Lecture 7 - Systems of Equations

Lecture 7 - Systems of EquationsLecture 7 - Systems of Equations

CVEN 302

June 17, 2002

Lecture’s GoalsLecture’s Goals

• Discuss how to solve systems– Gaussian Elimination– Gaussian Elimination with Pivoting– Tridiagonal Solver

• Problems with the technique

• Examples

• Iterative Techniques

Computer ProgramComputer Program

The program GEdemo(A,b) does the Gaussian

elimination for a square matrix (nxn). It does

not do any pivoting and works for only one

{b} vector.

Test the ProgramTest the Program

• Example 1

• Example 2

• New Matrix

2X1 + 4X2 - 2 X3 - 2 X4 = - 4

1X1 + 2X2 + 4X3 - 3 X4 = 5

- 3X1 - 3X2 + 8X3 - 2X4 = 7

- X1 + X2 + 6X3 - 3X4 = 7

Problem with Gaussian Problem with Gaussian EliminationElimination

• The problem can occur when a zero appears in the diagonal and makes a simple Gaussian elimination impossible.

• Pivoting changes the matrix so that it will become diagonally dominate and reduce the round-off and truncation errors in the solving the matrix.

Example of PivotingExample of Pivoting

2 X1 + 4 X2 - 2 X3 = 10

X1 + 2 X2 + 4 X3 = 6

2 X1 + 2 X2 + 1X3 = 2

Answer [X1 X2 X3 ] = [-3.40, 4.30, 0.20 ]

Computer ProgramComputer Program

• GEPivotdemo(A,b) is a program, which will do a Gaussian elimination on matrix A with pivoting technique to make matrix diagonally dominate.

• The program is modification to handle a single value of {b}

Question?Question?

• How would you modify the programs to handle multiple inputs?

• What is diagonal matrix, upper triangular matrix, and lower triangular matrix?

• Can you do a column exchange and how would you handle the problem if it works?

Gaussian EliminationGaussian Elimination

• If the diagonal is not dominate the problem can have round off error and truncation errors.

• The scaling will result in problems

Question?Question?

• What happens with the following example?

0.0001X1 + 0.5 X2 = 0.5

0.4000X1 - 0.3 X2 = 0.1

• What happens is the second equation becomes: 0.4000X1 - 2000 X2 = -2000

Question?Question?

• What happens with the following example for values with two-significant figures?

0.4000 X1 - 0.3 X2 = 0.1

0.0001 X1 + 0.5 X2 = 0.5

ScalingScaling

• Scaling is an operation of adjusting the coefficients of a set of equations so that they are all of the same magnitude.

ScalingScaling

• A set of equations may involve relationships between quantities measured in a widely different units (N vs. kN, sec vs hrs, etc.) This may result in equation having very large number and others with very small , if we select pivoting may put numbers on the diagonal that are not large in comparison to other rows and create round-off errors that pivoting was suppose to avoid.

ScalingScaling

• What happens with the following example?

3X1 + 2 X2 +100X3 = 105

- X1 + 3 X2 +100X3 = 102

X1 + 2 X2 - 1X3 = 2

ScalingScaling

• The best way to handle the problem is to normalize the results.

0.03X1 + 0.02 X2 +1.00X3 = 1.05

- 0.01X1 + 0.03 X2 +1.00X3 = 1.02

0.50X1 +1.00 X2 - 0.50X3 = 1.00

Gauss-Jordan MethodGauss-Jordan Method

• The Gauss-Jordan Method is similar to the Gaussian Elimination.

• The method requires almost 50% more operations.


The Gauss-Jordan method changes the matrix into the identity matrix.


There are one phases to the solving technique

• Elimination --- use row operations to convert the matrix into an identity matrix.

• The new b vector is the solution to the x values.

Gauss-Jordan AlgorithmGauss-Jordan Algorithm

[A]{x} ={b}

• Augment the n x n coefficient matrix with the vector of right hand sides to form a n x (n+1)

• Interchange rows if necessary to make the value a11 with the largest magnitude of any coefficient in the first row

• Create zero in 2nd through nth row in first row by subtracting ai1 / a11 times first row from ith row

Gauss-Jordan Elimination Gauss-Jordan Elimination AlgorithmAlgorithm

• Repeat (2) & (3) for first through the nth rows, putting the largest magnitude coefficient in the diagonal by interchanging rows (consider only row j to n ) and then subtract times the jth row from the ith row so as to create zeros in all positions of jth column and the diagonal becomes all ones

• Solve for all of the equations, xi = ai,n+1

Example 1Example 1

X1 + 3X2 = 5

2X1 + 4X2 = 6

Example 2Example 2

-3X1 + 2X2 - X3 = -1

6X1 - 6X2 + 7X3 = -7

3X1 - 4X2 + 4X3 = -6

Band SolverBand Solver

• Large matrices tend to be banded, which means that the matrix has a band of non-zero coefficients and zeroes on the outside of the matrix.

• The simplest of the methods is the Thomas Method, which is used for a tridiagonal matrix.

Advantages of Band SolversAdvantages of Band Solvers

• The method reduce the number of operations and save the matrix in smaller amount of memory.

• The band solver is faster and is useful for large scale matrices.

Thomas MethodThomas Method

• The method takes advantage of the bandedness of the matrix.

• The technique uses a two phase process.

– The first phase is to obtain the coefficients from the sweep.

– The second phase solves for the x values.


• The first phase starts with the first row of coefficients scales the a and r coefficients.

• The second phase solves for x values using the a and r coefficients.


• The program for the method is given as demoThomas(a,d,b,r)

• The algorithm is from the textbook, where a,d,b, & r are vectors from the matrix.

Iterative TechniquesIterative Techniques

• The method of solving simultaneous linear algebraic equations using Gaussian Elimination and the Gauss-Jordan Method. These techniques are known as direct methods. Problems can arise from round-off errors and zero on the diagonal.

• One means of obtaining an approximate solution to the equations is to use an “educated guess”.

Iterative MethodsIterative Methods

• We will look at three iterative methods:– Jacobi Method– Gauss-Seidel Method– Successive over Relaxation (SOR)

Convergence RestrictionsConvergence Restrictions

• There are two conditions for the iterative method to converge.

– Necessary that 1 coefficient in each equation is dominate.

– The sufficient condition is that the diagonal is dominate.

Jacobi IterationJacobi Iteration

• If the diagonal is dominant, the matrix can be rewritten in the following form

Jacobi IterationJacobi Iteration

• The technique can be rewritten in a shorthand fashion, where D is the diagonal, A” is the matrix without the diagonal and c is the right-hand side of the equations.

SummarySummary

• Scaling of the problem will help in the convergence.

• Gauss-Jordan method is more computational intense and does not improve the round-off errors. However, it is useful for finding matrix inverses.

• Banded matrix solvers are faster and use less memory.

HomeworkHomework

• Check the Homework webpage

Lecture 7 - Systems of Equations

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Transcript of Lecture 7 - Systems of Equations