Chapter 19 : Recursion

An Introduction to Programming and Object

Oriented Design using Java2nd Edition. May 2004

Jaime NiñoFrederick Hosch

2May 2004 NH-Chapter 19

Objectives

After studying this chapter you should understand the following: recursion, and the relationship between recursive and

iterative algorithms;

the design of the quick sort algorithm;

indirect recursion;

backtracking and the class of problems it solves;

object recursion and its class structure.

Objectives

Also, you should be able to: write simple recursive algorithms;

use backtracking in the solution to problems;

structure class definitions to use object recursion.

Iterative Algorithms

Algorithm Specifies a step in the solution process.

Step is iterated.

After each iteration, we are closer to solution.

Solution is reached after a finite number of iterations.

Recursion

Algorithms Solve a trivial, basic case of problem,

Solution to general case is reduced to one that is a step closer to basic case.

Iteration v.s. Recursion

Reducing general case to a easier case roughly corresponds to a single iterative step.

Reaching base case stops recursion as exit condition stops iteration.

Iteration v.s. Recursion

In an iterative solution step toward solution until while condition is false. explicitly drive repetition with a loop.

In a recursive solution, reduce or “unwind” problem until base case. write a solution for base case and for reduction step.

Recursive solution form

if ( trivial case )

solve directly

solve in terms of a slightly easier case

Sorting a list

Trivial case size of list is one or zero.

General case Size of list is greater than 1.

Slightly easier problem reduce general case to sort a list with one fewer element.

Sorting a list

if ( list is empty or has one element )solution is easy: do nothing

elsesort the list, assuming a way of sorting a listwith one fewer element is available

Recursive algorithm implementation

Trivial case Solved directly.

General case algorithm invokes itself to solve a slightly reduced case. solution is built from solution of slightly reduced case.

Recursive algorithm execution

Results in a chain of self-calls, each a slightly easier problem to solve that previous.

Finally method is invoked with trivial case.

Recursive algorithm correctness

Must guarantee that general case will eventually reduce to basic case.

Example: Exponentiation

public static int power (int number, int exponent)The specified number raised to the specified power.

require:exponent >= 0

Exponentiation iterative solution

Invariant: result equals number raised to the count power. Exponent requirement to be >= 0 ensures iteration will terminate.

public static int power (int number, int exponent) {

int result = 1;

int count = 0;

while (count != exponent) {

result = number * result;

count = count + 1;

return result;

Exponentiation recursive solution

Base cases: raise integer to the power 0.

General case: raise integer to the power n, n is an integer and n > 0.

Reduction step: raising a number to the power n-1.

Compute number to the power n, assuming we have already computed number to the power n-1. If we have numbern-1, multiply this value by number to get numbern.

number n-1 is gotten by a self-call: power (number, n-1)

/** * The specified number raised to the specified power.

* require: exponent >= 0 */public static int power (int number, int exponent) {int result;if (exponent == 0)

result = 1;else

result = number * power(number,exponent-1);return result;

Tracing recursion: invoking power(2,3)

if (exponent == 0)

result = 1;

result = number * power(number,exponent-1);

return result;

number

exponent

result

Tracing recursion : invoking power(2,3)

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

invoke

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

invoke

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

invoke

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

Tracing recursion:resuming power(2,1)

if (exponent == 0)

result = 1;

return result;

number

exponent

result

Result of previous call

Tracing recursion :resuming power(2,1)

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

if (exponent == 0)

result = 1;

return result;

number

exponent

result

Finding the minimum element: Recursive version

public int minFinalExam (List<Student> students)

The lowest final exam grades of the specified Students.

require:students.size() > 0

Base case: find smallest on list containing one element.

General case: find smallest on list with n elements, where n > 1.

Reducing general case: find smallest on list with n-1 elements.

how to find smallest of n elements, assuming we know how to find smallest of n-1 elements.

Find smallest of the n-1 elements after first.

Find smallest of this and the first.

private int minFinalExam (List<Student> students, int first)

The lowest final exam grades of Students on the list with indexes greater than or equal to first.

require:0 <= first && first < students.size()

public int minFinalExam (List<Student> students) {

return minFinalExam(students,0);

private int minFinalExam (List<Student> students,int first) {

int smallest;

int gradeOfFirst = students.get(first).finalExam();

if (first == students.size()-1) {

smallest = gradeOfFirst; // the base case

else {// the general case:

int minOfRest = minFinalExam(students,first+1);

if (minOfRest < gradeOfFirst)

smallest = minOfRest;

smallest = gradeOfFirst;

return smallest;

Selection sort: recursive version

Base cases: sort an empty list or with 1 element.

