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Explore fixed point iteration, Taylor series linearization, and Newton-Raphson methods for nonlinear systems of equations. Learn how to solve complex equations efficiently using linear and nonlinear methods. Dive into techniques for finding roots of polynomials and understanding the behavior of systems. Engage with practical examples and computational strategies.
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ChE 250 Numeric Methods Lecture #7, Chapra Chapter 6 (non-linear systems), Chapter 7 20070131
Open Methods • Non-Linear Systems of Equations • Fixed Point Method • Talyor Series Linearization Roots of Polynomials • Polynomial tricks and subroutines • Friday • Müller’s Method • Bairstow’s Method • Root location with Excel • Matlab, Scilab
Systems of Nonlinear Equations • We will discuss two ways to solve nonlinear equations (p. 153-7) • The first is to use a fixed point method where the equations are manipulated to calculate the next iteration • This method is subject to constraints on the formulation of the equations • The method is sensitive to the initial guesses • The second method is to linearize the system • Linear methods are then used for a solution
Systems of Nonlinear Equations • What is a linear equation? • Non-linear equation have transcendental functions like log, exponential, sine, cosine, etc. • Or ‘Mixed’ variables: • xy+y3x-1-zsin(y)=x
Systems of Nonlinear Equations • To solve a set of equations, we need a specified set • n equations • n unknowns • We will need initialization values for all variables • If you do not have these…. • Do more work on your model • Make assumptions?
Systems of Nonlinear Equations • Fixed Point Iteration • As before, with a single independent variable, we rearrange the functions to isolate one independent variable • Then we can iterate a set of solutions (x,y)
Systems of Nonlinear Equations • Fixed Point Method • Advantages • Easy to understand and use • Easy algorithm • Disadvantages • Diverges very easily depending on the formulation • Need a fairly close initialization set of variables e.g. (x,y) • We will use this more for linear systems • Questions??
Systems of Nonlinear Equations • Newton-Raphson • ‘Linearize’ the system of equations using the Taylor Expansion • Throw away all higher order terms!
Systems of Nonlinear Equations Taylor Expansion for two variables Rearrange and group
Systems of Nonlinear Equations • The resultant iteration scheme depends only on • xi, yi • ui, vi evaluated at xi, yi • All the partial differentials evaluated at xi,yi • Easy to iterate! Solve simultaneously for x and y
Systems of Nonlinear Equations • Newton method looks complicated and intimidating, but is really quite easy to implement • Strengths • Fast convergence • Weaknesses • Need sufficiently close initialization values • Partials require many calculations contributing to error • As always, curvature…but now in three dimensions • Hard to visualize! No easy graphical method like 2-d • Questions??
Roots of Polynomials • First a note on the computation of polynomial equations • Optimum polynomial function evaluation shown on p. 163, • Derivative function on p. 164 • These are best for calculating polynomials IF your software doesn’t have them already built in
Roots of Polynomials • Why polynomials • There are many engineering models that use linear differential equations • The ODE then must be solved and polynomials come into play • Finding the roots of the characteristic equation is the first step in understanding the behavior of the system
Roots of Polynomials • Second order ODE example • We know from DiffEq that y=ert is the form of the solution • We solve for r, the value of the roots • The roots tells us the nature of the solution • Real or complex? • Positive or negative?
Preparation for Feb 2nd • Reading • Chapra Chapter 8: • Homework due Feb 7th • Chapter 6 • 6.2, 6.7, 6.9, 6.11, 6.12, 6.13 • Chapter 7 • 7.4, 7.5, 7.12, 7.18, 7.19a • Chapter 8 • 8.1, 8.2