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Recursive Algorithm Analysis

Dr. Ying Luylu@cse.unl.edu

RAIK 283: Data Structures & RAIK 283: Data Structures & AlgorithmsAlgorithms

September 13, 2012

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Giving credit where credit is due:Giving credit where credit is due:• Most of the lecture notes are based on the slides Most of the lecture notes are based on the slides

from the Textbook’s companion websitefrom the Textbook’s companion website

http://www.aw-bc.com/info/levitinhttp://www.aw-bc.com/info/levitin

• Several slides are from Hsu Wen Jing of the Several slides are from Hsu Wen Jing of the National University of SingaporeNational University of Singapore

• I have modified them and added new slidesI have modified them and added new slides

RAIK 283: Data Structures & RAIK 283: Data Structures & AlgorithmsAlgorithms

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Example: a recursive algorithmExample: a recursive algorithm

Algorithm:Algorithm:if if nn=0 then =0 then FF((nn) := 1) := 1

else else FF((nn) := ) := FF((nn-1) * -1) * nnreturn return FF((nn) )

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Algorithm F(n)

// Compute the nth Fibonacci number recursively

//Input: A nonnegative integer n

//Output: the nth Fibonacci number

if n 1 return n

else return F(n-1) + F(n-2)

Example: another recursive Example: another recursive algorithmalgorithm

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Recurrence RelationRecurrence Relation

Recurrence RelationRecurrence Relation

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Recurrence RelationRecurrence Relation

Recurrence Relation:Recurrence Relation: an equation or inequality that an equation or inequality that describes a function in terms of its value on smaller describes a function in terms of its value on smaller inputsinputs

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Example: a recursive algorithmExample: a recursive algorithm

Algorithm:Algorithm:if if nn=0 then =0 then FF((nn) := 1) := 1

else else FF((nn) := ) := FF((nn-1) * -1) * nnreturn return FF((nn) )

What does this algorithm compute?What does this algorithm compute?

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Example: a recursive algorithmExample: a recursive algorithm

Algorithm:Algorithm:if if nn=0 then =0 then FF((nn) := 1) := 1

else else FF((nn) := ) := FF((nn-1) * -1) * nnreturn return FF((nn) )

What does this algorithm compute?What does this algorithm compute?

What’s the basic operation of this algorithm?What’s the basic operation of this algorithm?

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Example: recursive evaluation Example: recursive evaluation of of nn ! !

Recursive definition of Recursive definition of nn!:!: Algorithm:Algorithm:

if if nn=0 then =0 then FF((nn) := 1) := 1else else FF((nn) := ) := FF((nn-1) * -1) * nn

return return FF((nn) )

M(n): number of multiplications to compute M(n): number of multiplications to compute nn! ! with this recursive algorithmwith this recursive algorithm

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Example: recursive evaluation Example: recursive evaluation of of nn ! !

Recursive definition of Recursive definition of nn!:!: Algorithm:Algorithm:

if if nn=0 then =0 then FF((nn) := 1) := 1else else FF((nn) := ) := FF((nn-1) * -1) * nn

return return FF((nn) )

M(n): number of multiplications to compute M(n): number of multiplications to compute nn! ! with this recursive algorithmwith this recursive algorithm

Could we establish a Could we establish a recurrence relationrecurrence relation for for deriving M(n)? deriving M(n)?

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Example: recursive evaluation Example: recursive evaluation of of nn ! !

Definition:Definition: n n ! = 1*2! = 1*2*…*(n-*…*(n-1)*1)*nn Recursive definition of Recursive definition of nn!:!: Algorithm:Algorithm:

if if nn=0 then =0 then FF((nn) := 1) := 1else else FF((nn) := ) := FF((nn-1) * -1) * nn

return return FF((nn) )

M(n) = M(n-1) + 1M(n) = M(n-1) + 1 Initial Condition: M(0) = ?Initial Condition: M(0) = ?

