Dynamic Programming - Simulation Laboratoryphg-simulation-laboratory.com/.../2016/04/M19Dynamic-Programming-1.pdf · ZKA-STT Telkom Bandung 4 Dynamic programming strategy is related

Dynamic ProgrammingMatakuliah Desain & Analisis Algoritma

(CS 3024)

ZK Abdurahman Baizal

STT Telkom Bandung

ZKA-STT Telkom Bandung 2

Ditemukan oleh Seorang matematikawan AS, Richard Bellman tahun 1950

Kata Programming lebih mengacu ke planning, dan bukan mengacu ke pemrograman komputer

The word dynamic is related with the manner in which the tables used in obtaining the solution are constructed


Pemrograman Dinamis adalah sebuah teknik untuk menyelesaikan masalah dengan cara membagi masalah dalam beberapa sub masalah yang tidak saling independent (istilah lain:overlapping subproblem) [Levitin]

Pemrograman Dinamis (dynamic programming): metode pemecahan masalah dengan cara menguraikan solusi menjadi sekumpulan langkah (step) atau tahapan (stage) sedemikian sehingga solusi dari persoalan dapat dipandang dari serangkaian keputusan yang saling berkaitan [diktat Rinaldi]


Dynamic programming strategy is related with divide and conquer strategy because both of them are based on dividing a problem in subproblems. However there are some differences between these two approaches:

in divide and conquer approaches the subproblems are usually independent so all of them have to be solved

in dynamic programming approaches the subproblems are dependent (overlapping) so we need the result obtained for a subproblem many times (so it is important to store it)


Divide & Conquer menggunakan pendekatan top down

Pemrograman Dinamis : Pada umumnya menggunakan pendekatan bottom

up

Pendekatan Top down + bottom up memory functions (lihat levitin halaman 295)


The steps in the development of a dynamic programming

1. Establish a recursive property that gives the solution to an instance of the problem

2. Solve an instance of the problem in a bottom-up fashion by solving smaller instance first


Bilangan Fibonacci

Bilangan Fibonacci

0,1, 1, 2, 3, 5, 8, 13, 21, 34,…

Dengan relasi recurrence :

0 untuk n=0

F(n) = 1 untuk n=1

F(n-1)+F(n-2) untuk n>=2


Fibonacci using Divide-and-Conquer (Top down approach)

problem: determine the nth term in the fibo

sequence

inputs: a nonnegative integer n

outputs: fib, the nth term of the fibo sequence

function fib(n:integer):integer;

begin

if (n=1) or (n=2) then

fib:=1

else

fib:=fib(n-1)+fib(n-2)

end

end;


Ilustrasi


nth Fibonacci using Dynamic Programming (Bottom up Approach)

1. Establish a recursive property in terms of F, it is

3 ]2[]1[

2 1

1 1

nnFnF

n

n

nF



2. Solve the instance of the problem by computing the 1th and 2nd term of fibonacci sequenceExample. F[5]Compute 1th term: F[1]=1Compute 2nd term: F[2]=1Compute 3rd term: F[3]=F[2]+F[1]=2Compute 4th term: F[4]=F[3]+F[2]=3Compute 5th term: F[5]=F[4]+F[3]=5



problem: determine the nth term in the fibo sequenceinputs: a nonnegative integer noutputs: fib2, the nth term of the fibo sequence

function fib2(n:integer):integer;var

f: array[1..n] of integer;i: index;

beginf[1]:=1;if n > 1 then

beginf[2]:=1;for i := 3 to n

beginf[i]:=f[i-1]+f[i-2]

endend

fib2:=f[n];end;


Ilustrasi


Binomial Coefficient

Teorema Binomial :

(x + y)n =

C(n, 0) xn + C(n, 1) xn-1 y1 + … + C(n, k) xn-k yk

+ … + C(n, n) yn

Koefisien untuk xn-kyk adalah C(n, k). Bilangan C(n, k) disebut koefisien binomial.


Computing a binomial coefficient C(n,k)

1 if k=0 or n=k

C(n,k)=

C(n-1,k)+C(n-1,k-1) otherwise


Binomial Coefficient using Divide-and-Conquer (Top down approach)

comb(n,k)

//Problem : Compute the binomial coefficient

//Input : nonnegatif integers n and k, where k<=n

//Output :comb, the binomial coefficient C(n,k)

IF (k=0) OR (n=k) THEN

RETURN 1

ELSE

RETURN comb(n-1,k)+comb(n-1,k-1)


Binomial Coefficient using Dinamic Programming (Bottom up approach)

Establish a recursive property

1 if k=0 or n=k

C[n,k]=

C[n-1,k]+C[n-1,k-1] otherwise


Bottom-up approach: constructing the Pascal’s triangle

0 1 2 3 4 … k-1 k

0 1

1 1 1

2 1 2 1

3 1 3 3 1

4 1 4 6 4 1

…

1 … 1

…

n-1 1 C(n-1,k-1) C(n-1,k)

n 1 C(n,k)

