1. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 1 ADT Implementation: Recursion, Algorithm Analysis, and Standard Algorithms Chapter 10

2. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 2 Chapter Contents 10.1 Recursion 10.2 Examples of Recursion: Towers of Hanoi; Parsing 10.3 Implementing Recursion 10.4 Algorithm Efficiency 10.5 Standard Algorithms in C++ 10.6 Proving Algorithms Correct (Optional)

3. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 3 Chapter Objectives Review recursion by looking at examples Show how recursion is implemented using a run-time stack Look at the important topic of algorithm efficiency and how it is measured Describe some of the powerful and useful standard C++ function templates in STL (Optional) Introduce briefly the topic of algorithm verification

4. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 4 Recursion A function is defined recursively if it has the following two parts An anchor or base case –The function is defined for one or more specific values of the parameter(s) An inductive or recursive case –The function's value for current parameter(s) is defined in terms of previously defined function values and/or parameter(s)

5. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 5 Recursive Example Consider a recursive power function double power (double x, unsigned n) { if ( n == 0 ) return 1.0; else return x * power (x, n-1); } Which is the anchor? Which is the inductive or recursive part? How does the anchor keep it from going forever?

6. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 6 Recursive Example Note the results of a call –Recursive calls –Resolution of the calls

7. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 7 A Bad Use of Recursion Fibonacci numbers 1, 1, 2, 3, 5, 8, 13, 21, 34 f 1 = 1, f 2 = 1 … f n = f n -2 + f n -1 –A recursive function double Fib (unsigned n) { if (n <= 2) return 1; else return Fib (n – 1) + Fib (n – 2); }

8. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 8 A Bad Use of Recursion Why is this inefficient? –Note the recursion tree

9. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 9 Uses of Recursion Binary Search –See source codesource code –Note results of recursive call

10. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 10 Uses of Recursion Palindrome checker –A palindrome has same value with characters reversed 1234321 racecar Recursive algorithm for an integer –If numDigiths <= 1 return true –Else check first and last digits num/10 numDigits-1 and num % 10 if they do not match return false –If they match, check more digits Apply algorithm recursively to: num % 10 numDigits-1 and numDigits - 2

11. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 11 Recursion Example: Towers of Hanoi Recursive algorithm especially appropriate for solution by recursion Task –Move disks from left peg to right peg –When disk moved, must be placed on a peg –Only one disk (top disk on a peg) moved at a time –Larger disk may never be placed on a smaller disk

12. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 12 Recursion Example: Towers of Hanoi Identify base case: If there is one disk move from A to C Inductive solution for n > 1 disks –Move topmost n – 1 disks from A to B, using C for temporary storage –Move final disk remaining on A to C –Move the n – 1 disk from B to C using A for temporary storage View code for solution, Fig 10.4Fig 10.4

13. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 13 Recursion Example: Towers of Hanoi Note the graphical steps to the solution

14. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 14 Recursion Example: Parsing Examples so far are direct recursion –Function calls itself directly Indirect recursion occurs when –A function calls other functions –Some chain of function calls eventually results in a call to original function again An example of this is the problem of processing arithmetic expressions

15. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 15 Recursion Example: Parsing Parser is part of the compiler Input to a compiler is characters –Broken up into meaningful groups –Identifiers, reserved words, constants, operators These units are called tokens –Recognized by lexical analyzer –Syntax rules applied

16. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 16 Recursion Example: Parsing Parser generates a parse tree using the tokens according to rules below: An expression: term + term | term – term | term A term: factor * factor | factor / factor | factor A factor: ( expression ) | letter | digit Note the indirect recursion

17. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 17 Implementing Recursion Recall section 7.4, activation record created for function call Activation records placed on run-time stack Recursive calls generate stack of similar activation records

18. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 18 Implementing Recursion When base case reached and successive calls resolved –Activation records are popped off the stack

19. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 19 Algorithm Efficiency How do we measure efficiency –Space utilization – amount of memory required –Time required to accomplish the task Time efficiency depends on : –size of input –speed of machine –quality of source code –quality of compiler These vary from one platform to another

20. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 20 Algorithm Efficiency We can count the number of times instructions are executed –This gives us a measure of efficiency of an algorithm So we measure computing time as: T(n)= computing time of an algorithm for input of size n = number of times the instructions are executed

21. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 21 Example: Calculating the Mean Task# times executed 1.Initialize the sum to 01 2.Initialize index i to 01 3.While i < n do followingn+1 4. a) Add x[i] to sumn 5. b) Increment i by 1n 6.Return mean = sum/n1 Total 3n + 4

22. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 22 Computing Time Order of Magnitude As number of inputs increases  T(n) = 3n + 4 grows at a rate proportional to n Thus T(n) has the "order of magnitude" n The computing time of an algorithm on input of size n,  T(n) said to have order of magnitude f(n),  written T(n) is O(f(n)) if … there is some constant C such that  T(n) < C  f(n) for all sufficiently large values of n

23. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 23 Big Oh Notation Another way of saying this: The complexity of the algorithm is O(f(n)). Example: For the Mean-Calculation Algorithm: T(n) is O(n) Note that constants and multiplicative factors are ignored.

24. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 24 Big Oh Notation f(n) is usually simple: n, n 2, n 3,... 2n 1, log 2 n n log 2 n log 2 log 2 n Note graph of common computing times

25. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 25 Big Oh Notation Graphs of common computing times

26. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 26 Common Computing Time Functions log 2 log 2 nlog 2 nnn log 2 nn2n2 n3n3 2n2n ---010112 0.00122484 1.00248166416 1.58382464512256 2.0041664256409665536 2.325321601024327684294967296 2.5866438440962621441.84467E+19 3.008256204865536167772161.15792E+77 3.321010241024010485761.07E+091.8E+308 4.32201048576209715201.1E+121.15E+186.7E+315652

27. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 27 Computing in Real Time Suppose each instruction can be done in 1 microsecond For n = 256 inputs how long for various f(n) FunctionTime log 2 log 2 n3 microseconds Log 2 n8 microseconds n.25 milliseconds n log 2 n2 milliseconds n2n2 65 milliseconds n3n3 17 seconds 2n2n 3.7+E64 centuries!!

28. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 28 STL's Algorithms STL has a collection of more than 80 generic algorithms. –not member functions of STL's container classes –do not access containers directly. They are stand-alone functions –operate on data by means of iterators. Makes it possible to work with regular C-style arrays as well as containers.

29. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 29 Example of sort Algorithm Comes in several different forms One uses the < operator to compare elements –Requires that operator<() be defined on the data type being sorted –Example program, Fig 10.7Fig 10.7 The sort algorithm can be used with arrays –See example, Fig. 10.8Fig. 10.8 Sort algorithm can have a third parameter –The name of a boolean function to be used for a less than –See Fig. 10.9Fig. 10.9

30. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 30 A Sample of STL Algorithms Designed to operate on a sequence of elements Designate a sequence by using two iterators –One positioned at the first element of the sequence –One positioned after the last element in the sequence Note Table 10-4, Page 571 of text

31. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 31 Algorithms from Library Contains function templates that operate on sequential containers Intended for use with numeric sequences –Such as valarrays See Table 10-5, page 572

32. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 32 Using Algorithms Consider the judging of figure skaters –When judges give scores the high and low score are discarded –Then the remaining scores are averaged Given the vector of scores shown below

33. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 33 Using Algorithms Use min_element algorithms to find and discard minimum score scores.erase(min_element(scores.begin(), scores.end()))); Discard maximum element similarly Now use accumulate() algorithm double avg = accumulate(scores.begin(), scores.end(),0.0)/scores.size();

34. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 34 Proving Algorithms Correct Deductive proof of correctness may be required –In safety-critical systems where lives at risk Must specify –The "given" or preconditions –The "to show" or post conditions Pre and Algorithm => Post

35. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 35 Deduction ( 演繹法 ; indicator, “ ⇨” ) Deduction, a kind of inference form –by reasoning from the general to the specific. Deduction (in logic) form of inference such that the conclusion must be true if the premises are true. –The famous Aristotelian syllogism is one species of deductive reasoning.syllogism –For example, if we know that all men have two legs and that John is a man, it is then logical to deduce that John has two legs. All men have two legs. John is a man. ⇨ John has two legs.

36. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 36 Induction ( 歸納法 ) Induction (logic): –The process of deriving general principles (i.e., rules) from particular facts or instances. –A conclusion reached by this process. induction, in logic, a form of argument in which the premises give grounds for the conclusion but do not necessitate it.logic –An important form of induction is the process of reasoning from the particular to the general.

37. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 37 Example: Recursive Power Function Function: double power (double x, unsigned n) { if ( n == 0 ) return 1.0; else return x * power (x, n-1); } Precondition: –Input consists of a real number x and a nonnegative integer n Postcondition: –Execution of function terminates –When it terminates value returned is x n

38. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 38 Example: Recursive Power Function Use mathematical induction on n –Show postcondition follows if n = 0 Assume for n = k, execution terminates and returns correct value –When called with n = k + 1, inductive case return x * power (x, n – 1) is executed –Value of n – 1 is k –It follows that with n = k + 1, returns x * x k which equals x k+1

39. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 39 Notation could be used to state assertions Pre { Post = Pre (v, e ) } "If precondition Pre holds before an assignment statement S of the form v = e is executed, then the postcondition Post is obtained from Pre by replacing each occurrence of variable v by expression e " Formal deductive system can be used to reason from one assertion to next Proving Algorithms Correct S

40. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 40 Proving Algorithms Correct (cont.) A Very Simple case Pre  A  Post More complex case Pre  A 1  A 2  …  A n  Post General cases might even contain many selection paths (e.g., even more complex) S AiAi A i+1

41. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 41 Logic – validity vs. truth Logic, concerned with reasoning and validity of arguments – in general, in logic, we are not concerned with the truth of statements, but rather are concerned with their validity. –That is, although the following arguments (syllogism; 三段 論 ) is clearly logically, it is not something that we would considered to be true. All lemons are blue. Mary is lemon. Therefore, Mary is blue. –This set of statements is considered valid because the conclusions (Mary is blue) follows logically from the other two statements, which we often call the premises.

42. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 42 Logic – validity vs. truth (cont.)  inference is not directly related to truth –i.e. we can infer a sentence provided we have rules of inference that produce the sentence from the original sentences. For example, Black list: blocking all SMTP relays without reverse DNS ( heuristic; false positive). While list: accepting all messages with known envelope From address (heuristic; false negative)

43. Nyhoff, ADTs, Data Structures and Problem Solving with C++, Second Edition, © 2005 Pearson Education, Inc. All rights reserved. 0-13-140909-3 43 Deduction vs. Induction Deduction has to do with necessity; induction has to do with probability. –Logicians contrast deduction with induction, in which the conclusion might be false even when the premises are true. Facts: Lots of spam mails are originated from Internet sites without reverse DNS mappings. –Induction rule (valid but not necessarily true): E-mail messages relayed through incoming MTAs/MUAs, without reverse DNS mappings, will be considered as SPAM messages. Specific-to-general; maybe false positive –Deduction rule (general principle, or policy): E-mail messages relayed through incoming MTAs (or from NCTU MUAs), without reverse DNS mappings, should be blocked (i.e., considered as SPAM messages. ) General-to-specific