1. Numerical Methods in Scientific Computation Lecture 2 Programming and Software Introduction to error analysis Numerical Methods, Lecture 2 1 Prof. Jinbo Bi CSE, UConn

2. Packages vs. Programming Packages MATLAB Excel Mathematica Maple Packages do the work for you Most offer an interactive environment Numerical Methods, Lecture 2 2 Prof. Jinbo Bi CSE, UConn

3. MATLAB Software product from The MathWorks, Inc. Originally focused on matrix manipulations Interactive tool to do numerical functions, and visualization Commands can be saved into user scripts call and m-file Graphics and graphical user interfaces (GUI) are built into program Numerical Methods, Lecture 2 3 Prof. Jinbo Bi CSE, UConn

4. MATLAB Base product is extended with a variety of “toolboxes” Optimization Statistics Curve fitting Image processing Numerical Methods, Lecture 2 4 Prof. Jinbo Bi CSE, UConn

5. MATLAB Example: Find roots of >> p = [1 -6 -72 -27] >> r = roots (p) >> r = 12.1229 -5.7345 -0.3884 Numerical Methods, Lecture 2 5 Prof. Jinbo Bi CSE, UConn

6. Advantages: Large library of functions to evaluate a wide variety of numerical problems Interactive tool allows immediate evaluation of results Disadvantages Expensive Not as fast as C/C++/FORTRAN code MATLAB Numerical Methods, Lecture 2 6 Prof. Jinbo Bi CSE, UConn

7. Programming language concepts Interpreted Languages MATLAB Usually integrated with an interactive GUI Allows quick evaluation of algorithms Ideal for debugging and one-time analysis and in cases where high speed is not critical Numerical Methods, Lecture 2 7 Prof. Jinbo Bi CSE, UConn

8. Programming language concepts Compiled Languages FORTRAN, C, C++ Source code is created with an editor as a plain text file Programs then needs to be “compiled” (converted from source code to machine instructions with relative addresses and undefined external routines still needed). The compiled routines (called object modules) need then to be linked with system libraries. Faster execution than interpreted languages Numerical Methods, Lecture 2 8 Prof. Jinbo Bi CSE, UConn

9. Data representation Constants, variables, type declarations Data organization and structures Arrays, lists, trees, etc. Programming concepts Numerical Methods, Lecture 2 9 Prof. Jinbo Bi CSE, UConn

10. Mathematical formulas Assignment, priority rules Input/Output Stdin / Stdout, files, GUI Programming concepts Numerical Methods, Lecture 2 10 Prof. Jinbo Bi CSE, UConn

11. Structured programming Set of rules that prescribe good programming style Single entry point and exit point to all control structures Flexible enough to allow creativity and personal expression Easy to debug, test and update Programming concepts Numerical Methods, Lecture 2 11 Prof. Jinbo Bi CSE, UConn

12. Control structures Sequence Selection Repetition Computer code is clearer and easier to follow Structured programming Numerical Methods, Lecture 2 12 Prof. Jinbo Bi CSE, UConn

13. Flowcharts Visual representation Useful in planning Pseudocode Simple representation of an algorithm Basic program constructs without being language-specific Easy to modify and share with others Algorithm expression Numerical Methods, Lecture 2 13 Prof. Jinbo Bi CSE, UConn

14. Flowcharts Numerical Methods, Lecture 2 14 Prof. Jinbo Bi CSE, UConn

15. Sequence Implement one instruction at a time Control structures Numerical Methods, Lecture 2 15 Prof. Jinbo Bi CSE, UConn

16. Selection Branching IF/THEN/ELSE CASE/ELSE Control structures Numerical Methods, Lecture 2 16 Prof. Jinbo Bi CSE, UConn

17. Control structures Numerical Methods, Lecture 2 17 Prof. Jinbo Bi CSE, UConn

18. Control structures Numerical Methods, Lecture 2 18 Prof. Jinbo Bi CSE, UConn

19. Repetition DOEXIT loops (break loop) Similar to C while loop DOFOR loop (count-controlled) Control structures Numerical Methods, Lecture 2 19 Prof. Jinbo Bi CSE, UConn

