1. Introduction to Data Structures and Algorithms Chapter 6 Recursion (1)

2. Recursion Recursion Definition Process: –Expand: A function calls itself with “smaller parameter” –Converge: A function with parameter x uses the computation result of function with x-1. When terminate the sequence of recursive calls? –Base case: »A function call with the smallest parameter needs no further calls; it simply returns result.

3. Sum of Squares int SumSquares(int m, int n) { if (m < n) return m*m + SumSquares (m+1,n); else return m*m; }

4. Factorials Computation N ! = N × (N-1) × (n-2) × … × 1 Another way to compute factorial? –Instead of computing factorial, computing multiplication Basic Idea (tree structure): –N ! = N × (N -1) ! –(N -1) ! = (N -1) × (N -2) ! –… –1! = 1

5. int factorial(int n) { int result =1; for (int j=n; j>=1; j--) result *= j; return result; } int factorial(int n) { if (n==1) return 1; else return ( n * factorial (n-1) ); }

6. Reversing a List Partition the list into –Head --- one node –Tail --- the rest of the list Reverse the head and the tail (recursively) Combine the two reverse lists

7. Functions getHead getTail Node getHead( List mlist), returns the first node List getTail (List mlist), returns the sublist that results after removing the first node List Concat (List m1, List m2), joins two sublists

8. Recursive Reverse List List reverseL (List mlist) { List head; List tail; if (mlist.first == null) return null; else { head = getHead(mlist); tail = getTail(mlist); return concat (reserseL (tail), head); }

9. Binary Search Binary Search Algorithm (Chapter 2): –Order array elements –Compare the value of middle element with target –Based on comparison result, change range and middle element –Keep doing that until a match is found Note that each time we apply same logic to different range and middle element Can it be done recursively? How? –Develop a function called recFind(key, LowerBond, Upper Bound) –If key < middle element recFind (key, LowerBound, Middle-1) –If key > middle element recFind (key, Middle+1, UpperBound)

10. private int recFind(long searchKey, int lowerBound, int upperBound) { int curIn; curIn = (lowerBound + upperBound ) / 2; if(a[curIn]==searchKey) return curIn; // found it else if(lowerBound > upperBound) return nElems; // can't find it else // divide range { if(a[curIn] < searchKey) // it's in upper half return recFind(searchKey, curIn+1, upperBound); else // it's in lower half return recFind(searchKey, lowerBound, curIn-1); } // end else divide range } // end recFind()

11. Anagrams All possible permutations of the input string Example: anagrams of cat –cat; cta; atc; act; tca; tac Problem: how to compute all anagrams –Loop idea: many levels of loop –Recursive Idea: Anagrams(S) = the first character + anagrams (S-1) –How many choices for the first character ? –Anagrams (S-1) is different for each possible choice

12. Anagramming Recursive Algorithm –Anagram the rightmost n-1 letters –Shift n letters to left by one postion cat – atc – tca -- cat –Repeat these steps n times Trace anagramming recursive algorithm for the word “cat” –Figure 6.7

13. public static void doAnagram(int newSize) { int limit; if(newSize == 1) // base case return; // go no further for(int j=0; j<newSize; j++) // for each position, { doAnagram(newSize-1); // anagram remaining if(newSize==2) // if innermost, displayWord(); // display it rotate(newSize); // rotate word } }

14. // rotate left all chars from position to end public static void rotate(int newSize) { int j; int position = size - newSize; char temp = arrChar[position]; // save first letter for(j=position+1; j<size; j++) // shift others left arrChar[j-1] = arrChar[j]; arrChar[j-1] = temp; // put first on right }

15. Tower of Hanoi Demo A number of disks placed on three columns –S: source column –I: intermediate column –D: destination column Rule: –no larger disk is allowed to place on top of smaller disks

16. Idea: –Sub-tree with fewer disks –Sub-trees are formed many times Recursive algorithm –Move the sub-tree consisting of the top n-1 disks from S to I –Move the remaining disk (largest) from S to D –Move the sub-tree from I to D

17. class TowersApp { static int nDisks = 3; public static void main(String[] args) { doTowers(nDisks, 'A', 'B', 'C'); } //----------------------------------------------------------- public static void doTowers(int topN, char src, char inter, char dest) { if(topN==1) System.out.println("Disk 1 from " + src + " to "+ dest); else { doTowers(topN-1, src, dest, inter); // src to inter System.out.println("Disk " + topN + " from " + src + " to "+ dest); doTowers(topN-1, inter, src, dest); // inter to dest } } //------------------------------------------------------------- } // end class TowersApp Trace Algorithm: Page 279

18. Running time analysis: –Depends on number of recursive calls Examples: –Factorial O(?) –Binary search O(?) –Anagram O(?)

19. Divide-and-Conquer approach divide a big problem into two smaller problems and solve them separately. The process continues until you get to the base case, which can be solved easily Divide-and-conquer approach usually involves two recursive calls, one for each smaller problems Lots of time, you will see O(n2) algorithm can be improved to O(nlogn) with Divide-and-conquer approach

20. Exercise 1: String length computation –Compute the length of an input string recursively Exercise 2: Teddy Bear Game –Initially, you have initial number of bears –Each step, you can do one of the followings: Ask for increment number of bears Give away half of bears if you have even number of bears goaln –Goal: reach goal number of bears within n steps –How do you know if you can reach goal or not?