Versions of the Knapsack problem

  • MHB
  • Thread starter mathmari
  • Start date
In summary, the Knapsack problem asks for the maximum total benefit while keeping the weight under a limit. There are two versions - integer and fractional - where the difference lies in the picking of items. The integer version only allows for full or no picking, while the fractional version allows for partial picking. Both versions can be solved using programming, but the integer version has the optimal substructure property while the fractional version has the greedy choice property. This means that for the greedy choice property, the best item is chosen at each step based on the greatest benefit and smallest weight, until the Knapsack is full or there are no more items. To check for these properties, we need to ensure that each subproblem has an optimal solution.
  • #1
mathmari
Gold Member
MHB
5,049
7
Hey! :eek:


I am asked to state the two versions of the Knapsack problem and their differences. Could I formulate it as followed?? (Wondering)

The Knapsack problem is the following:

There are $n$ items, where the $i^{th}$ item has a benefit of $v_i$ and it has weight $w_i$.

We want to pick some items so that we maximize the total benefit while keeping the total weight of $W$.

The difference between the integer and the fractional version of the Knapsack problem is the following:

At the integer version we want to pick each item either fully or we don't pick it.

At the fractional version we can take a part of the item.
 
Physics news on Phys.org
  • #2
With what type of programming can each of the two versions be solved?

Which of the two versions has the optimal substructure property ?

Which of the two versions has the greedy choice property ?
For the greedy choice property I thought the following:

Wir choose at each step the "best" item, which is the one with the greatest benefit and the smallest weight. We do the same choice until the Knapsack is full or there aren't any objects that fill in the Knapsack.

Is this correct? The optimal substructure property means that each subproblem we have the optimal solution.

Right? But how can we check which of the versions has these properties?
 

FAQ: Versions of the Knapsack problem

What is the Knapsack problem?

The Knapsack problem is a well-known combinatorial optimization problem that involves finding the most valuable combination of items to fit into a limited capacity knapsack. It has numerous real-world applications, such as resource allocation and budget planning.

What are the different versions of the Knapsack problem?

There are three main versions of the Knapsack problem: the 0-1 Knapsack problem, the Bounded Knapsack problem, and the Unbounded Knapsack problem. The 0-1 Knapsack problem only allows items to be included or excluded as a whole, while the Bounded Knapsack problem sets a limit on the number of each item that can be included. The Unbounded Knapsack problem allows for an unlimited number of each item to be included.

How do you solve the Knapsack problem?

The Knapsack problem can be solved using a variety of algorithms, such as dynamic programming, greedy algorithms, and branch and bound methods. The best approach depends on the specific version of the problem and the size of the input.

What is the complexity of the Knapsack problem?

The Knapsack problem is known to be NP-hard, meaning that there is no known polynomial-time solution for solving it. However, for small inputs, optimal solutions can be found using dynamic programming or brute force methods. The complexity increases as the input size grows.

What are some real-world applications of the Knapsack problem?

The Knapsack problem has numerous applications in real-world scenarios, including resource allocation for project planning, portfolio optimization in finance, and cutting stock problems in manufacturing. It can also be applied to scheduling and routing problems, such as maximizing cargo load on a shipping vessel or fitting items into a delivery truck.

Similar threads

Replies
11
Views
2K
Replies
1
Views
1K
Replies
30
Views
5K
Replies
14
Views
4K
Replies
1
Views
1K
Replies
20
Views
3K
Back
Top