20. Control structures pretest posttest midtest Numerical Methods, Lecture 2 20 Prof. Jinbo Bi CSE, UConn

21. Control structures Numerical Methods, Lecture 2 21 Prof. Jinbo Bi CSE, UConn

22. Break complicated tasks into manageable parts Independent and self-contained Well-defined modules with a single entry point and single exit point Always a good idea to return a value for status or error code Modular programming Numerical Methods, Lecture 2 22 Prof. Jinbo Bi CSE, UConn

23. Makes logic easier to understand and digest Allows black-box development of modules Requires well-defined interfaces with no side effects Isolates errors Allows for reuse of modules and development of libraries Modular programming Numerical Methods, Lecture 2 23 Prof. Jinbo Bi CSE, UConn

24. Specification: A clear statement of the problem, the requirements, and any special parameters of operation Algorithm/Design: A flow chart or pseudo code representation of how exactly how will the problem be solved. Software engineering approach Numerical Methods, Lecture 2 24 Prof. Jinbo Bi CSE, UConn

25. Implementation: Breaking the algorithm into manageable pieces that can be coded into the language of choice, and putting all the pieces together to solve the problem. Verification: Checking that the implementation solves the original specification. In numerical problems, this is difficult because most of the time you don’t know what the correct answer is. Software engineering approach Numerical Methods, Lecture 2 25 Prof. Jinbo Bi CSE, UConn

26. How do you solve the sum of integers problem? a)Simplest sum = 1 + 2 + 3 + 4 + 5 +.... + N Coded specifically for specific values of N. Example case Numerical Methods, Lecture 2 26 Prof. Jinbo Bi CSE, UConn

27. b)Intermediate solution Does not require much thought, takes advantage of looping ability of most languages: C/C++: MATLAB Possible solutions Numerical Methods, Lecture 2 27 Prof. Jinbo Bi CSE, UConn

28. c)Analytical solution Requires some thought about the sequence remember back to one of your math classes. Possible solutions Numerical Methods, Lecture 2 28 Prof. Jinbo Bi CSE, UConn

29. What can fail in the above algorithms. (To know all the possible failure modes requires knowledge of how computers work). Some examples of failures are: For (b): This algorithm is fairly robust. But, when N is large execution will be slow compared to (c) Verification of algorithm Numerical Methods, Lecture 2 29 Prof. Jinbo Bi CSE, UConn

30. Algorithm (c) This is most common algorithm for this type of problem, and it has many potential failure modes. For example: (c.1) What if N is less than zero? Still returns an answer but not what would be expected. (What happens in (b) if N is negative?). Numerical Methods, Lecture 2 30 Prof. Jinbo Bi CSE, UConn

31. Algorithm (c) (c.2) In which order will the multiplication and division be done. For all values of N, either N or N+1 will be even and therefore N*(N+1) will always be even but what if the division is done first? Algorithm will work half of the time. If division is done first and N+1 is odd, the algorithm will return a result but it will not be correct. This is a bug. For verification, it means the algorithm works sometimes but not always. Numerical Methods, Lecture 2 31 Prof. Jinbo Bi CSE, UConn

32. Algorithm (c) (c.3) What if N is very large? What is the maximum value N*(N+1) can have? There are maximum values that integers can be represented in a computer and we may overflow. What happens then? Can we code this to handle large values of N? Numerical Methods, Lecture 2 32 Prof. Jinbo Bi CSE, UConn

33. Verification By breaking the program into small modules, each of which can be checked, the sum of the parts is likely to be correct but not always.. Problems can be that the program only works some of the time or it may not work on all platforms. The critical issue to realize all possible cases that might occur. Numerical Methods, Lecture 2 33 Prof. Jinbo Bi CSE, UConn

34. Introduction to error analysis Error is the difference between the exact solution and a numerical method solution In most cases, you don’t know what the exact solution is, so how do you calculate the error Error analysis is the process of predicting what the error will be even if you don’t know what the exact solution Errors can also be introduced by the fact that the numerical algorithm has been implemented on a computer Numerical Methods, Lecture 2 34 Prof. Jinbo Bi CSE, UConn