General case: sort a list containing n elements, n > 1.

Reducing general case: sort a list containing n-1 elements.

Find the smallest element and put it in first place. Sort the remaining n-1 elements.

Note: sorting the remaining n-1 elements, refers to a segment of the list

remaining n-1 elements

private static <Element> void selectionSort (List<Element> list, int first, Order<Element> order)

{if (first < list.size()) {

find the smallest element and put it first;sort the remaining n-1 elements

private static <Element> void selectionSort (List<Element> list, int first, Order<Element> order)

{if (first < list.size()) {

int small = smallestOf(list,first,list.size()-1,order);

interchange(list,first,small);selectionSort(list,first+1,order);

public static <Element> void selectionSort (List<Element> list, Order<Element> order) {

selectionSort(list,0,order);}

Towers of Hanoi move stack of disks from starting peg to one of the

other pegs. Disks are moved one at a time, and a disk can never be placed on top of a smaller one.

Example of moves with 3 discs

PuzzleSolver method moveTower

public void moveTower (int size, int from, int to)Move a tower of the specified number of disks from the specified starting peg to the

specified destination peg.

require:size >= 11 <= from && from <= 31 <= to && to <= 3from != to

It uses a private method to move one disc:

private void moveDisk (int from, int to)

Move a single disk from the specified peg to the specified destination.

require:1 <= from && from <= 3

1 <= to && to <= 3from != to

Base case: a tower with 1 disc.

General case: a tower with n discs.

Reduction case: move a tower with n-1 discs.

Reduce problem of moving n disks to problem of moving n-1:

Move n-1 disks from the starting peg to the “other” peg. Move a disk from the starting peg to the destination peg.

Move n-1 disks from the “other” peg to the destination peg.

To find “other” peg subtract source peg number (from) and destination peg

number (to) from 6 (= 1 + 2 + 3).

PuzzleSolver : moveTower implementation

public void moveTower (int n, int from, int to) {

if (n == 1) {moveDisk(from,to);

} else {int other = 6-from-to; // not from or tomoveTower(n-1,from,other);moveDisk(from,to);moveTower(n-1,other,to);

PuzzleSolver: moveDisk

Simply solution:

private void moveDisk (int from, int to) {

System.out.println("Move a disk from peg " + from +" to peg " + to + '.');

Not very satisfactory. PuzzleSolver is a model class;

should be independent of the user interface.

Better way to implement moveDisk Use the Observer pattern. When output client is notified, pass move to make:

public class MoveA move in the Towers puzzle. Pegs are numbered 1, 2, 3.

public Move (int from, int to)Create a move of a disk from the peg from to the peg to.

public int from ()Peg the disk is moved from.

public int to ()Peg the disk is moved to.

private void moveDisk (int from, int to) {setChanged();notifyObservers(new Move(from, to));

Quicksort

Quicksort 1. puts an arbitrary element in proper position,

2. smaller elements are below it.

3. larger elements are above it.

Sublists 2, 3 are recursively sorted.

Quicksort

Implement a private method to sort a list segment. sort calls method with entire list:

public static <Element> void quickSort (List<Element> list, Order<Element> order) {quickSort(list,0,list.size()-1,order);

//Sort list elements indexed first through last.// require 0 <= first && last < list.size()private static <Element> void quickSort (

List<Element> list, int first, int last,Order<Element> order) {…

Quicksort

Base case an empty list or a list with a single element.

The general case is handled by the three steps: 1. puts an arbitrary element in proper position,

2. smaller elements are below it.

3. larger elements are above it.

Sublists 2, 3 are sorted.

Quicksort: partition

This method positions pivot such that: Smaller elements in list are below it. Larger elements in list are above it.

Reports where pivot was placed.

Quicksort

private static <Element> void quickSort (List<Element> list, int first, int last,Order<Element> order) {

if (first < last) { int position; // pivot index position = partition(list,first,last,order); quickSort(list,first,position-1,order); quickSort(list,position+1,last,order);}

Choose middle element of sublist as pivot element.

1 2346 28

(5) (6) (8)(7)

18 3073 19

(10) (11) (13)(12)

61 1241 45

(0) (1) (3)(2)

move the pivot element into the last position

1 2346 56

(5) (6) (8)(7)

18 3073 19

(10) (11) (13)(12)

61 1241 45

(0) (1) (3)(2)

L ?L L

(5) (6) (8)(7)

? ?? ?

