Komputasi Numerik : Integrasi dan Differensiasi Numerik

1. Komputasi Numerik:Integrasi dan Differensiasi Numerik Agus Naba Physics Dept., FMIPA-UB

2. Ordinary Differential Equation Ordinary Differential Equation (ODE) is a differential equation in which all dependent variables are functions of a single independent variable.

3. ODE’s Problem First-order Ordinary Differential Equation (ODE):

4. Euler’s Method

5. y(t) Slope: yn tn tn+1 It enables us to calculate all of yn = y (tn), given y(t0).

6. Numerical Errors • Truncation Errors, depending on numerical methods • Round-off Errors, depending on capability of computer in storing floating-point number

7. Truncation Errors The curve y(t) is not generally a straight-line between the neighbouring grid-times tn and tn+1as assumed. According to Taylor Series: O(t2) Truncation Error Each step incurs truncation error ~ t2 Net truncation errors of Euler’s Method ~ t

8. Round-off Errors For every type of computer, there is a charasteristic number, , which defined as the smallest number which when added to a number of order unity gives rise to a new number. For example:  = 2.2 x 10-16 (for double precision number in IBM-PC )  = 1.19 x 10-7 (for single precision number in IBM-PC ) The net round-off errors of Euler’s Method  /t.

9. Net Numerical Errors of Euler’s Method At large t, the error is dominated by the truncation errors, whereas the round-off errors dominates at small t. Minimum net numerical errors are achieved when

10.  ~1/2 t t~1/2

11. Numerical Instalibilities y(0)=1 t

12. Solusi Numerik y(0) y(0) Numerical Instabilities t t

13. Defect of Euler’s Method Not generally used in scientific computing: • Truncation errors is far larger than other, more advanced, methods. • Too prone to numerical instabilities

14. Main reason of large truncation errors: Euler’s method only evaluates derivative at the beginning of the interval [tn,tn+1], i.e., at tn. （Very asymetric with respect to the beginning and the end of the interval)

15. k1 k2 k1 /2 yn Runge-Kutta (RK) Methods Euler’s Method y(t) f2 y(t) The 2nd order RK Method f1 tn tn+ h/2 tn+1 h

16. (k1+k2)/2 k2 k1 yn Modified Euler’s Method y(t) yn+1 =yn+(k1+k2)/2 Modified Euler’s Method f1 f2 tn tn+ h h

17. The 4th order RK method f2 f4 f3 f1 tn tn + h/2 tn + h

18. The 4th order RK method

20. Net Numerical Errors of RK Methods

21. RK Methods Performanceon IBM-PC for double precision hmin increases and min decreases as order gets larger, but needs more computational effort.

22. Example

23. err = yanalitic-ynumeric Global integration errors associated with Euler's method (solid curve) and the4th order Runge-Kutta method (dotted curve) plotted against the step-length h. Single precision calculation.

24. Global integration errors associated with Euler's method (solid curve) and the 4th order Runge-Kutta method (dotted curve) plotted against the step-length h. Double precision calculation.

25. Adaptive Integration Method Consider the following ODE: Analitic solution:

26. err = xanalitic-xnumeric Global integration error associated with a xed step-length (h = 0:01), 4th order RK method, plotted against the independent variable, t, for a system of o.d.e.s in which the variation scale-length decreases rapidly with increasing t. Double precision calculation.

27. It can be seen that, although the error starts off small, it rises rapidly as the variation scale-length of the solution decreases (i.e., as t increases), and quickly becomes unacceptably large. Of course, we could reduce the error by simply reducing the step-length, h. However, this is a very inefficient solution. The step-length only needs to be reduced at large t. There is no need to reduce it, at all, at small t. Solution: h should be large at small t but needs to be reduced at large t

28. The step-length h should be increased if the truncation error per step is too small, and vice versa, in such a manner that the error per step remains relatively constant at 0.

29. Global integration errors associated with fixed step-length (h = 0.01), 4th order RK method (solid curve) and a corresponding adaptive method (0 = 10-8) (dotted curve), plotted against the independent variable, t, for a system of o.d.e.s in which the variation scale-length decreases rapidly with increasing t. Double precision calculation.

30. Differentiation An object is moving through space, its position as a function of time x(t) is recorded in a table. Problem: Determine the object’s velocity v(t)=dx/dt and acceleration a(t)=d2x/dt2

31. Method: Numeric Even a computer runs into errors with such a method because of its subtraction operations: the numerator tends to fluctuate between 0 and the machine precision  as the denominator approaches zero.

32. Method: Forward Difference (FD) c denotes a computed expression.

33. f(x) x x+h h FD: using two points to represent the 1st derivative function by a straight line in the interval from x to x+h

34. Error

35. Example of FD

36. FD solution This clearly becomes a good approximation only for small h, i.e., h << 2x

37. Method: Central Difference (CD)

38. f(x) x x+h/2 x-h/2 h CD: using two points to represent the function by a straight line in the interval from x-h/2 to x+h/2

39. Error

40. Example of CD

41. CD solution CD Method gives the exact answer regardless of the size of h !

42. Method:Extrapolated Difference (ED) • The error in FD ~ h • The error in CD ~ h2 • The error in ED ~ h4

44. f(x) x+h/2 x+h/4 x x-h/2 x-h/4 h/2 h

45. Extrapolated Difference

46. f(x) x+h/2 x+h/4 x x-h/2 x-h/4 h/2 h

47. Error

48. A Good Way of Computing for ED It reduces the loss of precision that occurs when large and small numbers are added together, only to be subtracted from other large numbers. Subtract the large number from each other and then add the difference to the small numbers !

49. Attention ! Regardless of the algorithm, evaluating the derivative of f(x) at x requires us to know the values of f surrounding x ! HOW ? Once we have the derivative of f(x) at x, USE the integration methods, ex., RK Method, to approximate the values of f surrounding x !

50. Error Analysis The approximation/truncation errors in numerical differentiation decrease with decreasing step size h while roundoff errors increase with a smaller step size. Total error is minimum if minimum. This occurs when