0 1 Knapsack

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0-1 Knapsack problem

Dr. Ying [email protected]

RAIK 283Data Structures & Algorithms

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Giving credit where credit is due:» Most of slides for this lecture are based on slides

created by Dr. David Luebke, University of Virginia.

» Some slides are based on lecture notes created by Dr. Chuck Cusack, Hope College.

» I have modified them and added new slides.

RAIK 283Data Structures & Algorithms

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Given some items, pack the knapsack to get the maximum total value. Each item has some weight and some value. Total weight that we can carry is no more than some fixed number W.So we must consider weights of items as well as their values.

Item # Weight Value 1 1 8 2 3 6 3 5 5

Knapsack problem

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Knapsack problem

There are two versions of the problem:1. “0-1 knapsack problem”

Items are indivisible; you either take an item or not. Some special instances can be solved with dynamic programming

2. “Fractional knapsack problem” Items are divisible: you can take any fraction of an item

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Given a knapsack with maximum capacity W, and a set S consisting of n items

Each item i has some weight wi and benefit value bi (all wi and W are integer values)

Problem: How to pack the knapsack to achieve maximum total value of packed items?


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Problem, in other words, is to find

Ti

iTi

i Wwb subject to max


The problem is called a “0-1” problem, because each item must be entirely accepted or rejected.

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Let’s first solve this problem with a straightforward algorithm

Since there are n items, there are 2n possible combinations of items.

We go through all combinations and find the one with maximum value and with total weight less or equal to W

Running time will be O(2n)

0-1 Knapsack problem: brute-force approach

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We can do better with an algorithm based on dynamic programming

We need to carefully identify the subproblems

0-1 Knapsack problem: dynamic programming approach

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Defining a Subproblem

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We can do better with an algorithm based on dynamic programming

We need to carefully identify the subproblems

Let’s try this:If items are labeled 1..n, then a subproblem would be to find an optimal solution for Sk = {items labeled 1, 2, .. k}


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If items are labeled 1..n, then a subproblem would be to find an optimal solution for Sk =

{items labeled 1, 2, .. k}

This is a reasonable subproblem definition. The question is: can we describe the final

solution (Sn ) in terms of subproblems (Sk)? Unfortunately, we can’t do that.


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Max weight: W = 20For S4:Total weight: 14Maximum benefit: 20

w1 =2b1 =3

w2 =4b2 =5

w3 =5b3 =8

w4 =3b4 =4 wi bi

10

85

54

43

32

Weight Benefit

9

Item#

4

3

2

1

5

S4

S5

w1 =2b1 =3

w2 =4b2 =5

w3 =5b3 =8

w5 =9b5 =10

For S5:Total weight: 20Maximum benefit: 26

Solution for S4 is not part of the solution for S5!!!

?


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As we have seen, the solution for S4 is not part of the solution for S5

So our definition of a subproblem is flawed and we need another one!


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Let’s add another parameter: w, which will represent the maximum weight for each subset of items

The subproblem then will be to compute V[k,w], i.e., to find an optimal solution for Sk = {items labeled 1, 2, .. k} in a knapsack of size w


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The subproblem will then be to compute V[k,w], i.e., to find an optimal solution for Sk = {items labeled 1, 2, .. k} in a knapsack of size w

Assuming knowing V[i, j], where i=0,1, 2, … k-1, j=0,1,2, …w, how to derive V[k,w]?

Recursive Formula for subproblems

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It means, that the best subset of Sk that has total weight w is:1) the best subset of Sk-1 that has total weight w, or2) the best subset of Sk-1 that has total weight w-wk

plus the item k

else }],1[],,1[max{

if ],1[],[

kk

kbwwkVwkV

wwwkVwkV

Recursive formula for subproblems:

Recursive Formula for subproblems (continued)

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Recursive Formula

The best subset of Sk that has the total weight w, either contains item k or not.

First case: wk>w. Item k can’t be part of the solution, since if it was, the total weight would be > w, which is unacceptable.

Second case: wk w. Then the item k can be in the solution, and we choose the case with greater value.

else }],1[],,1[max{

if ],1[],[

kk

kbwwkVwkV

wwwkVwkV

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for w = 0 to WV[0,w] = 0

for i = 1 to nV[i,0] = 0

for i = 1 to nfor w = 0 to W

if wi <= w // item i can be part of the solutionif bi + V[i-1,w-wi] > V[i-1,w]

V[i,w] = bi + V[i-1,w- wi]else

V[i,w] = V[i-1,w]else V[i,w] = V[i-1,w] // wi > w

0-1 Knapsack Algorithm

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for w = 0 to WV[0,w] = 0

for i = 1 to nV[i,0] = 0


< the rest of the code >

What is the running time of this algorithm?