(10) (11) (13)(12)

s Ls s

(0) (1) (3)(2)

i, pi start at 0 Compare i-th entry with pivot

1 2346 56

(5) (6) (8)(7)

18 3073 19

(10) (11) (13)(12)

61 1241 45

(0) (1) (3)(2)

Compare i-th entry with pivot.

1 2346 56

(5) (6) (8)(7)

18 3073 19

(10) (11) (13)(12)

61 1241 45

(0) (1) (3)(2)

1 2346 56

(5) (6) (8)(7)

18 3073 19

(10) (11) (13)(12)

61 1241 45

(0) (1) (3)(2)

Compare i-th entry with pivot. It’s less. Swap and increment indices.

1 2346 56

(5) (6) (8)(7)

18 3073 19

(10) (11) (13)(12)

61 1241 45

(0) (1) (3)(2)

1 2346 56

(5) (6) (8)(7)

18 3073 19

(10)(11) (13)(12)

12 6141 45

(0) (1) (3)(2)

1 2346 56

(5) (6) (8)(7)

18 3073 19

(10)(11) (13)(12)

12 6141 45

(0) (1) (3)(2)

41 2346 56

(5) (6) (8)(7)

18 3073 19

(10)(11) (13)(12)

12 611 45

(0) (1) (3)(2)

41 2346 56

(5) (6) (8)(7)

18 3073 19

(10)(11) (13)(12)

12 611 45

(0) (1) (3)(2)

Compare i-th entry with pivot. It’s less. Swap and increment.

41 2346 56

(5) (6) (8)(7)

18 3073 19

(10)(11) (13)(12)

12 611 45

(0) (1) (3)(2)

41 4546 56

(5) (6) (8)(7)

18 3073 19

(10)(11) (13)(12)

12 611 23

(0) (1) (3)(2)

41 4546 56

(5) (6) (8)(7)

18 3073 19

(10)(11) (13)(12)

12 31 23

(0) (1) (3)(2)

41 4546 56

(5) (6) (8)(7)

55 3073 19

(10)(11) (13)(12)

12 31 23

(0) (1) (3)(2)

Compare i-th entry with pivot. It’s less Swap and increment indices.

41 4546 56

(5) (6) (8)(7)

55 3073 19

(10)(11) (13)(12)

12 31 23

(0) (1) (3)(2)

19 4546 56

(5) (6) (8)(7)

55 3073 41

(10)(11) (13)(12)

12 31 23

(0) (1) (3)(2)

Loop exited. Swap pivot with pi.

19 4546 56

(5) (6) (8)(7)

55 3073 41

(10)(11) (13)(12)

12 31 23

(0) (1) (3)(2)

19 4528 56

(5) (6) (8)(7)

55 3073 41

(10)(11) (13)(12)

12 31 23

(0) (1) (3)(2)

partition implementation

private static <Element> int partition (List<Element> list, int first, int last, Order<Element> order) {

int pi; // pivot indexint i; // index of the next to examineElement pivot; // pivot itemint mid = (first+last)/2;pivot = list.get(mid);interchange(list,mid,last); // put pivot item at end.pi = first;i = first; while (i != last) { // list.get(last) is pivot item

if (order.inOrder(list.get(i),pivot)) {interchange(list,pi,i);pi = pi+1;

}i = i+1;

}interchange(list,pi,last); // put pivot item in placereturn pi;

Indirect Recursion

Method m1 invokes m2 which invokes m3 … which invokes mn which invokes m1.

Indirect Recursion: RemoveSet

Given a balanced string of parentheses as argument, removes the first balanced substring from the front.

Examples removeSet("()") "" removeSet("()()()()") "()()()" removeSet("((()))") "" removeSet("(()()(()()))()(())") "()(())"

RemoveSet design

Remove first “(“ Invoke reduceClosed for matching “)”

private String reduceClosed (String s)A String equal to specified String with first substring with one more closed parenthesis than open parenthesis removed.

RemoveSet design

For example, reduceClosed(")") "“

reduceClosed(")()()") "()()“

reduceClosed("(()()))(()) "(())“

reduceClosed("()()(()()))()(())") "()(())"

RemoveClosed design

Basic case: first character is “)”.

General case : first character of s is open parenthesis; then s starts with balanced substring. Remove balanced substring, Recursively apply method to what’s left.

RemoveClosed design

Use removeSet to remove balanced substring :

private String reduceClosed (String s) {

if (head(s) == ')')

return tail(s);

else // head(s) == '(‘, remove balanced set

return reduceClosed(removeSet(s));

Backtracking

Algorithmic technique for solving problems that cannot be solved directly.

