knapsack | various algorithms for solving knapsack problem | Learning library
kandi X-RAY | knapsack Summary
kandi X-RAY | knapsack Summary
Implementation of various algorithms for solving knapsack problem. Knapsack algorithms are implemented in GoLang, just run this command to generated the executable.
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Top functions reviewed by kandi - BETA
- KnapsackDynamicPrice computes the price of a set of items
- MesureInstance runs the MesureInstance command
- This function is the same as Mesos
- ReadInstance reads an instance
- MesureGenetic takes a number of initial generations and builds the initial population and initial population size .
- MuxureFPTASAcc is used to measure the accuracy of the desired accuracy
- MesureFPTAS runs the computeFPTAS and returns the total duration .
- KnapsackDynamicWeight computes the weight of a set of items
- MmesureHeuristic runs the scheduler
- MuxureBranchBound runs the minimum capacity bound to the specified capacity
knapsack Key Features
knapsack Examples and Code Snippets
Lines of Code : 56License : Permissive (MIT License)
Copydef knapsack_with_example_solution(W: int, wt: list, val: list):
"""
Solves the integer weights knapsack problem returns one of
the several possible optimal subsets.
Parameters
---------
W: int, the total maximum weight for def fractional_knapsack(
value: list[int], weight: list[int], capacity: int
) -> tuple[float, list[float]]:
"""
>>> value = [1, 3, 5, 7, 9]
>>> weight = [0.9, 0.7, 0.5, 0.3, 0.1]
>>> fractional_knapsa def knapsack(capacity: int, weights: list[int], values: list[int], counter: int) -> int:
"""
Returns the maximum value that can be put in a knapsack of a capacity cap,
whereby each weight w has a specific value val.
>>> c Community Discussions
Trending Discussions on knapsack
QUESTION
- Each item has an associated weight wi and profit pi
- With a maximum total weight Wmax
- There are categories of items and I have to choose exactly one item from each category
- Of course, the aim is to choose items to maximise the sum of the profits
Here, the best solution is (The little prince, Banana)
I have a similar problem and I'd like to find out the best way to code it but I can't figure out what version/ variation of the probleme this is, is it a known variation ?
...ANSWER
Answered 2022-Mar-19 at 17:06I’m not sure if there’s an existing variation that matches yours, but it’s easy to draw inference from the classical variant and solve this.
Classic variant has 2D dynamic programming (DP[N][W], where N and W are number of items and max weight).
In this variant, since we can only pick one of each category, you can use 3D DP like dp[i][w][j], which denotes the maximum value you can get from the first i items with weight w and j is a 0/1 int denoting whether an item from category number j has been selected or not.
I’ll leave the implementation, since the recursive relation is relatively simple and quite similar to the classic variant.
QUESTION
We are given an array Ai denoting the price of item i.
We have k amount of currency and we can buy the first item n times, second item n-1 times, third item n-2 times and so on. Find the maximum number of items which can be bought.
...ANSWER
Answered 2022-Mar-08 at 20:21If you want to maximize the number of items you can buy you don't have a Knapsack problem (in a Knapsack problem each item has some value or weight and you want to maximize the overall weight of the items you pack). A greedy solution should work for your problem. Just sort the items according to their price (you have to remember to keep track of how many times you can buy each item, e.g. using pairs) and then buy the cheapest available until you run out of money. This should give you an O(nlog(n)) solution.
QUESTION
I'm solving a knapsack similar problem: which is to print out the first combination of objects that has value above a number but weight below a limit.
I have tried:
...ANSWER
Answered 2022-Feb-28 at 07:11I tried this and it works for some samples:
QUESTION
I have a collection in MongoDB that represents the chores kids needs to do each day and whether they are done. Here is an example of a document.
...ANSWER
Answered 2022-Feb-21 at 15:44You could use the update method with aggregation pipeline to update the chores array. You would need to use the following pipeline to achieve the desired results:
QUESTION
Problem Statement:
While playing an RPG game, you were assigned to complete one of the hardest quests in this game. There are n monsters you'll need to defeat in this quest. Each monster i is described with two integer numbers - poweri and bonusi. To defeat this monster, you'll need at least poweri experience points. If you try fighting this monster without having enough experience points, you lose immediately.
You will also gain bonusi experience points if you defeat this monster. You can defeat monsters in any order. The quest turned out to be very hard - you try to defeat the monsters but keep losing repeatedly. Your friend told you that this quest is impossible to complete. Knowing that, you're interested, what is the maximum possible number of monsters you can defeat?
Input:
The first line contains an integer, n, denoting the number of monsters.
The next line contains an integer, e, denoting your initial experience.
Each line i of the n subsequent lines (where 0 ≤ i < n) contains an integer, poweri, which represents power of the corresponding monster.
Each line i of the n subsequent lines (where 0 ≤ i < n) contains an integer, bonusi, which represents bonus for defeating the corresponding monster.
Sample cases:
Input 2 123 78 130 10 0
Output 2
Output description
Initial experience level is 123 points. Defeat the first monster having power of 78 and bonus of 10. Experience level is now 123+10=133. Defeat the second monster.
What I have tried:
...ANSWER
Answered 2021-Aug-07 at 17:35You can just sort the monsters from lowest to highest power required and defeat them in that order.
