Time Diffusion Formalism
20 Simple Iterative Methods: Jacobi, Gauss-Seidel, SOR
21 Matrix Notation for Iterative Methods
22 General Iteration Matrix Splitting and Preconditioning
23 Explicit Form of General Jacobi Iteration
in Matrix and Component Formalism
24 The Special Case of Jacobi Iteration for Laplace's Equation
25 Pseudo Code for the Jacobi Method
26 Formalism for Convergence of Stationary Iterative Methods
27 Eigenvalue Analysis of Iterative Methods
28 Estimation of largest Eigenvalue in One Dimension
29 Eigenvalues and Convergence Rate of Jacobi Iteration
30 Difficult and Easy Eigenfunctions Controlling
Convergence of Jacobi Iteration
31 Decoupling of Even and Odd Grid Point Updates in Basic Jacobi Iteration
32 Damping of Eigenfunctions of Short and Long Wavelength
33 Extension of Jacobi Eigenvalue Analysis to two or more Dimensions
34 Direct Solution Method for Ax=b
35 Banded Matrix Computational Complexity
36 Comparison of Computational Complexity between Direct and Iterative Methods
37 Memory Use in Direct and Iterative Methods
38 Over Relaxation (SOR) and Relation to Jacobi and Gauss-Seidel
39 Over Relaxation Eigenvalues and Matrices for Jacobi Iteration
40 Jacobi Iteration Eigenvalues as a function of Over Relaxation Parameter
41 Jacobi Relaxation for Over Relaxation Parameter w =1/2
42 Introduction to Gauss-Seidel Iterative Approach

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