Backtracking is used when a large set of possible solutions must be examined to find an actual solution to the problem.

Backtracking

For backtracking to be an appropriate technique, problem solution must be: a composite, made up of parts;

constructible in a series of steps, with each step adding a piece to the solution.

At each step there will several possible ways of extending the partial solution of the previous step. Some of these alternative will lead to a solution, others will not.

Backtracking example

suppose we have a maze consisting of a set of rooms connected by passages.

Each room is connected to one or more passages. A passage may lead to another room, or may lead to

the maze exit.

For instance, a maze with seven rooms might look like this, where the rooms are lettered and the doors are labelled north, south, east, west.

Problem: find a path from a given room to the exit.

Note solution is composite: “to reach the exit from room F, go south to room D, west

to room C, east to room E, east to the exit.”

The path can be constructed in a series of steps, each step adding a component to the partial solution built in the previous step.

At each step, there may be several alternatives.

For example, the first step in the above solution is “go south to room D.”

This partial solution can be extended in two ways: “go west to room C” or “go south to room G.”

The first option leads to a solution, while the second does not. Room G is a dead end.

Backtracking works by repeatedly extending a partial solution until a solution is found or a “dead end” is reached.

A dead end is simply a state that cannot be further extended.

If a dead end is reached, the algorithm “backs up” to the most recent point at which untried possibilities exist, and tries one.

To see how this works, suppose we are trying to reach the exit from room A in the above maze.

room A:go north to room B:

dead end; back up to room A.go south to room C:

go north to room D:go east to room F

dead end; back up to room D.go south to room G

dead end; back up to room D.no more choices (dead end); back up to

room Cgo east to room E:

go east to exit.

Implementing backtracking

Implement a backtracking algorithm for the maze traversal problem.

A Maze is composed of a number of Rooms, with each Room connected to a number of other Rooms.

public List<Room> connections ()

The list of Rooms this Room is connected to.

public boolean isExit ()

This Room is a maze exit.

public class PathPointA Room and a connection from the Room.

public PathPoint (Room room, int connection)

Create a new PathPoint.

require:0<=connection&&connection <

room.connections().size()

Implementing backtracking A path is a list of PathPoints modeling a sequence of connected

Rooms. If PathPoint [B,j] follows [A,i] on list, then connection i from

Room A leads to Room B. That is, A.connections(i) == B. Require: path has no repeated Rooms. A Path from Room A to H is : [(A,1),(C,2),(E,1)]

Implementing backtracking write a method that will produce an exit path from a

specified Room:

public List<PathPoint> exitPathFrom (Room room)A path leading from the specified Room to an exit; returns null if no path exists.

use an auxiliary method that takes a partial solution and extends it.

private List<PathPoint> extendedPathFrom (Room room, List<PathPoint> path)

The specified path extended from the specified Room to an exit. Returns null if the path cannot be extended to an exit. The specified path must be empty or lead to the specified Room.

Implementing backtracking The public method calls the auxiliary method with an

empty initial path:

public List<PathPoint> exitPathFrom (Room room) {

return extendedPathFrom(room, new

DefaultList<PathPoint>());}

Implementing backtrackingprivate List<PathPoint> extendedPathFrom ( Room room, List<PathPoint> path) { if (room.isExit()) return path; else { boolean found = false; List<PathPoint> solution = null; int i = 0;

while (i < room.connections().size() && !found){ // get a Room to extend the path Room nextRoom = room.connections().get(i);

if (!pathContains(path, nextRoom)) { List<PathPoint> extendedPath = path.copy().add(new PathPoint(room,i));

solution = extendedPathFrom(nextRoom,extendedPath); found = solution != null; }

i = i+1; } return solution; }}

Object recursion

Examine another form of recursion, called structural recursion or object recursion.

Structural recursion uses object structure in a recursive manner to derive problem solutions.

Object recursion

Two flavors of solver objects: General problem solver, the trivial problem solver.

We make these children of a common abstract parent:

GeneralSolverTrivialSolver

Solver

«interface»

Object recursion

Construct an odometer-like counter consisting of a sequence of digits. A digit turning over from 9 to 0 causes its left neighbor to increment

A solution is simply a stable state of the counter, one in which all digits are set. to find first solution: that is, reset all digits to 0. to find next solution: increment the counter by 1. There is a final solution, all digits are 9. We specify that attempting to find the next solution after the final state

will fail: i.e., a counter that is all 9s will not turn over to all 0s.