QUESTION
I am working on a knapsack optimization exercise written in Python. The goal is to fill a knapsack with limited weight capacity with items with a certain value and weight resulting maximizing the total value of the knapsack. I ran into a problem with setting the variables when a better solution is found.
I've created a simplified version of the code without any constraints, demonstrating my problem. Since there are no constraints, the first solution is always the best solution since that is the solution where all items are taken.
There is an if statement in the code that sets the max_value and best_taken variables in case a better solution is found.
However: When the print statement at the end of the code prints the max_value and best_taken variables, the max_value shows the correct value (the sum of the first indices in the list of lists assigned to the items variable). The best_taken value always ends up as [0,0] where a value of [1,1] (=take both items) is expected.
What am I doing wrong?
...ANSWER
Answered 2021-Dec-14 at 13:51You need to copy local_taken. Assigning an array to an attribute just adds a reference to the same (mutable) array
self.best_taken = self.local_taken[:]
QUESTION
I'm working on a variation of the 0-1 knapsack problem using OR tools. I've managed to solve one type of variation, however, on this attempt, I'm having difficulty specifically with the objective function. Here is the code that I'm using. Basically, I want to sum each of the variables associated with an item in the objective function, and then if the item is selected the x[i,j] binary variable will be set to 1:
...ANSWER
Answered 2021-Dec-02 at 14:48most likely you data is a numpy.float(). Cast it to float in SetCoefficient.
QUESTION
I have just started learning dynamic programming and was able to do some of the basic problems, such as fibbonaci, the knapsack and a few more problems. Coming across the problem below, I got stuck and do not know how to proceed forward. What confuses me is what would be the base case in this case, and the overlapping problems. Not knowing this prevents me from developing a relation. They are not as apparent in this example as they were in the previous ones I have solved thus far.
...Suppose we are given some string origString, a string toMatch and some number maxNum greater than or equal to 0. How can we count in how many ways it is possible to take maxNum number of nonempty and nonoverlapping substrings of the string origString to make up the string toMatch?
Example:
If origString = "ppkpke", and toMatch = "ppke"
maxNum = 1: countWays("ppkpke", "ppke", 1) will give 0 because toMatch is not a substring of origString.
maxNum = 2: countWays("ppkpke", "ppke", 2) will give 4 because 4 different combinations of 2 substring made up of "ppkpke" can make "ppke". Those strings are "ppk" & "e", "pp" & "ke" , "p" & "pke" (excluding "p") and "p" & "pke" (excluding "k")
ANSWER
Answered 2021-Oct-12 at 07:28As an initial word of caution, I’d say that although my solution happens to match the expected output for the tiny test set, it is very likely wrong. It’s up to you to double-check it on other examples you may have etc.
The algorithm walks the longer string and tries to spread the shorter string over it. The incremental state of the algorithm consists of tuples of 3 elements:
- long string coordinate
i(origString[i] == toMatch[j]) - short string coordinate
j(origString[i] == toMatch[j]) - number of ways we made it into that^^^ state
Then we just walk along the strings over and over again, using stored, previously discovered state, and sum up the total number(s) of ways each state was achieved — in the typical dynamic programming fashion.
For a state to count as a solution, j must be at the end of the short string and the number of iterations of the dynamic algorithm must be equivalent to the number of substrings we wanted at that point (because each iteration added one substring).
It is not entirely clear to me from the assignment whether maxNum actually means something like “exactNum”, i.e. exactly that many substrings, or whether we should sum across all lower or equal numbers of substrings. So the function returns a dictionary like { #substrings : #decompositions }, so that the output can be adjusted as needed.
QUESTION
I came across one algorithm problem in the book Competitive programming handbook https://cses.fi/book/book.pdf: Integer partitions, Knapsack.
The problem is: Given an array of integers whose sum is n. Output all possible sums that can be formed by using a subset of integers.
The dynamic programming solution is understandable. Define dp(x,k) as a bool function that is true when we can use the first k numbers to sum up to x, and false otherwise. Then dp(x,k) = dp(x,k-1) | dp(x-A[k],k-1). The complexity is O(n^2).
However, the book described another solution that leverages one fact that the number of distinct numbers in the array is O(sqrt (n)), and then mentioned a solution by grouping similar numbers together. How does it work? The book seems hard to follow.
...ANSWER
Answered 2021-Oct-20 at 15:39I agree that the author should have been more explicit. My reconstruction is that, given an element a and an (n+1)-element bit array indicating which sums can be made by subsets of the previously considered elements, we can calculate in linear time the minimum number of copies of a that we need to make a particular sum, then threshold by how many copies of a we actually have. Python implementation below.
QUESTION
I'm learning Dynamic Programming, and have the book Grokking Algorithms
The example given there uses these images/values:
When I run the code below, which is supposed to implement the 0/1 Knapsack Problem in Python, I get the following output:
...ANSWER
Answered 2021-Oct-09 at 08:47+---+------+------+------+------+
| 0 | 0 | 0 | 0 | 0 |
| 0 | 1500 | 1500 | 1500 | 1500 | guitar
| 0 | 1500 | 1500 | 2000 | 3500 | laptop
| 0 | 1500 | 1500 | 2000 | 3500 | stereo
+---+------+------+------+------+
3500
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