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Computer Science II Chapter 8

by: Gaoqi Zheng

Computer Science II Chapter 8 CSE 214

Marketplace > Stony Brook University > ComputerScienence > CSE 214 > Computer Science II Chapter 8
Gaoqi Zheng
Stony Brook U

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Chapter 8 - Recursion
Computer Science II
Ahmad Esmaili
Class Notes
Computer Science II
25 ?




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This 23 page Class Notes was uploaded by Gaoqi Zheng on Monday March 28, 2016. The Class Notes belongs to CSE 214 at Stony Brook University taught by Ahmad Esmaili in Spring 2016. Since its upload, it has received 28 views. For similar materials see Computer Science II in ComputerScienence at Stony Brook University.

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Date Created: 03/28/16
Recursion Revisited Chapter 8 Fundamentals • A method is recursive if it calls itself. • A recursive method should contain a stopping case or base case that is not recursive. (Without it, the method would be infinitely recursive, never ending.) • The recursive call(s) should be for simpler versions of the same problem. Activation Records • When a method calls another method (even itself), an activation record is stored on the system stack (of the O.S.). • An activation record contains: -where to return when the called method ends - parameter(s) passed to the called method - values of the method’s local variables • When a method returns, it uses the top activation record on the system stack to restore the conditions before the method call. Factorial public int factorial(int n) { if (n == 0) return 1; int x = factorial(n-1);  return location B return n * x; }  return location A result = factorial(4); System.out.println(result); Factorial Activation Record x n Return location Trace (Activation Record) ? 0 if (n=0) return 1; B x = factorial(n-1); ? 1 1 1 return n * x; B B 2 2 2 B B B ? ? ? 2 3 3 3 3 B B B B ? ? ? ? 6 4 4 4 4 4 A A A A A Trace (factorial) public int factorial(int n) { if (n == 0) return 1; int x = factorial(n-1); return n * x; } factorial 4 return 4 * 6 = 24 factorial 3 return 3 * 2 = 6 factorial 2 return 2 * 1 = 2 factorial 1 return 1 * 1 = 1 factorial 0 return 1 Activation Records(summary) • Hold return location. • Temporary storage for local variables including parameters, if any. • Basis for re-entrant code. Fibonacci Numbers public int fib(int n) { if (n == 0 || n == 1) return n; int x = fib(n-1);  return location B int y = fib(n-2);  return location C return (x + y); } result = fib(4);  return location A System.out.println(result); Fibonacci Numbers Activation Record y x n Return location f(6) f(5) f(4) f(4) f(3) f(3) f(2) f(3) f(2) f(2) f(1) f(2) f(1) f(1) f(0) f(2) f(1) f(1) f(0) f(1) f(0) f(1) f(0) f(1) f(0) Backtracking • An exhaustive search is a technique of generating a solution from all combinations of partial solutions. • If any step leads to an invalid solution or infeasible solution, we backtrack to the most recent partial solution and try a different path to a full solution until we find the best solution. Example: Searching a maze X X X X X X X X O X O X X O O X O X O O O O X O O O O X O X O O O X O X O X X O O X X O O O O O T X X X X X O X X O O O O O O o findPath(x,y): The trick findPath(x,y-1) findPath(x-1,y) findPath(x+1,y) X findPath(x,y+1) Initial Algorithm • Start from position x,y such that Maze[x][y] = o • if findPath(x,y) output TARGET FOUND else output TARGET NOT FOUND findPath(x,y) if Maze[x][y] = T output x,y return true This is a simplification in the algorithm. What’s else if Maze[x][y] = X missing? return false else (must be an O) Maze[x][y] = X if findPath(x-1,y) OR findPath(x,y-1) OR findPath(x+1,y) OR findPath(x,y+1) output x,y return true else return false Dynamic Programming • Reduce the number of recursive calls by saving the return values of recursive calls as they are determined. • Use the saved value in place of an identical recursive call later in the execution. • Example: let d[ ] be a global array that holds the previously determined Fibonacci values. Give these initial values of –1. Example : Matrix Chain Multiplication LetA be a 100 x 10 matrix 1 LetA b2 a 10 x 20 matrix LetA b3 a 20 x 30 matrix How many operations are required to multiply A 1 A x 2 ? 3 Fibonacci Numbers Revisited Using Dynamic Programming public int fib(int n) { if (n == 0 || n == 1) { return n; } if (d[n-1] == -1) d[n-1] = fib(n-1); if (d[n-2] == -1) d[n-2] = fib(n-2); return (d[n-1]+d[n-2]); } Tail Recursion • If a method is defined such that it has one recursive call as the last computational statement, then the method is called tail recursive. • Every tail recursive method can be re- written as an equivalent method without recursion using a loop. • Example: factorial is tail recursive. Factorial Revisited public int factorial(int n) { if (n == 0) return 1; return n * factorial(n-1);  tail } recursion public int factorial(int n) { int product = 1; int i; Why should for (i = n; i >= 1; i--) we try to product = i * product; eliminate return product; tail recursion? } Reverse Print public static void reversePrint(int n) { if (n > 0) { System.out.println(n); reversePrint(n-1);  tail } recursion } public static void reversePrint(int n) { L: if (n > 0) { while (n > 0) { System.out.println(n); System.out.println(n); Set up new parameters n--; Jump to L // no code necessary } } } T owers of Hanoi public static void move(int n, int src, int dest, int aux) { if (n > 0) { move(n-1, src, aux, dest); System.out.println(src+" "+dest); move(n-1, aux, dest, src); }  tail } recursion public static void move(int n, int src, int dest, int aux) { while (n > 0) { move(n-1, src, aux, dest); System.out.println(src+" "+dest); n = n-1; exchange(aux, src); } }


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