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RECURSION Lecture 6 CS2110 – Fall 2013. Overview references to sections in text 2  What is recursion? 7.1-7.39 slide 1-7  Base case 7.1-7.10 slide 13.

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งานนำเสนอเรื่อง: "RECURSION Lecture 6 CS2110 – Fall 2013. Overview references to sections in text 2  What is recursion? 7.1-7.39 slide 1-7  Base case 7.1-7.10 slide 13."— ใบสำเนางานนำเสนอ:

1 RECURSION Lecture 6 CS2110 – Fall 2013

2 Overview references to sections in text 2  What is recursion? slide 1-7  Base case slide 13  How Java stack frames work slide 28-32

3 Recursion 3  Arises in three forms in computer science  Recursion as a mathematical tool for defining a function in terms of its own value in a simpler case  Recursion as a programming tool. You’ve seen this previously but we’ll take it to mind-bending extremes (by the end of the class it will seem easy!)  Recursion used to prove properties about algorithms. We use the term induction for this and will discuss it later.

4 Pictorial examples 4 Droste effect Sierpinksi triangle Some examples of recursion (inside and outside CS): Garfield dreaming recursively. Recursive tree Recursion เป็นกระบวนการรูปแบบซ้ำ ๆ โดยที่มีรูปแบบ เหมือนเดิมแต่มีขนาดเล็กลง [CS1020 Lecture 10: Recursion]

5 Textual examples 5 Definitions based on recursion: Recursive definitions: 1.A person is a descendant of another if  the former is the latter’s child, or  the former is one of the descendants of the latter’s child. 2.A list of numbers is  a number, or  a number followed by a list of numbers. Recursive acronyms: 1.GNU = GNU’s Not Unix 2.PHP = PHP: Hypertext Preprocessor [CS1020 Lecture 10: Recursion]

6 Recursion as a math technique 6  Broadly, recursion is a powerful technique for specifying functions, sets, and programs  A few recursively-defined functions and programs  factorial  combinations  exponentiation (raising to an integer power)  Some recursively-defined sets  grammars  expressions  data structures (lists, trees,...)

7 Example: Sum the digits in a number 7  E.g. sum(7) = 7; sum(87012) = 2 + sum(8701); sum(87012) = 2+(1+(0+(7+8))) = 18 /** return sum of digits in n, given n >= 0 */ public static int sum(int n) { if (n < 10) return n; // { n has at least two digits } // return first digit + sum of rest return n%10 + sum(n/10); } sum calls itself!

8 Example: Is a string a palindrome? 8  isPal(“racecar”) = true  isPal(“pumpkin”) = false /** = "s is a palindrome" */ public static boolean isPal(String s) { if (s.length() <= 1) return true; // { s has at least 2 chars } int n= s.length()-1; return s.charAt(0) == s.charAt(n) && isPal(s.substring(1, n)); } racecar aceca cec e Substring from s[1] to s[n-1]

9 Count the e’s in a string 9  countEm(‘e’, “it is easy to see that this has many e’s”) = 4  countEm(‘e’, “Mississippi”) = 0 /** = “number of times c occurs in s */ public static int countEm(char c, String s) { if (s.length() == 0) return 0; // { s has at least 1 character } if (s.charAt(0) != c) return countEm(c, s.substring(1)); // { first character of s is c} return 1 + countEm (c, s.substring(1)); } Substring s[1..], i.e. s[1], …, s(s.length()-1)

10 The Factorial Function (n!) 10  Define n! = n·(n  1)·(n  2)···3·2·1 read: “n factorial” E.g. 3! = 3·2·1 = 6  Looking at definition, can see that n! = n * (n-1)!  By convention, 0! = 1  The function int  int that gives n! on input n is called the factorial function

11 The Factorial Function (n!) 11  n! is the number of permutations of n distinct objects  There is just one permutation of one object. 1! = 1  There are two permutations of two objects: 2! =  There are six permutations of three objects: 3! =  If n > 0, n! = n·(n  1)!