35. Significant digits Can a number be used with confidence? How accurate is the number? How many digits of the number do we trust? Numerical Methods, Lecture 2 35 Prof. Jinbo Bi CSE, UConn

36. Significant digits Can the speed be estimated to one decimal place? Numerical Methods, Lecture 2 36 Prof. Jinbo Bi CSE, UConn

37. The significant digits of a number are those that can be used with confidence The digits that are known with certainty plus one estimated digit zeros are not always significant digits –0.0001845, 0.001845, 0.01845 –4.53x10 4, 4.530x10 4, 4.5300x10 4 Exact numbers vs. measured numbers Exact numbers have an infinite number of significant digits  is an exact number but usually subject to round-off Significant digits Numerical Methods, Lecture 2 37 Prof. Jinbo Bi CSE, UConn

38. Significant digits Numerical Methods, Lecture 2 38 Prof. Jinbo Bi CSE, UConn

39. Accuracy and precision Accuracy is how close a computed or measured value is to the true value Precision is how close individual computed or measured values agree with each other Reproducibility Inaccuracy/Bias vs. Imprecision/Uncertainty Inaccuracy: systematic deviation from the truth imprecision: magnitude of the scatter Numerical Methods, Lecture 2 39 Prof. Jinbo Bi CSE, UConn

40. Accuracy and precision Numerical Methods, Lecture 2 40 Prof. Jinbo Bi CSE, UConn

41. The level of accuracy and precision required depend on the problem Error to represent both the inaccuracy and imprecision of predictions Accuracy and precision Numerical Methods, Lecture 2 41 Prof. Jinbo Bi CSE, UConn

42. Two general types of errors Truncation errors – due to approximations of exact mathematical functions Round-off errors – due to limited significant digit representation of exact numbers E t = true value – approximation Error definitions Numerical Methods, Lecture 2 42 Prof. Jinbo Bi CSE, UConn

43. E t does not capture the order of magnitude of error 1V error probably doesn’t matter if you’re measuring line voltage, but it does matter if you’re measuring the voltage supply to a VLSI chip Therefore, its better to normalize the error relative to the value Error definitions Numerical Methods, Lecture 2 43 Prof. Jinbo Bi CSE, UConn

44. Example: Line voltage Chip supply voltage Error definitions Numerical Methods, Lecture 2 44 Prof. Jinbo Bi CSE, UConn

45. What if we don’t know the true value? Use an approximation of the true value How do we calculate the approximate error? Use an iterative approach Approximate error = current approximation – previous approximation Assumes that the iteration will converge Error definitions a Numerical Methods, Lecture 2 45 Prof. Jinbo Bi CSE, UConn

46. For most problems, we are interested in keeping the error less than specified error tolerance How many iterations do you do, before you’re satisfied that the result is correct to at least n significant digits? Error Definitions Numerical Methods, Lecture 2 46 Prof. Jinbo Bi CSE, UConn

47. Infinite series expansion of e x As we add terms to the expansion, the expression becomes more exact Using this series expansion, can we calculate e 0.5 to three significant digits? Example Numerical Methods, Lecture 2 47 Prof. Jinbo Bi CSE, UConn

48. Calculate the error tolerance First approximation Second approximation Error approximation Example Numerical Methods, Lecture 2 48 Prof. Jinbo Bi CSE, UConn

49. Third approximation Error approximation Example Numerical Methods, Lecture 2 49 Prof. Jinbo Bi CSE, UConn

50. Example Numerical Methods, Lecture 2 50 Prof. Jinbo Bi CSE, UConn

51. Next class HW1, due 9/5 Chapra & Canale 6 th Edition 1.8 (typo: dy/dx -> dy/dt), 1.12, 2.5 (choose order n=6), 2.14 Next class Continue on error analysis Numerical Methods, Lecture 2 51 Prof. Jinbo Bi CSE, UConn