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Recursion - see Recursion. RHS – SOC 2 Recursion We know that: –We can define classes –We can define methods on classes –Mehtods can call other methods.

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Presentation on theme: "Recursion - see Recursion. RHS – SOC 2 Recursion We know that: –We can define classes –We can define methods on classes –Mehtods can call other methods."— Presentation transcript:

1 Recursion - see Recursion

2 RHS – SOC 2 Recursion We know that: –We can define classes –We can define methods on classes –Mehtods can call other methods But…can a method call itself…?

3 RHS – SOC 3 Recursion public void callMe() { System.out.println(”Hello”); callMe(); }

4 RHS – SOC 4 Recursion Previous method definition was legal, but hardly useful… Just calling the same method will result in an infinite loop BUT what if we –Supply a parameter to the method –Change the parameter in each call –Stop calling ourselves for some specific value

5 RHS – SOC 5 Recursion public void callMe(int calls) { if (calls > 0) { System.out.println(”Hello”); int fewercalls = calls – 1; callMe(fewercalls); }

6 RHS – SOC 6 Recursion Calling methods like this is often called recursion The previous example could easily be rewritten as a ”traditional” loop, and would even be more efficient However, quite a lot of problems can be solved very elegantly by recursion

7 RHS – SOC 7 Recursion Example: the factorial function The factorial function F(n) is defined as: F(n) = n × (n-1) × (n-2) × … × 2 × 1 But you could also define F(n) as: F(n) = n × F(n-1)

8 RHS – SOC 8 Recursion public void factorial(int n) { int result = 1; for (int val = 1; val <= n; val++) { result = result * val; } return result }

9 RHS – SOC 9 Recursion public void factorial(int n) { if (n <= 1) return 1; else return (n * factorial(n-1)); }

10 RHS – SOC 10 Thinking recursively Thinking in terms of recursion may seem quite confusing at first However, one should try not to think about how it works in detail Think in terms of how a problem can be solved, by solving ”simpler” versions of the same problem

11 RHS – SOC 11 Thinking recursively Solving a problem by recursion: –Control step: Does the problem have a simple solution? –Division step: Split the problem into a simpler problem, plus a residual –Solution step: Solve the simpler problem –Combination step: Combine the solution to the simpler problem with the residual, in order to solve the original problem

12 RHS – SOC 12 Thinking recursively Solving the factorial function by recursion: Control step: –Is n < 1? If so, the result is 1 Division step: –Original problem: F(n) –Simpler problem: F(n-1) –Residual: n

13 RHS – SOC 13 Thinking recursively Solving the factorial function by recursion: Solution step: –Solve F(n-1) (go to top…) Combination step: –Combine by multiplying F(n-1) with n

14 RHS – SOC 14 Thinking recursively public void factorial(int n) { if (n <= 1) return 1; else return (n * factorial(n-1)); } Control Step Solution Step Combination Step Division Step

15 RHS – SOC 15 Thinking recursively A slightly harder problem is string permutations: –Given a string of text, find all possible permutations of the characters in the string –A string of length n will have n! different permutations

16 RHS – SOC 16 Thinking recursively The string ”cat” has 3! = 6 permutations: –”cat” –”cta” –”act” –”atc” –”tac” –”tca”

17 RHS – SOC 17 Thinking recursively Solving string permutations by recursion: Control step: –Does the string have length 1? If so, the result is the string itself

18 RHS – SOC 18 Thinking recursively Solving string permutations by recursion: Division step: –For each character c in the string: Let c be the residual Remove c from the original string. The resulting string is then the simpler problem

19 RHS – SOC 19 Thinking recursively Solving string permutations by recursion: Solution step: –For each string S generated during the division step: Find all permutations of S

20 RHS – SOC 20 Thinking recursively Solving string permutations by recursion: Combination step: –For each string S generated during the solution step: Combine S with the residual character c, by adding c to the front of S (permutation = c + S) Add the permutation to the result set

21 RHS – SOC 21 Thinking recursively Solving string permutation by recursion is not trivial… …but try to write an algorithm for string permutation without using recursion! Trust that it works! When there is a simple solution to simple inputs, and the problem can be solved by solving it for simpler inputs, it will work!

22 RHS – SOC 22 Recursive helper methods It is sometimes easier to solve a more general problem by recursion Example: How to find a palindrome (a string which is equal to its own reverse) Possible interface to a Sentence class: –Sentence(String theSentence) –boolean isPalindrome()

23 RHS – SOC 23 Recursive helper methods public boolean isPalindrome() { int len = text.length(); if (len < 2) return true; if ((text.substring(0,1).equals(text.substring(len-1,len))) { Sentence newSen = new Sentence(text.substring(1,len-1)); return newSen.isPalindrome(); } else return false; }

24 RHS – SOC 24 Recursive helper methods Previous implementation works, but is somewhat inefficient We create a lot of Sentence objects Let us include a helper method, that checks if a substring is a palindrome: boolean isPalindrome(int start, int end)

25 RHS – SOC 25 Recursive helper methods public boolean isPalindrome(int start, int end) { int len = end – start; if (len < 2) return true; if ((text.substring(start,start+1).equals( text.substring(end-1,end))) return isPalindrome(start+1,end-1); else return false; }

26 RHS – SOC 26 Recursive helper methods Finally, implement original method by using the helper method: public boolean isPalindrome() { return text.isPalindrome(0, lext.length() – 1); }

27 RHS – SOC 27 Exercises Review: R13.2, R13.3 Programming: P13.1, P13.4

28 RHS – SOC 28 Efficiency of recursion Recursion is a very elegant principle, but not always the correct strategy… Can result in very inefficient algorithms, if care is not taken Common pitfall is to calculate identical values over and over

29 RHS – SOC 29 Efficiency of recursion Example: Calculating Fibonacci numbers –Fib(1) = 1 –Fib(2) = 1 –Fib(n) = Fib(n-1) + Fib(n-2) Looks like a recursive solution is a no- brainer…

30 RHS – SOC 30 Efficiency of recursion public long fib(int n) { if (n <= 2) return 1; else return (fib(n-1) + fib(n-2)); }

31 RHS – SOC 31 Efficiency of recursion Method does work, but is very inefficient We calculate same values over and over Fib(n) Fib(n-1)Fib(n-2) Fib(n-3)

32 RHS – SOC 32 Recursion summary Recursion is a powerful and elegant tool for algorithm development Many algoritms are very easy to under- stand, if you understand recursion No silver bullet – recursive algorithms can be very slow Always consider (iterative) alternatives

33 RHS – SOC 33 Exercises Review: R13.6, R13.7 Programming: P13.6, P13.10

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