12 Recursion in CS1010: Factorial (2/2) 12 fact(n): if (n == 0) return 1; else return n * fact(n-1); fact(5) fact(4) 5* fact(3) 4* fact(2) 3* fact(1) 2* fact(0) 1* Tracing recursion [CS1020 Lecture 10: Recursion]

13 General Approach to Writing Recursive Methods หา parameter n ที่เราสามารถหาคำตอบของ n ด้วยการรวมคำตอบของปัญหาเดิมที่มีค่า n ที่ เล็กกว่า เช่น n! = n * (n-1)! นั่นคือ หาคำตอบ ของปัญหาใหญ่ จาก คำตอบของปัญหาที่ง่าย กว่า 2. หากรณีพื้นฐาน base case(s) – ค่าน้อยที่สุดของ n ที่เรารู้คำตอบแน่ ๆ (e.g. 0! = 1) นั่นคือ หาคำตอบในปัญหาที่ง่ายที่สุด ที่ใคร ๆ ก็ รู้ไม่ต้องคำนวณ 3. ตรวจสอบ method โดยใช้ค่า n ใด ๆ ดูว่า code จะถึงกรณีพื้นฐานหรือไม่

14 A cautionary note 14  จำไว้ว่า เมื่อ method เรียกตัวเอง method ที่ถูกเรียก จะมีตัวแปรเป็นของตัวเอง  จำไว้ว่า method ที่ค่า n สูงกว่า จะรอ method ที่มีค่า n น้อยกว่า return เสมอ  อย่าเปลี่ยนค่า ตัวแปร global ใน recursive method  แม้ว่าจะ compile ผ่าน แต่ มันจะมี error เยอะ  มันยาก

15 The Fibonacci Function 15  Mathematical definition: fib(0) = 0 fib(1) = 1 fib(n) = fib(n  1) + fib(n  2), n ≥ 2  Fibonacci sequence: 0, 1, 1, 2, 3, 5, 8, 13, … /** = fibonacci(n), for n >= 0 */ static int fib(int n) { if (n <= 1) return n; // { 1 < n } return fib(n-2) + fib(n-1); } two base cases! Fibonacci (Leonardo Pisano) 1170  1240? Statue in Pisa, Italy Giovanni Paganucci 1863

16 Recursive Execution 16 fib(4) fib(2) fib(0) fib(1) Execution of fib(4): fib(3) fib(0) fib(1) fib(2) /** = fibonacci(n), for n >= 0 */ static int fib(int n) { if (n <= 1) return n; // { 1 < n } return fib(n-2) + fib(n-1); }

17 Tracing recursive Fibonacci 17 Tracing recursive Fibonacci: Note the many duplicated fib()’s! The same computations are done over and over again! fib(4) fib(3) fib(2) fib(1) fib(2)fib(1) fib(2)fib(1) fib(3) fib(2) fib(6) fib(5) fib(4) Three duplicate calls to fib(3)! [CS1020 Lecture 10: Recursion]

18 ข้อสังเกต 18  วิธีคำนวณ Fibonacci elegant but inefficient  It “recomputes” answers again and again!  To improve speed, need to save known answers in a table!  One entry per answer  Such a table is called a cache fib(4) fib(2) fib(0) fib(1) fib(3) fib(0) fib(1) fib(2)

19 Memoization (fancy term for “caching”) 19  Memoization เป็นเทคนิคช่วยให้ โปรแกรมคำนวณไวขึ้น โดยพยายาม ไม่ให้เรียกคำนวณแก้ปัญหาเดิม ๆ ซ้ำ ๆ ที่เคยทำไปแล้ว  นั่นคือ พอคำนวณคำตอบได้เสร็จ, เรา ต้อง save ผลลัพธ์ไว้  ครั้งต่อไป ที่เราอยากได้คำตอบของ ปัญหาเดิม, เราก็แค่ไปดูที่เรา save ไว้

20 Adding Memoization to our solution  Before memoization: 20 static int fib(int n) { if (n <= 1) return n; else return fib(n-2) + fib(n-1); } /** For 0 <= k < cached.size(), cached[k] = fib(k) */ static ArrayList cached= new ArrayList (); The list used to memoize

21 /** For 0 <= k < cached.size(), cached[k] = fib(k) */ static ArrayList cached= new ArrayList (); static int fib(int n) { if (n < cached.size()) return cached.get(n); int v; if (n <= 1) v= n; else v= fib(n-2) + fib(n-1); if (n == cached.size()) cached.add(v); return v; } After Memoization This works because of definition of cached 21 This appends v to cached, keeping cached’s definition true

