mws_gen_sle_txt_seidel

# mws_gen_sle_txt_seidel - 04.08.1 Chapter 04.08 Gauss-Seidel...

This preview shows pages 1–4. Sign up to view the full content.

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document

This preview has intentionally blurred sections. Sign up to view the full version.

View Full Document
This is the end of the preview. Sign up to access the rest of the document.

Unformatted text preview: 04.08.1 Chapter 04.08 Gauss-Seidel Method After reading this chapter, you should be able to: 1. solve a set of equations using the Gauss-Seidel method, 2. recognize the advantages and pitfalls of the Gauss-Seidel method, and 3. determine under what conditions the Gauss-Seidel method always converges. Why do we need another method to solve a set of simultaneous linear equations? In certain cases, such as when a system of equations is large, iterative methods of solving equations are more advantageous. Elimination methods, such as Gaussian elimination, are prone to large round-off errors for a large set of equations. Iterative methods, such as the Gauss-Seidel method, give the user control of the round-off error. Also, if the physics of the problem are well known, initial guesses needed in iterative methods can be made more judiciously leading to faster convergence. What is the algorithm for the Gauss-Seidel method? Given a general set of n equations and n unknowns, we have 1 1 3 13 2 12 1 11 ... c x a x a x a x a n n 2 2 3 23 2 22 1 21 ... c x a x a x a x a n n . . . . . . n n nn n n n c x a x a x a x a ... 3 3 2 2 1 1 If the diagonal elements are non-zero, each equation is rewritten for the corresponding unknown, that is, the first equation is rewritten with 1 x on the left hand side, the second equation is rewritten with 2 x on the left hand side and so on as follows 04.08.2 Chapter 04.08 nn n n n n n n n n n n n n n n n n n n n n n a x a x a x a c x a x a x a x a x a c x a x a x a x a c x 1 1 , 2 2 1 1 1 , 1 , 1 2 2 , 1 2 2 , 1 1 1 , 1 1 1 22 2 3 23 1 21 2 2 These equations can be rewritten in a summation form as 11 1 1 1 1 1 a x a c x n j j j j 22 2 1 2 2 2 a x a c x j n j j j . . . 1 , 1 1 1 , 1 1 1 n n n n j j j j n n n a x a c x nn n n j j j nj n n a x a c x 1 Hence for any row i , . , , 2 , 1 , 1 n i a x a c x ii n i j j j ij i i Now to find i x ’s, one assumes an initial guess for the i x ’s and then uses the rewritten equations to calculate the new estimates. Remember, one always uses the most recent estimates to calculate the next estimates, i x . At the end of each iteration, one calculates the absolute relative approximate error for each i x as 100 new old new i i i i a x x x where new i x is the recently obtained value of i x , and old i x is the previous value of i x . Gauss-Seidel Method 04.08.3 When the absolute relative approximate error for each x i is less than the pre-specified tolerance, the iterations are stopped....
View Full Document

{[ snackBarMessage ]}

### Page1 / 10

mws_gen_sle_txt_seidel - 04.08.1 Chapter 04.08 Gauss-Seidel...

This preview shows document pages 1 - 4. Sign up to view the full document.

View Full Document
Ask a homework question - tutors are online