Solving Knapsack Problem: 1st & 2nd Algorithm

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  • Thread starter mathmari
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In summary, the conversation discussed the application of the Knapsack algorithm to a set of elements with different weights and benefits. Two versions of the algorithm were presented, with calculations showing the optimal choices for each version. The fractional version of the Knapsack problem was also introduced, with an algorithm for finding the optimal choices for maximizing benefit within a given weight limit. The conversation concluded with questioning the interpretation of the results and whether the total weight exceeded the maximum weight limit.
  • #1
mathmari
Gold Member
MHB
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Hey! :eek:

I want to apply the Knapsack algorithm at the following:

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

The first version of the algorithm is the following:

Code:
K(0)=0
for w=1 to W
    K(w)=max_{w_i \leq w} {K(w-w_i)+v_i}

I have found the following:

$$K(1)=\max_{w_i \leq 1} \{K(1-w_i)+v_i\}=0 \\ K(2)=\max_{w_i \leq 2} \{K(2-w_i)+v_i\}=K(2-2)+3=K(0)+3=3 \\ K(3)=\max_{w_i \leq 3} \{K(3-w_i)+v_i \}=\max \{K(3-3)+4, K(3-2)+3\}=\max \{K(0)+4, K(1)+3\}=\max \{4, 3\}=4 \\ K(4)=\max_{w_i \leq 4} \{K(4-w_i)+v_i\}=\max \{K(4-4)+5, K(4-3)+4, K(4-2)+3\}=\max \{K(0)+5, K(1)+4, K(2)+3\}=\max \{5, 4, 6\}=6 \\ K(5)=\max_{w_i \leq 5} \{K(5-w_i)+v_i\}=\max \{K(5-5)+6, K(5-4)+5, K(5-3)+4, K(5-2)+3\}=\max \{K(0)+6, K(1)+5, K(2)+4, K(3)+3\}=\max \{6, 5, 7, 7\}=7$$

At $K(5)=7$, the $7$ came from the elements $(2,3)$ and $(3,4)$. So, the optimal choice is to take these two elements with benefit $3+4=7$. Is this correct?? (Wondering)
The second version of the Knapsack algorithm is the following:

Code:
K(0,j)=0, for all j=1,2, ... ,n 
K(w,0)=0, for all w=1,2, ... ,W 
for j<-1 to n 
     for w<-1 to W 
          if w>w_j 
             K(w,j)=max{K(w,j-1),K(w-w_j,j-1)+v_j} 
          else 
             K(w,j)=K(w,j-1)

I have found the following:

$$K(1,1)=K(1,0)=0 \\ K(2, 1)=K(2, 0)=0 \\ K(3, 1)=\max \{K(3, 0), K(3-2, 0)+3\}=\max \{0, 0+3\}=\max \{0, 3\}=3 \\ K(4, 1)=\max \{K(4, 0), K(4-2, 0)+3\}=\max \{0, 0+3\}=\max \{0, 3\}=3 \\ K(5, 1)=\max \{K(5, 0)+K(5-2, 0)+3\}=\max \{0, 0+3\}=\max \{0, 3\}=3 \\ \\
K(1, 2)=K(1, 1)=0 \\ K(2, 2)=K(2, 1)=0 \\ K(3, 2)=K(3, 1)=3 \\ K(4, 2)=\max \{K(4, 1), K(4-3, 1)+4\}=\max \{3, 0+4\}=\max \{3, 4\}=4 \\ K(5, 2)=\max \{K(5, 1), K(5-3, 1)+4\}=\max \{3, 0+4\}=\max \{3, 4\}=4 \\ \\
K(1, 3)=K(1, 2)=0 \\ K(2, 3)=K(2, 2)=0 \\ K(3, 3)=K(3, 2)=3 \\ K(4, 3)=K(4, 2)=4 \\ K(5, 3)=\max \{K(5, 2), K(5-4, 2)+5\}=\max \{4, 0+5\}=\max \{4, 5\}=5 \\ \\
K(1, 4)=K(1, 3)=0 \\ K(2, 4)=K(2, 3)=0 \\ K(3, 4)=K(3, 3)=3 \\ K(4, 4)=K(4, 3)=4 \\ K(5, 4)=K(5, 3)=5$$ Is this correct?? (Wondering) How can we find which elements we have to take?? (Wondering)
 
