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Lecture 9 Nonlinear systems of equations

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Title: Lecture 9 Nonlinear systems of equations


1
Lecture 9 - Nonlinear systems of equations
  • CVEN 302
  • June 15, 2001

2
Lectures Goals
  • Iterative Techniques
  • Jacobian method
  • Gaus-Siedel method
  • Relaxation technique

3
Lectures Goals
  • Nonlinear Systems of equations
  • Newton method
  • fixed point iterations

4
Nonlinear Equations
  • What is a nonlinear equation?
  • Nonlinear equations are assumed to be those which
    contain powers or products of the variables
    and/or transcendental functions.

5
Nonlinear Equations
  • What methods will we be using to handle nonlinear
    equations?
  • Newtons method
  • Direct iteration
  • Minimum of a nonlinear function of several
    variables.

6
Newtons Method
  • By assuming that a sufficiently close approximate
    solution x a1 and y b1 is known, the
    equations can be expanded into Taylor series
    about these points. By using Dx and Dy to
    represent the changes in x and y, which need to
    be made in successive cycles of approximation.

7
1-D Newtons Method
  • The Newton method comes from a Taylor series
    expansion
  • The series is truncated and the root is set equal
    to zero.

8
Newtons Method
  • The goal of the calculations is to find a f(x)0,
    so set f(xk1) 0 and rearrange the equation.
    f x (xk) is the first derivative of f(x).
  • 0 f(xk) (xk1 - xk)f x(xk)
  • xk1 _at_ xk - f(xk) / fx (xk)

9
Newton Method for 2-D Problem
  • Using a two nonlinear functions, we would like to
    find the intersection of the two.
  • The problem uses a Taylor series expansion to
    calculate the next value if we have an initial
    estimation.

10
Newton Method
  • The Taylor series expansion for two equations
    will result in

11
Newton Method
  • The Taylor series is truncated at the first
    derivative and resulting set equations

12
Newton Method
  • Rearrange the equations in matrix format

13
Newton Method
  • The step size will vary with the function of the
    derivatives.
  • The equation can be written in-terms of the
    functions and their partial derivatives.

14
Newton Method
  • The Taylor series expansion for two equations
    will result in

15
Newtons Method
  • The Jacobian matrix is defined as the matrix of
    partial derivatives of the functions.

16
Newtons Method
  • The method is exactly the same as the 1-D method
    except that the Jacobian matrix has more partial
    derivatives to use to find the solution.

17
Example of Newtons Method
  • 1.4X1 - X2 0.6
  • X12 - 1.6X1 - X2 4.6
  • Solution (X1 , X2 ) ( (-1,-2), (4,5) )

18
Newtons Example
  • Jacobian matrix
  • The functions are given.

19
Newton Example Program
  • The program demoNewtonSys is a demonstration of
    the Newton method for a two dimensional example.

20
Example of Newton
  • Example of a 3 x 3 matrix (demoNewtonSys4.m)
  • 10 X1 - X12 - X2
    X32 4
  • - X12 10X2 - X22 - X3
    5
  • -X1 - X22 10X3 -
    X32 6

21
Newton Example
  • The Jacobian matrix of the example is

22
Nonlinear Equations
  • Newtons method for systems of equations uses the
    Taylor Series expansion to help find the
    intersections of the equations.
  • The problem of finding the solution of a set of
    nonlinear equations is much more difficult than
    for linear equations.

23
Nonlinear Equations
  • In nonlinear systems the expression
  • x A-1(x) b(x)
  • indicates that the right hand side of the
    equation can
  • not be evaluated until x is known. The best one
    can
  • do is devise a method that gives a sequence of
  • increasingly good guesses at the x that satisfies
    the
  • equation.

24
Iterative methods for nonlinear equations
  • Direct iteration solution of nonlinear system in
    which the equations are selected.
  • Successive substitution
  • Minimization of a scalars functions.

25
Direct iterative process
  • Does not require computation of partial
    derivatives of the function.
  • The equations are set up to iterate to find the
    solution.

26
Iterative Solutions
  • General rules of the thumb for convergence are
  • xf(x,y,z,) , yg(x,y,z,..), zh(x,y,z,)
  • fx fy fz lt 1
  • gx gy gz lt 1
  • hx hy hz lt 1

27
Consider the following example using an
iterative method
  • 1.4X1 - X2 0.6
  • X12 - 1.6X1 - X2 4.6
  • Solution (X1 , X2 ) ( (-1,-2), (4,5) )

28
Iterative Example
  • From the Newton method we could find the results
    with iterative techniques.
  • The functions are selected to get the values to
    converge.

29
Test Iterations
  • Case 1

30
Test Iterations
  • Case 2

31
Test Iterations
  • Case 3

32
Test Iterations
  • Case 4

33
Iteration Example Program
  • The program demoIteration demonstrates different
    sets of equations to find the roots.
  • The problem depend on the partial derivative to
    find out whether or not the program will
    converge.

34
Programs for Fixed Point Iteration
  • X12 50 X1 X22 X32 - 200 0
  • X12 20 X2
    X32 - 50 0
  • -X12 - X22 40 X3 75 0
  • Which becomes
  • X1 -0.02X12 - 0.02X22 - 0.02X32 4
  • X2 -0.05X12 - 0.05X32 2.5
  • X3 0.025X12 0.025X22 - 1.875

35
Iterative methods
  • When they do converge they may favor one solution
    over the other.
  • The solution to nonlinear equations may converge
    at different rates depending on how you select
    the values.

36
Successive Substitution
  • The technique is a simple iterative method for
    nonlinear systems.
  • A (k) x (k1) b (k)
  • x (k1) A (k) -1 b (k)

37
For the example problem using successive
substitution
  • 1.4X1 - X2 0.6
  • X12 - 1.6X1 - X2 4.6
  • Solution (X1 , X2 ) ( (-1,-2), (4,5) )

38
For the example problem using successive
substitution
39
Programs for Successive Substitution
  • The set of equations are
  • X12 50 X1 X22 X32 - 200 0
  • X12 20 X2
    X32 - 50 0
  • -X12 - X22 40 X3 75 0

40
Successive Substitution
41
Successive Substitution
  • This method may also favor one solution over
    another solution.
  • The method does not work for highly nonlinear
    problems.

42
Successive Substitution
  • The program demoSSub will show how the iterative
    technique works for the mildly nonlinear set of
    equations.
  • The book has a set of programs which will do the
    same thing

43
Summary
  • Iteration on nonlinear equations are limited to
    set of conditions.
  • The iterations can converge on a solution but not
    all solutions, it may favor on solution to
    another.
  • Newtons method uses PDE to help solve the
    problem and can be used for nonlinear functions.

44
Homework
  • Check the Homework webpage
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