22 Notice the development process 22  We started with the idea of recursion  Created a very simple recursive procedure  Noticed it will be slow, because it wastefully recomputes the same thing again and again  So made it a bit more complex but gained a lot of speed in doing so  This is a common software engineering pattern

23 Why did it work? 23  This cached list “works” because for each value of n, either cached.get(n) is still undefined, or has fib(n)  Takes advantage of the fact that an ArrayList adds elements at the end, and indexes from size=5 cached.get(0) = 0 cached.get(1) = 1 … cached.get(n) = fib(n) Property of our code: cached.get(n) accessed after fib(n) computed

24 Iterative Fibonacci 24 int fib(int n) { if (n <= 2) return 1; else { int prev1=1, prev2=1, curr; for (int i=3; i<=n; i++) { curr = prev1 + prev2; prev2 = prev1; prev1 = curr; } return curr; }  Q: Which part of the code is the key to the improved efficiency? (1) Part A (red) (2) Part B (blue) [CS1020 Lecture 10: Recursion]

25 Non-Negative Integer Powers 25  a n = a·a·a···a (n times)  Alternative description:  a 0 = 1  a n+1 = a·a n /** = a n, given n >= 0 */ static int power(int a, int n) { if (n == 0) return 1; else return a*power(a,n-1); }

26 A Smarter Version 26  Power computation:  a 0 = 1  If n is nonzero and even, a n = (a*a) n/2  If n is nonzero, a n = a * a n-1 Java note: For ints x and y, “x/y” is the integer part of the quotient  We’ll see that judicious use of the second property makes this a logarithmic algorithm, as we will see Example: 3 8 = (3*3) * (3*3) * (3*3) * (3*3) = (3*3) 4

27 Smarter Version in Java 27  n = 0: a 0 = 1  n nonzero and even: a n = (a*a) n/2  n nonzero: a n = a·a n-1 /** = a**n, for n >= 0 */ static int power(int a, int n) { if (n == 0) return 1; if (n%2 == 0) return power(a*a, n/2); return a * power(a, n-1); }

28 Build table of multiplications 28 nnmults … Start with n = 0, then n = 1, etc., for each, calculate number of mults based on method body and recursion. See from the table: For n a power of 2, n = 2 k, only k+1 = (log n) + 1 mults static int power(int a, int n) { if (n == 0) return 1; if (n%2 == 0) return power(a*a, n/2); return a * power(a, n-1); } For n = 2 15 = 32768, only 16 mults!

29 Java “compiles” recursive code อย่างไร ? 29  Key idea:  Java ใช้ stack เพื่อจำ parameters และตัวแปรท้องถิ่น ของแต่ละ method calls  แต่ละการเรียก method จะได้รับ stack frame ของตัวเอง  stack frame จะเก็บ  ตัวแปรท้องถิ่นของ method  Parameters ของ method  ค่าที่ method return, ที่อยู่การ return

30 return info local variables parameters Stack Frame 30  stack frame ใหม่ถูก pushed เมื่อมี การเรียก method  stack frame ถูก popped เมื่อ method returns  โดยที่ค่าการ return จะอยู่ ที่ top ของ stack a stack frame retval a, n

31 Example: power(2, 5) 31 return info (a = ) 2 (n = ) 5 (hP = ) ? return info (a = ) 2 (n = ) 5 (hP = ) ? return info (a = ) 2 (n = ) 2 (hP = ) ? return info (a = ) 2 (n = ) 5 (hP = ) ? return info (a = ) 2 (n = ) 2 (hP = ) ? return info (a = ) 2 (n = ) 1 (hP = ) ? return info (a = ) 2 (n = ) 5 (hP = ) 4 return info (a = ) 2 (n = ) 5 (hP = ) ? return info (a = ) 2 (n = ) 2 (hP = ) 2 return info (a = ) 2 (n = ) 5 (hP = ) ? return info (a = ) 2 (n = ) 2 (hP = ) ? return info (a = ) 2 (n = ) 1 (hP = ) 1 (retval = ) 1 (retval = ) 2 (retval = ) 4 (retval = ) 32 hP: short for halfPower

32 How Do We Keep Track? 32  อาจจะมีหลาย frame ใน stack, แต่การคำนวณจะทำ เฉพาะข้อมูลที่อยู่บน top ของ stack เท่านั้น  frame ด้านล่างจะรอผลการ return จาก frame ด้านบน