Object recursion

Digit: general solver class. Each Digit instance is responsible for a single digit of the

solution. Each Digit has an associate, the Digit to its left. A Digit extends solution provided by its associate. Digits are “linked together” to form the counter. The high-order (left-most) Digit has a NullDigit as its

associate. (it needs no associate).

NullDigit trivial solver class. The logic in the NullDigit simply ends the recursion.

Object recursion: DigitCounter

associate

DigitNullDigit

VirtualDigit

«interface»

Note: a Digit and its associate have the same structure.

NullDigitDigit

associate

public class DigitCounter {

private VirtualDigit lowDigit; // right-most digit

/** * Create a new DigitCounter with the specified * number of digits. * @require : digits >= 1 */public DigitCounter (int digits) {

VirtualDigit d = new NullDigit();for (int i = 1; i <= digits; i = i+1)

d = new Digit(d);lowDigit = d;

public void first () {lowDigit.first();

public void next () {lowDigit.next();

public boolean solved () {return lowDigit.solved();

public String toString () {return lowDigit.toString();

//definition of VirtualDigit interface // Digit class, NullDigit class.}//end DigitCounter

private interface VirtualDigit {

public boolean solved ();

public void first ();public void next ();public String toString

DigitCounter contains a private interface implemented by Digit and NullDigit.

Object recursion: Digit

Digit classImplements VirtualDigit interface.

Contains an int as data component for the digit.

When given command first, instructs left neighbor to find first number, and then sets itself to 0.

When given the command next, it increments its digit if it’s not 9. If its digit is 9, it instructs its left neighbor to increment, and sets itself to 0. In each case checks neighbor was successful.

Object recursion: Digitprivate class Digit implements VirtualDigit { private VirtualDigit associate; // left nghb private boolean solved; // valid num private int digit; // my digit

public Digit (VirtualDigit associate) {this.associate = associate;this.digit = 0;this.solved = false;

} public boolean solved () {

return solved; } public void first () {

associate.first();if (associate.solved()) {

digit = 0;solved = true;

} elsesolved = false;

public void next () {if (digit < 9) { digit = digit + 1; solved = true;} else { associate.next(); if (associate.solved()) {

digit = 0;solved = true;

} elsesolved = false;

public String toString () { if (solved) return associate.toString() + digit; else return "No solution";}

}//end Digit

Object recursion: NullDigitprivate class NullDigit implements VirtualDigit {

private boolean solved;public NullDigit () {

this.solved = false;}public boolean solved () {

return solved;}public void first () {

solved = true;}public void next () {

solved = false;}public String toString () {

if (solved)return "";

elsereturn "No solution";

Summary

Introduced the problem solving technique of recursion.

Two forms of recursive computation: algorithmic recursion, in which a method invokes itself

directly or indirectly, object recursion, based on the creation of composite object

structure in which the structure of a component is the same as that of the composite object.

Summary

Algorithmic recursion solution logic depends on identifying simple base cases that can be easily solved

directly, and describing how to reduce the general case to one that is

slightly simpler: that is, slightly closer to a base case.

The method that actualizes this logic invokes itself to solve the slightly simpler case to which the general case is reduced.

Summary

A well-known algorithm using direct recursion is quick sort.

Quick sort is much more efficient that the selection and bubble sorts generally requiring on the order of nlog2n steps to sort a list of n elements.

Quick sort works by positioning an arbitrary element, with smaller elements below and

larger elements above. list segments containing the smaller elements and the larger elements

are then recursively sorted.

Summary

Indirect recursion, a method is invoked before it returns through a sequence of other method invocations. That is, method m1 invokes m2 which invokes m3 … which invokes mn which invokes m1.

When indirect recursion is used, we generally set a collection of mutually recursive methods.

Summary

Backtracking is an algorithmic technique for examining a set of possible solutions to a problem in an organized way in order to find an actual solution.

Solution to problem is a composite, made up of several pieces.

Each step in a backtracking algorithm attempts to extend partial solution produced by the previous step.

If not possible to extend partial solution, algorithm “backs up” to find another partial solution to extend.

Summary

Using object recursion, a solver object is structured with a similar solver object as an associate.

Solver object delegates the responsibility of solving a simpler version of the problem to its associate, and then extends the solution provided by the associate.

Recursive nature of approach is seen here : a solution to the general case is found by using a solution to a simpler case given by the associate.

Chapter 19 : Recursion

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Chapter 8. Recursion - Izobrazevalna delavnica · PDF fileRecursion 215 Chapter 8. Recursion Recursion is when a procedure uses itself as part of the ... Rabbit Trail 19. Recursive