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  • #2
I found the following algorithm for the fractional version of the Knapsack problem:

Code:
    Algorithm fractionalKnapsack(S, W) 

    Input: set S of items w/ benefit bi
    and weight wi
    ; max. weight W

    Output: amount xi of each item i
    to maximize benefit with
    weight at most W

    for each item i in S
         xi ← 0
         vi ← bi / wi {value}
    w ← 0 {total weight}
    while w < W
         remove item i with highest vi
         xi ← min{wi , W − w}
         w ← w + min{wi , W − w}

I tried to apply this algorithm and I got the following:

$$x_1 \leftarrow 0 \\ v_1 \leftarrow \frac{3}{2}=1.5 \\ x_2 \leftarrow 0 \\ v_2 \leftarrow \frac{4}{3}=1.33 \\ x_3 \leftarrow 0 \\ v_3 \leftarrow \frac{5}{4}=1.25 \\ x_4 \leftarrow 0 \\ v_4 \leftarrow \frac{6}{5}=1.2 \\ w \leftarrow 0 \\ 0<5 \checkmark \\ \ \ \ \ \ \text{ remove item } =1, \max v_i=1.5 \\ \ \ \ \ \ x_1 \leftarrow \min \{w_1, W-w\}=\min \{2, 5-0\}=\min \{2, 5\}=2 \\ \ \ \ \ \ w \leftarrow 2+\min \{w_1, W-w\}=0+2=2 \\ 2<5 \checkmark \\ \ \ \ \ \ \text{ remove item } i=2, \max v_i=1.33 \\ \ \ \ \ \ x_2 \leftarrow \min \{w_2 , W-w\}=\min \{3, 5-2\}=\min \{3, 3\}=3 \\ \ \ \ \ \ w \leftarrow w+\min \{w_2, W-w\}=2+3=5 \\ 5<5 \times$$ Does this mean that the optimal choice is to take two times the item $1$ and three times the item $2$ ?? (Wondering)

But isn't then the total weight greater than $5$ ?? (Wondering)
 
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Related to Solving Knapsack Problem: 1st & 2nd Algorithm

1. What is the Knapsack Problem?

The Knapsack Problem is a well-known optimization problem in computer science and mathematics. It involves selecting a subset of items with certain values and weights to maximize the total value while staying within a given weight limit. This problem has various applications in real-world scenarios such as resource allocation, scheduling, and financial portfolio management.

2. What is the 1st Algorithm for solving the Knapsack Problem?

The 1st Algorithm for solving the Knapsack Problem is known as the Brute Force or Exhaustive Search Algorithm. It involves checking all possible combinations of items and selecting the one with the maximum value while adhering to the weight limit. This algorithm has a time complexity of O(2^n) and is only suitable for small instances of the problem.

3. What is the 2nd Algorithm for solving the Knapsack Problem?

The 2nd Algorithm for solving the Knapsack Problem is known as the Dynamic Programming Algorithm. It uses a bottom-up approach and breaks down the problem into smaller subproblems, which are then solved and combined to find the optimal solution. This algorithm has a time complexity of O(nW), where n is the number of items and W is the weight limit.

4. How does the 2nd Algorithm improve upon the 1st Algorithm?

The 2nd Algorithm improves upon the 1st Algorithm by reducing the time complexity from exponential to polynomial. This makes it more efficient for larger instances of the problem and allows it to find the optimal solution in a reasonable amount of time. Additionally, the Dynamic Programming Algorithm also takes into account previously calculated subproblems, making it more efficient in terms of space complexity.

5. Are there any other algorithms for solving the Knapsack Problem?

Yes, there are various other algorithms for solving the Knapsack Problem, such as the Greedy Algorithm, Branch and Bound Algorithm, and Genetic Algorithm. Each of these algorithms has its own approach and trade-offs in terms of time and space complexity. The selection of the most suitable algorithm depends on the specific requirements and constraints of the problem at hand.

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