Binomial Coefficient using Dinamic Programming (Bottom up Approach)



Algorithm :Comb(n,k)

//Problem : Compute the binomial coefficient

//Input : nonnegative integers n and k, where k<=n

//Ouput : Comb, the binomial coefficient C(n,k)

FOR i:=0 to n DO

FOR j:=0,min{i,k} DO

IF (j=0) OR (j=k)

THEN C[i,j]:=1

ELSE

C[i,j]:=C[i-1,j]+C[i-1,j-1]

RETURN C[n,k]



Contoh : Compute C[4,2] !

Compute row 0 : C[0,0] = 1


C[1,1] = 1


C[2,1] = C[1,0]+C[1,1]=1+1=2




C[3,1] = C[2,0]+C[2,1]=1+2=3

C[3,2] = C[2,1]+C[2,2]=2+1=3


C[4,1] = C[3,0]+C[3,1]=1+3=4

C[4,2] = C[3,1]+C[3,2]=3+3=6

Jadi, C[4,2] = 6


Warshall’s Algorithms

Warshall’s Algorithms digunakan untuk menentukan transitif closure dari suatu graf berarah

Transitif closure suatu matriks yang berisi informasi tentang keberadaan lintasan antar vertex dalam sebuah graf berarah.



Warshall’s algorithm membentuk transitif closure dari graf dengan n vertex melalui sederetan matriks boolean

R(0),...., R(k-1), R(k),...., R(n)

Element r(k)[i,j] of matrix R(k) is equal to 1 if only if there exists a directed path from vertex i to the vertex j with intermediate vertex, if any, numbered not higher than k.

k

ijr



Recursive property:

r(k)[i,j] = r(k-1)[i,j] OR

(r(k-1)[i,k] AND r(k-1)[k,j])



Given graf :

a b

dc

a b c d

a 0 1 0 0

b 0 0 0 1 adjacency

c 0 0 0 0 matrix

d 1 0 1 0

a b c d

a 1 1 1 1

b 1 1 1 1 transitive

c 0 0 0 0 closure

d 1 1 1 1



Algorithm

Warshall(A[1..n, 1..n])

//Input : The adjacency matrix A of digraph

//Output : The transitive closure of digraph

R(0) A

for k 1 to n do

for i 1 to n do

for j 1 to n do

r(k)[i,j ]= r(k-1)[i,j] OR (r(k-1)[i,k] AND r(k-1)[k,j])

return R(k)



Given graf :

a b

dc

a b c d

a 0 1 0 0

b 0 0 0 1 R(0)

c 0 0 0 0

d 1 0 1 0

a b c d

a 0 1 0 0

b 0 0 0 1 R(1)

c 0 0 0 0

d 1 1 1 0

Boxes are used for getting R(k+1)



Given graf :

a b

dc

a b c d

a 0 1 0 1

b 0 0 0 1 R(2)

c 0 0 0 0

d 1 0 1 1

a b c d

a 0 1 0 1

b 0 0 0 1 R(3)

c 0 0 0 0

d 1 1 1 1

Boxes are used for getting R(k+1)



Given graf :

a b

dc

a b c d

a 1 1 1 1

b 1 1 1 1 R(4)

c 0 0 0 0 Transitive

d 1 1 1 1 Closure


Dynamic Programming and Optimization Problems

Dynamic programming is also related to greedy strategy since both of them can be applied to optimization problems which have the property of optimal substructure


Prinsip Optimalitas

Pada program dinamis, rangkaian keputusan yang optimal dibuat dengan menggunakan Prinsip Optimalitas.

Prinsip Optimalitas: jika solusi total optimal, maka bagian solusi sampai tahap ke-k juga optimal.


Prinsip optimalitas berarti bahwa jika kita bekerja dari tahap k ke tahap k + 1, kita dapat menggunakan hasil optimal dari tahap k tanpa harus kembali ke tahap awal.

ongkos pada tahap k +1 =

(ongkos yang dihasilkan pada tahap k ) +

(ongkos dari tahap k ke tahap k + 1)


Dengan prinsip optimalitas ini dijamin bahwa pengambilan keputusan pada suatu tahap adalah keputusan yang benar untuk tahap-tahap selanjutnya.

Pada metode greedy hanya satu rangkaian keputusan yang pernah dihasilkan, sedangkan pada metode program dinamis lebih dari satu rangkaian keputusan. Hanya rangkaian keputusan yang memenuhi prinsip optimalitas yang akan dihasilkan.