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Example: recursive evaluation Example: recursive evaluation of of nn ! !

Recursive definition of Recursive definition of nn!:!: Algorithm:Algorithm:

if if nn=0 then =0 then FF((nn) := 1) := 1

else else FF((nn) := ) := FF((nn-1) * -1) * nn

return return FF((nn) )

M(n) = M(n-1) + 1M(n) = M(n-1) + 1 Initial condition: M(0) = 0Initial condition: M(0) = 0 Explicit formula for M(n) in terms of n only? Explicit formula for M(n) in terms of n only?

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Time efficiency of recursive Time efficiency of recursive algorithmsalgorithms

Steps in analysis of recursive algorithms: Steps in analysis of recursive algorithms: Decide on parameter Decide on parameter nn indicating indicating input sizeinput size Identify algorithm’s Identify algorithm’s basic operationbasic operation Determine Determine worstworst, , averageaverage, and , and bestbest case for inputs of size case for inputs of size nn

Set up a recurrence relation and initial condition(s) for Set up a recurrence relation and initial condition(s) for CC((nn)-the number of times the basic operation will be )-the number of times the basic operation will be executed for an input of size executed for an input of size nn

Solve the recurrence to obtain a closed form or Solve the recurrence to obtain a closed form or determine the order of growth of the solution (see determine the order of growth of the solution (see Appendix B)Appendix B)

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

Problem:Problem:• Given three pegs (A, B, C) and n disks of different sizes Given three pegs (A, B, C) and n disks of different sizes • Initially, all the disks are on peg A in order of size, the largest Initially, all the disks are on peg A in order of size, the largest

on the bottom and the smallest on top on the bottom and the smallest on top • The goal is to move all the disks to peg C using peg B as an The goal is to move all the disks to peg C using peg B as an

auxiliaryauxiliary• Only 1 disk can be moved at a time, and a larger disk cannot Only 1 disk can be moved at a time, and a larger disk cannot

be placed on top of a smaller onebe placed on top of a smaller one

A C

B

n disks

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

Design a recursive algorithm to solve this problem:Design a recursive algorithm to solve this problem:• Given three pegs (A, B, C) and n disks of different sizes Given three pegs (A, B, C) and n disks of different sizes • Initially, all the disks are on peg A in order of size, the largest on Initially, all the disks are on peg A in order of size, the largest on

the bottom and the smallest on top the bottom and the smallest on top • The goal is to move all the disks to peg C using peg B as an The goal is to move all the disks to peg C using peg B as an

auxiliaryauxiliary• Only 1 disk can be moved at a time, and a larger disk cannot be Only 1 disk can be moved at a time, and a larger disk cannot be

placed on top of a smaller oneplaced on top of a smaller one

A C

B

n disks

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

Step 1: Solve simple case when n<=1?Step 1: Solve simple case when n<=1?Just trivialJust trivial

A C

B

A C

B

Move(A, C)

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

Step 2: Assume that a smaller instance can be Step 2: Assume that a smaller instance can be solved, i.e. can move n-1 disks. Then?solved, i.e. can move n-1 disks. Then?

A C

B

A C

B

A C

B

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

A C

BA C

B

A C

B

A C

B

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

A C

BA C

B

A C

B

A C

B

TOWER(n, A, B, C)

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

A C

BA C

B

A C

B

A C

B

TOWER(n, A, B, C)

TOWER(n-1, A, C, B)

Move(A, C)

TOWER(n-1, B, A, C)

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

TOWER(n, A, B, C) {

TOWER(n-1, A, C, B); Move(A, C);

TOWER(n-1, B, A, C)}

if n<1 return;

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

TOWER(n, A, B, C) {

TOWER(n-1, A, C, B); Move(A, C);

TOWER(n-1, B, A, C)}

if n<1 return;

Algorithm analysis: Algorithm analysis: Input size? Basic operation?Input size? Basic operation?