33 public static void printLL(Node n) { if (n != null) { System.out.print(n.item); printLL(n.next); } Printing a Linked List recursively 33 printLL head Output: 589 printLL (head)  Q: How about printing in reverse order? Q: What is the base case? Example 3 [CS1020 Lecture 10: Recursion]

34 public static void printLL (Node n) { if (n!=null) { System.out.print(n.value); printLL (n.next); } 3.4 Printing a Linked List recursively in reverse order 34 printRev head Output: 58 Just change the name! … Sure, right! printRev(head)  9 printRev printRev(n.next); System.out.print(n.value); Example 4 [CS1020 Lecture 10: Recursion]

35 Sorted Linked List Insertion (1/2) 35 insert(p,v) p == null v < p.element v >= p.element v p.next p.element v Case 1: Tail insertion Case 2: Insert before p Case 3: Insert after p (recursion) p.element insert ( p.next,v) Insert an item v into the sorted linked list with head p Example 5 [CS1020 Lecture 10: Recursion]

36 Sorted Linked List Insertion (2/2) 36 public static Node insert(Node p, int v) { // Find the first node whose value is bigger than v // and insert before it. // p is the “head” of the current recursion. // Returns the “head” after the current recursion. if (p == null || v < p.item) return new Node(v, p); else { p.next = insert(p.next, v); return p; } To call this method: head = insert(head, newItem); Example 5 [CS1020 Lecture 10: Recursion]

37 Conclusion 37  Recursion is a convenient and powerful way to define functions  Problems that seem insurmountable can often be solved in a “divide-and-conquer” fashion:  Reduce a big problem to smaller problems of the same kind, solve the smaller problems  Recombine the solutions to smaller problems to form solution for big problem

38 Extra slides 38  For use if we have time for one more example of recursion  This builds on the ideas in the Fibonacci example

39 Combinations (a.k.a. Binomial Coefficients) 39  How many ways can you choose r items from a set of n distinct elements? ( ) “n choose r” ( ) = number of 2-element subsets of {A,B,C,D,E} 2-element subsets containing A: {A,B}, {A,C}, {A,D}, {A,E} 2-element subsets not containing A: {B,C},{B,D},{B,E},{C,D},{C,E},{D,E}  Therefore, = +  … in perfect form to write a recursive function! ( ) nrnr 5252

40 Combinations 40 = +, n > r > 0 = 1 ( ) nrnr n1rn1r n1r1n1r1 nnnn n0n = Pascal’s triangle Can also show that = ( ) nrnr n! r!(n  r)!

41 Binomial Coefficients 41 (x + y) n = x n + x n  1 y + x n  2 y 2 + ··· + y n =  x n  i y i ( ) nini nnnn n0n0 n1n1 n2n2 n i = 0 Combinations are also called binomial coefficients because they appear as coefficients in the expansion of the binomial power (x+y) n :

42 Combinations Have Two Base Cases 42  Coming up with right base cases can be tricky!  General idea:  Determine argument values for which recursive case does not apply  Introduce a base case for each one of these Two base cases = +, n > r > 0 = 1 ( ) nrnr n1rn1r n1r1n1r1 nnnn n0n0

43 Recursive Program for Combinations 43 /** = no. combinations of n things taking r at a time. Precondition: 0 <= r <= n */ static int combs(int n, int r) { if (r == 0 || r == n) return 1; //base cases else return combs(n-1,r) + combs(n-1,r-1); } = +, n > r > 0 = 1 ( ) nrnr n1rn1r n1r1n1r1 nnnn n0n0

44 Exercise for the reader (you!) 44  Modify our recursive program so that it caches results  Same idea as for our caching version of the fibonacci series  Question to ponder: When is it worthwhile to adding caching to a recursive function?  Certainly not always…  Must think about tradeoffs: space to maintain the cached results vs speedup obtained by having them

45 Something to think about 45  With fib(), it was kind of a trick to arrange that: cached[n] = fib(n)  Caching combinatorial values will force you to store more than just the answer:  Create a class called Triple  Design it to have integer fields n, r, v  Store Triple objects into ArrayList cached ;  Search cached for a saved value matching n and r Hint: use a foreach loop


ดาวน์โหลด ppt RECURSION Lecture 6 CS2110 – Fall 2013. Overview references to sections in text 2  What is recursion? 7.1-7.39 slide 1-7  Base case 7.1-7.10 slide 13.

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