O(W)

O(W)Repeat n times

O(n*W)Remember that the brute-force algorithm

takes O(2n)

Running time

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Let’s run our algorithm on the following data:

n = 4 (# of elements)W = 5 (max weight)Elements (weight, benefit):(2,3), (3,4), (4,5), (5,6)

Example

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for w = 0 to WV[0,w] = 0

0 0 0 0 000123

4 50 1 2 3

4

i\W

Example (2)

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for i = 1 to nV[i,0] = 0

0000

0 0 0 0 000123

4 50 1 2 3

4

i\W

Example (3)

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if wi <= w // item i can be part of the solution if bi + V[i-1,w-wi] > V[i-1,w] V[i,w] = bi + V[i-1,w- wi] else V[i,w] = V[i-1,w]else V[i,w] = V[i-1,w] // wi > w

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

0

i=1bi=3wi=2w=1w-wi =-1

0 0 0 0 000123

4 50 1 2 3

4

i\W

000

Example (4)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

300000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=1bi=3wi=2w=2w-wi =0


Example (5)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

300000

0 0 0 0 000123

4 50 1 2 3

4



3

Example (6)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

300000

0 0 0 0 000123

4 50 1 2 3

4



3 3

Example (7)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

300000

0 0 0 0 000123

4 50 1 2 3

4



3 3 3

Example (8)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=2bi=4wi=3w=1w-wi =-2

3 3 3 30


Example (9)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=2bi=4wi=3w=2w-wi =-1

3 3 3 33


0

Example (10)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4


3 3 3 30


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Example (11)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4


3 3 3 30


43 4

Example (12)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4


3 3 3 30


73 4 4

Example (13)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=3bi=5wi=4w= 1..3

3 3 3 30 3 4 4


73 40

Example (14)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=3bi=5wi=4w= 4w- wi=0

3 3 3 30 3 4 4 70 3 4 5


Example (15)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4


3 3 3 30 3 4 4 70 3 4


5 7

Example (16)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=4bi=6wi=5w= 1..4

3 3 3 30 3 4 4


7

3 4070 3 4 5

5

Example (17)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4


3 3 3 30 3 4 4 70 3 4


577

0 3 4 5

Example (18)

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Exercise P303 8.2.1 (a).

How to find out which items are in the optimal subset?

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Comments This algorithm only finds the max possible

value that can be carried in the knapsack» i.e., the value in V[n,W]

To know the items that make this maximum value, an addition to this algorithm is necessary

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All of the information we need is in the table. V[n,W] is the maximal value of items that can be

placed in the Knapsack. Let i=n and k=W

if V[i,k] V[i1,k] then mark the ith item as in the knapsacki = i1, k = k-wi

else i = i1 // Assume the ith item is not in the knapsack

// Could it be in the optimally packed knapsack?

How to find actual Knapsack Items

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=4k= 5bi=6wi=5V[i,k] = 7V[i1,k] =7

3 3 3 30 3 4 4 70 3 4

i=n, k=Wwhile i,k > 0


else i = i1

5 70 3 4 5 7

Finding the Items

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=4k= 5bi=6wi=5V[i,k] = 7V[i1,k] =7

3 3 3 30 3 4 4 70 3 4



else i = i1

5 70 3 4 5 7

Finding the Items (2)

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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=3k= 5bi=5wi=4V[i,k] = 7V[i1,k] =7

3 3 3 30 3 4 4 70 3 4



else i = i1

5 70 3 4 5 7


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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=2k= 5bi=4wi=3V[i,k] = 7V[i1,k] =3k wi=2

3 3 3 30 3 4 4 70 3 4



else i = i1

5 70 3 4 5 7

7


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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W i=1k= 2bi=3wi=2V[i,k] = 3V[i1,k] =0k wi=0

3 3 3 30 3 4 4 70 3 4



else i = i1

5 70 3 4 5 7

3


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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W

3 3 3 30 3 4 4 70 3 4


if V[i,k] V[i1,k] then mark the nth item as in the knapsacki = i1, k = k-wi

else i = i1

5 70 3 4 5 7

i=0k= 0

The optimal knapsack should contain {1, 2}


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Items:1: (2,3)2: (3,4)3: (4,5) 4: (5,6)

00000

0 0 0 0 000123

4 50 1 2 3

4

i\W

3 3 3 30 3 4 4 70 3 4


if V[i,k] V[i1,k] then mark the nth item as in the knapsacki = i1, k = k-wi

else i = i1

5 70 3 4 5 7

The optimal knapsack should contain {1, 2}

73


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Memorization (Memory Function Method) Goal:

» Solve only subproblems that are necessary and solve it only once Memorization is another way to deal with overlapping

subproblems in dynamic programming With memorization, we implement the algorithm recursively:

» If we encounter a new subproblem, we compute and store the solution.

» If we encounter a subproblem we have seen, we look up the answer Most useful when the algorithm is easiest to implement

recursively» Especially if we do not need solutions to all subproblems.

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V[i,w] = -1

for w = 0 to WV[0,w] = 0

for i = 1 to nV[i,0] = 0

MFKnapsack(i, w)if V[i,w] < 0

if w < wi

value = MFKnapsack(i-1, w)else value = max(MFKnapsack(i-1, w),

bi + MFKnapsack(i-1, w-wi))

V[i,w] = valuereturn V[i,w]

0-1 Knapsack Memory Function Algorithm

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Dynamic programming is a useful technique of solving certain kind of problems

When the solution can be recursively described in terms of partial solutions, we can store these partial solutions and re-use them as necessary (memorization)

Running time of dynamic programming algorithm vs. naïve algorithm:» 0-1 Knapsack problem: O(W*n) vs. O(2n)

Conclusion

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In-Class Exercise

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Brute-Force Approach

Design and Analysis of Algorithms - Chapter 8 53

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Dynamic-Programming Approach (1) SMaxV(0) = 0 (2) MaxV(0) = 0 (3) for i=1 to n (4) SMaxV(i) = max(SmaxV(i-1)+xi, 0) (5) MaxV(i) = max(MaxV(i-1), SMaxV(i)) (6) return MaxV(n)

Run the algorithm on the following example instance: » 30, 40, -100, 10, 20, 50, -60, 90, -180, 100 54

0 1 Knapsack

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