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

TOWER(n, A, B, C) {

TOWER(n-1, A, C, B); Move(A, C);

TOWER(n-1, B, A, C)}

if n<1 return;

Algorithm analysis: Algorithm analysis: Do we need to differentiate best case, worst Do we need to differentiate best case, worst

case & average case for inputs of size n?case & average case for inputs of size n?

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

TOWER(n, A, B, C) {

TOWER(n-1, A, C, B); Move(A, C);

TOWER(n-1, B, A, C)}

if n<1 return;

Algorithm analysis: Algorithm analysis: Set up a recurrence relation and initial Set up a recurrence relation and initial

condition(s) for C(n)condition(s) for C(n)

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EXAMPLE: tower of hanoiEXAMPLE: tower of hanoi

TOWER(n, A, B, C) {

TOWER(n-1, A, C, B); Move(A, C);

TOWER(n-1, B, A, C)}

if n<1 return;

Algorithm analysis: Algorithm analysis: C(n) = 2C(n-1)+1C(n) = 2C(n-1)+1

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In-Class ExerciseIn-Class Exercise

P. 76 Problem 2.4.1 (c): solve this recurrence relation: P. 76 Problem 2.4.1 (c): solve this recurrence relation:

x(n) = x(n-1) + n for n>0, x(0)=0x(n) = x(n-1) + n for n>0, x(0)=0 P. 77 Problem 2.4.4: consider the following recursive algorithm: P. 77 Problem 2.4.4: consider the following recursive algorithm:

• Algorithm Q(n)Algorithm Q(n)// Input: A positive integer n// Input: A positive integer n

If n = 1 return 1If n = 1 return 1

else return Q(n-1) + 2 * n – 1else return Q(n-1) + 2 * n – 1

• A. Set up a recurrence relation for this function’s values and solve A. Set up a recurrence relation for this function’s values and solve it to determine what this algorithm computesit to determine what this algorithm computes

• B. Set up a recurrence relation for the number of multiplications B. Set up a recurrence relation for the number of multiplications made by this algorithm and solve it. made by this algorithm and solve it.

• C. Set up a recurrence relation for the number of C. Set up a recurrence relation for the number of additions/subtractions made by this algorithm and solve it. additions/subtractions made by this algorithm and solve it.

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Example: BinRec(n) Example: BinRec(n)

Algorithm BinRec(n)

//Input: A positive decimal integer n

//Output: The number of binary digits in n’s binary representation

if n = 1 return 1

else return BinRec( n/2 ) + 1

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Smoothness ruleSmoothness rule

If T(n) If T(n) (f(n)) (f(n))

for values of n that are powers of b, where b for values of n that are powers of b, where b 2, 2,

then then

T(n) T(n) (f(n))(f(n))

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Example: BinRec(n) Example: BinRec(n)

Algorithm BinRec(n)

//Input: A positive decimal integer n

//Output: The number of binary digits in n’s binary representation

if n = 1 return 1

else return BinRec( n/2 ) + 1

If C(n) If C(n) (f(n)) (f(n)) for values of n that are powers of b, where b for values of n that are powers of b, where b 2, 2, then then C(n) C(n) (f(n))(f(n))

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Fibonacci numbersFibonacci numbers

The Fibonacci sequence:The Fibonacci sequence:0, 1, 1, 2, 3, 5, 8, 13, 21, … 0, 1, 1, 2, 3, 5, 8, 13, 21, …

Fibonacci recurrence:Fibonacci recurrence:F(F(nn) = F() = F(nn-1) + F(-1) + F(nn-2) -2)

F(0) = 0 F(0) = 0

F(1) = 1F(1) = 1

2nd 2nd order linear homogeneous order linear homogeneous recurrence relation recurrence relation

with constant coefficientswith constant coefficients

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Solving linear homogeneous Solving linear homogeneous recurrence relations with constant recurrence relations with constant coefficientscoefficients

Easy first: 1Easy first: 1stst order LHRRCCs: order LHRRCCs:CC((nn) = ) = a Ca C((nn -1) -1) CC(0) = (0) = tt … Solution: … Solution: CC((nn) =) = t a t ann

Extrapolate to 2Extrapolate to 2ndnd order orderLL((nn) = ) = aa LL((nn-1) + -1) + bb LL((nn-2) … A solution?: -2) … A solution?: LL((nn) = ) = ??