The steps in the development of a dynamic programming (on optimization Problems)

1. Establish a recursive property that gives the optimal solution to an instance of the problem.

2. Compute the value of an optimal solution in a bottom-up fashion.

3. Construct an optimal solution in a bottom-up fashion.


The 0/1 Knapsack Problem

Let us consider an instance defined by the first i items 1 ≤ i ≤ n

Weights w1,w2,..,wn

Values v1,v2,….,vn

Knapsack capasity j, 1 ≤ j ≤ W

V[i,j] value of an optimal solution to the instance


All subsets of first i items that fit into

knapsack of capacity j divide into 2

categories :

Among subsets that do not include the i item V[i-1,j]

Among the subsets that do include ith item (hence, j-wi ≤0) vi+V[i-1,j-wi]



Recurrence property :

V[i-1,j] if j-wi <0

V[i,j]=

max{V[i-1,j], vi+V[i-1,j-wi]} if j-wi ≥0

Initial conditions :

V[0,j]=0 dan V[i,0]=0 i,j ≥ 0



Contoh Kasus Knapsack

item Weight value

1 2 $12

2 1 $10

3 3 $20

4 2 $15

Knapsack Kapacity

W=5


Constructing the solution

Example:

0 1 2 3 4 5

0 0 0 0 0 0 0

1 0 0 12 12 12 12

2 0 10 12 22 22 22

3 0 10 12 22 30 32

4 0 10 15 25 30 37

Steps:

Compare V[4,5] with V[3,5]. Since

they are different it means that the

object item 4 is selected

Go to V[3,5-w4]=V[3,3]=22 and

compare it with V[2,3]=22. Since

they are equal it means that item 3

is not selected

Go to V[2,3]<>V[1,3]. it means that

also item 2 is selected

Go to V[1,3-w2]=V[1,2]=12 and

compare it with V[0,2]=0. Since

they are different it means that the

object item 1 is selected

Thus the solution is {1,2,4} or s=(1,1,0,1)

Contoh Kasus Knapsack


Floyd’s Algorithm

Floyd’s Algorithm digunakan untuk mencari jarak dari masing-masing vertex ke semua vertex yang lain dalam sebuah graf terhubung (berarah maupun tidak berarah) yang sering juga disebut all-pairs shortest-path problem

Elemen d[i,j] dari sebuah distance matrix menyatakan panjang lintasan terpendek dari vertex i ke vertex j


Floyd’s Algorithm

Given Graph :

a b

dc

2

6 7

1

3

a b c d

a 0 ∞ 3 ∞

b 2 0 ∞ ∞ weight matrix

c ∞ 7 0 1

d 6 ∞ ∞ 0

a b c d

a 0 ∞ 3 ∞

b 2 0 ∞ ∞ distance matrix

c ∞ 7 0 1

d 6 ∞ ∞ 0


Floyd’s Algorithm

Floyd’s algorithm computes distance matrix of a wighted graph with n vertces through a series of n-by-n matrices:

D(0),….,D(k-1),D(k),….,D(n)

Element d(k)[i,j] of D(k) is the length of shortest path among all path from vertex i to the vertex j with each intermediate vertex, if any, numbered not higher than k


Floyd’s Algorithm

Recurrence property :

min{d(k-1)[i,j], d(k-1)[i,k]+d(k-1)[k,j]}

d(k)[i,j]= for k≥1

d(k)[i,j]=w[i,j] for k=0


Floyd’s Algorithm

Algorithm

Floyd(W[1..n,1..n]

//input : The weight matrix W of a graph

//output : The distance matrix of the shortest path length

DW

for k 1 to n do

for i 1 to n do

for j 1 to n do

D[i,j] min{D[i,j], D[i,k]+D[k,j]}

return D


Floyd’s Algorithm

weighted graf :

a b

dc

2

6 7

1

3

a b c d

a 0 ∞ 3 ∞

b 2 0 ∞ ∞ D(0)

c ∞ 7 0 1

d 6 ∞ ∞ 0

a b c d

a 0 ∞ 3 ∞

b 2 0 5 ∞ D(1)

c ∞ 7 0 1

d 6 ∞ 9 0

Boxes are used for getting D(k+1)


Floyd’s Algorithm

weighted graf :

a b

dc

2

6 7

1

3

a b c d

a 0 ∞ 3 ∞

b 2 0 5 ∞ D(2)

c 9 7 0 1

d 6 ∞ 9 0

a b c d

a 0 10 3 4

b 2 0 5 6 D(3)

c 9 7 0 1

d 6 16 9 0

Boxes are used for getting D(k+1)


Floyd’s Algorithm

weighted graf :

a b

dc

2

6 7

1

3

a b c d

a 0 10 3 4

b 2 0 5 6 D(4)

c 7 7 0 1 distance matrix

d 6 16 9 0

Dynamic Programming - Simulation Laboratoryphg-simulation-laboratory.com/.../2016/04/M19Dynamic-Programming-1.pdf · ZKA-STT Telkom Bandung 4 Dynamic programming strategy is related

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