Lesson 25 - Learning Goals

25.1 Learn why Ordinary Differential Equations is useful

25.2 Learn several different Ordinary Differential Equations Techniques

25.3 Learn how to compare different Ordinary Differential Equations algorithms

Ordinary Differential Equations

Introduction

• First order, ordinary differential equations with initial conditions (initial value problems)

• Given a function y' = dy/dx = f(x,y), with y(0) = y0

• Want y(x) for some x* > 0
• More precisely, if the solution up to a point (xi,yi) is known, want to take a step of size h to estimate yi+1 = y(xi+1) where xi+1 = xi + h.

The Euler Method

Given y'= f(x,y)

Approximate y' at point (xi , yi) by the slope of the line joining it and (xi+1 , yi+1);

y'(xi , yi) (yi+1 - yi) / (xi+1 - xi) = (yi+1 - yi) / h

Then conclude f(xi , yi) (yi+1 - yi) / h , and yi+1 yi + h f(xi , yi)

• Analysis shows error in estimate h (very inaccurate 1st order method)
• Stability of the approximate solution is questionable - whether error in estimation introduces an additional parasitic solution to the differential equation that overwhelms the true solution.
• Roundoff error is also a problem.
  
  
  

Euler algorithm

Starting from the point (0 , yo), we estimate the solution to y' = f(x,y) at x-values seperated by the step size h, until we reach the desired value xf of x. It is assumed that xf = n h for some positive integer n, which is the number of steps taken in attaining the final solution.

1. x 0 , y y0
2. if(x==xf) , STOP
3. y = y + h* f(x,y)
4. x = x + h
5. go to step 2

The Runge-Kutta method

• This is a whole class of formulae of which one interests us.
• The fourth order method has error h4, making it very much better than the Euler method.

Advantages (relative to more complicated techniques):

• Easy to program
• Good stability
• Easy to change step size
• Self starting (only one initial point is required)
• Reasonably accurate
  
  

The derivation of the formula is too complex, but basic idea is to start from (xi , yi) and use Euler method to obtain different estimates of yi + ½ and yi+1 which are combined so as to minimize error:

y*i + ½ = yi + h/2 f(xi , yi)

y**i + ½ = yi + h/2 f(xi + ½ , y*i + ½)

y*i + 1 = yi + h f(xi + ½ , y**i + ½)

yi + 1 = yi + h/6 [ f(xi , yi) + 2f(xi + ½ , y*i + ½)

+2f(xi + ½ , y**i + ½) + f(xi + 1 , y*i + 1) ]

To use the Runge-Kutta method, start with y(0) = y0, and apply the above equations (in sequence!) to get y1 = y(h).

Repeat this process for x2, … , xn where xn = nh.

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