Characteristic equation (quadratic)Characteristic equation (quadratic) Solve to obtain roots Solve to obtain roots rr11 and and rr2 2 ((quadratic formula))

General solution to RR: linear combination of General solution to RR: linear combination of rr11nn

and and rr22nn

Particular solution: use initial conditionsParticular solution: use initial conditions

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Solving linear homogeneous Solving linear homogeneous recurrence relations with constant recurrence relations with constant coefficientscoefficients

Easy first: 1Easy first: 1stst order LHRRCCs: order LHRRCCs:CC((nn) = ) = a Ca C((nn -1) -1) CC(0) = (0) = tt … Solution: … Solution: CC((nn) =) = t a t ann

Extrapolate to 2Extrapolate to 2ndnd order orderLL((nn) = ) = aa LL((nn-1) + -1) + bb LL((nn-2) … A solution?: -2) … A solution?: LL((nn) = ) = ??

Characteristic equation (quadratic)Characteristic equation (quadratic) Solve to obtain roots Solve to obtain roots rr11 and and rr2 2 ((quadratic formula))

General solution to RR: linear combination of General solution to RR: linear combination of rr11nn

and and rr22nn

Particular solution: use initial conditionsParticular solution: use initial conditions

Explicit Formula for Explicit Formula for Fibonacci Number: F(n) = F(n-1) +F(n-2)Fibonacci Number: F(n) = F(n-1) +F(n-2)

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1. Definition based recursive algorithm1. Definition based recursive algorithm

Computing Fibonacci numbersComputing Fibonacci numbers

Algorithm F(n)

// Compute the nth Fibonacci number recursively

//Input: A nonnegative integer n

//Output: the nth Fibonacci number

if n 1 return n

else return F(n-1) + F(n-2)

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2. Nonrecursive brute-force algorithm2. Nonrecursive brute-force algorithm

Computing Fibonacci numbersComputing Fibonacci numbers

Algorithm Fib(n)

// Compute the nth Fibonacci number iteratively

//Input: A nonnegative integer n

//Output: the nth Fibonacci number

F[0] 0; F[1] 1

for i 2 to n do

F[i] F[i-1] + F[i-2]

return F[n]

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Computing Fibonacci numbersComputing Fibonacci numbers

3. Explicit formula algorithm3. Explicit formula algorithm

integernearest the torounded )2

51(

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)( nnF

Special care in its implementation: Special care in its implementation:

Intermediate results are irrational numbersIntermediate results are irrational numbersTheir approximations in the computer are accurate enoughTheir approximations in the computer are accurate enough

Final round-off yields a correct result Final round-off yields a correct result

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In-Class Exercises In-Class Exercises

What is the explicit formula for A(n)?What is the explicit formula for A(n)?A(A(nn) = 3A() = 3A(nn-1) – 2A(-1) – 2A(nn-2) A(0) = 1 A(1) = 3-2) A(0) = 1 A(1) = 3

P.83 2.5.3. Climbing stairs: Find the number of P.83 2.5.3. Climbing stairs: Find the number of different ways to climb an n-stair stair-case if each different ways to climb an n-stair stair-case if each step is either one or two stairs. (For example, a 3-step is either one or two stairs. (For example, a 3-stair staircase can be climbed three ways: 1-1-1, 1-stair staircase can be climbed three ways: 1-1-1, 1-2, and 2-1.)2, and 2-1.)