0/1 Knapsack Problem to print all possible solutions

Given weights and profits of N items, put these items in a knapsack of capacity W. The task is to print all possible solutions to the problem in such a way that there are no remaining items left whose weight is less than the remaining capacity of the knapsack. Also, compute the maximum profit.
Examples:

Input: Profits[] = {60, 100, 120, 50} 
Weights[] = {10, 20, 30, 40}, W = 40 
Output:
10: 60, 20: 100, 
10: 60, 30: 120, 
Maximum Profit = 180 
Explanation:
Maximum profit from all the possible solutions is 180

Input: Profits[] = {60, 100, 120, 50} 
Weights[] = {10, 20, 30, 40}, W = 50 
Output:
10: 60, 20: 100, 
10: 60, 30: 120, 
20: 100, 30: 120, 
Maximum Profit = 220 
Explanation:
Maximum profit from all the possible solutions is 220 

Approach: The idea is to make pairs for the weight and the profits of the items and then try out all permutations of the array and including the weights until their is no such item whose weight is less than the remaining capacity of the knapsack. Meanwhile after including an item increment the profit for that solution by the profit of that item. 

Below is the implementation of the above approach:

C++

// C++ implementation to print all
// the possible solutions of the
// 0/1 Knapsack problem

#include <bits/stdc++.h>

using namespace std;

// Utility function to find the
// maximum of the two elements
int max(int a, int b) {
    return (a > b) ? a : b;
}

// Function to find the all the
// possible solutions of the
// 0/1 knapSack problem
int knapSack(int W, vector<int> wt,
            vector<int> val, int n)
{
    // Mapping weights with Profits
    map<int, int> umap;

    set<vector<pair<int, int>>> set_sol;
    // Making Pairs and inserting
    // into the map
    for (int i = 0; i < n; i++) {
        umap.insert({ wt[i], val[i] });
    }

    int result = INT_MIN;
    int remaining_weight;
    int sum = 0;

    // Loop to iterate over all the
    // possible permutations of array
    do {
        sum = 0;

        // Initially bag will be empty
        remaining_weight = W;
        vector<pair<int, int>> possible;

        // Loop to fill up the bag
        // until there is no weight
        // such which is less than
        // remaining weight of the
        // 0-1 knapSack
        for (int i = 0; i < n; i++) {
            if (wt[i] <= remaining_weight) {

                remaining_weight -= wt[i];
                auto itr = umap.find(wt[i]);
                sum += (itr->second);
                possible.push_back({itr->first,
                     itr->second
                });
            }
        }
        sort(possible.begin(), possible.end());
        if (sum > result) {
            result = sum;
        }
        if (set_sol.find(possible) ==
                        set_sol.end()){
            for (auto sol: possible){
                cout << sol.first << ": "
                     << sol.second << ", ";
            }
            cout << endl;
            set_sol.insert(possible);
        }

    } while (
        next_permutation(wt.begin(),
                           wt.end()));
    return result;
}

// Driver Code
int main()
{
    vector<int> val{ 60, 100, 120 };
    vector<int> wt{ 10, 20, 30 };
    int W = 50;
    int n = val.size();
    int maximum = knapSack(W, wt, val, n);
    cout << "Maximum Profit = ";
    cout << maximum;
    return 0;
}

Java

// Java implementation to print all
// the possible solutions of the
// 0/1 Knapsack problem

import java.util.*;

public class Main {

    // Utility function to find the maximum of the two
    // elements
    static int max(int a, int b) { return (a > b) ? a : b; }

    // Function to find the all the possible solutions of
    // the 0/1 knapSack problem
    static int knapSack(int W, List<Integer> wt,
                        List<Integer> val, int n)
    {

        // Mapping weights with Profits
        Map<Integer, Integer> umap = new HashMap<>();

        Set<List<Map.Entry<Integer, Integer> > > setSol
            = new HashSet<>();

        // Making Pairs and inserting into the map
        for (int i = 0; i < n; i++) {
            umap.put(wt.get(i), val.get(i));
        }

        int result = Integer.MIN_VALUE;
        int remaining_weight;
        int sum = 0;

        // Loop to iterate over all the possible
        // permutations of array
        do {
            sum = 0;

            // Initially bag will be empty
            remaining_weight = W;
            List<Map.Entry<Integer, Integer> > possible
                = new ArrayList<>();

            // Loop to fill up the bag until there is no
            // weight such which is less than remaining
            // weight of the 0-1 knapSack
            for (int i = 0; i < n; i++) {
                if (wt.get(i) <= remaining_weight) {

                    remaining_weight -= wt.get(i);
                    Integer valAtWtI = umap.get(wt.get(i));
                    sum += valAtWtI;
                    possible.add(
                        new AbstractMap.SimpleEntry<>(
                            wt.get(i), valAtWtI));
                }
            }
            Collections.sort(
                possible,
                Comparator.comparingInt(Map.Entry::getKey));
            if (sum > result) {
                result = sum;
            }
            if (!setSol.contains(possible)) {
                for (Map.Entry<Integer, Integer> sol :
                     possible) {
                    System.out.print(sol.getKey() + ": "
                                     + sol.getValue()
                                     + ", ");
                }
                System.out.println();
                setSol.add(possible);
            }

        } while (nextPermutation(wt));

        return result;
    }

    // Utility function to generate the next permutation
    static boolean nextPermutation(List<Integer> arr)
    {
        int i = arr.size() - 2;
        while (i >= 0 && arr.get(i) >= arr.get(i + 1)) {
            i--;
        }
        if (i < 0) {
            return false;
        }
        int j = arr.size() - 1;
        while (arr.get(j) <= arr.get(i)) {
            j--;
        }
        int temp = arr.get(i);
        arr.set(i, arr.get(j));
        arr.set(j, temp);

        Collections.reverse(arr.subList(i + 1, arr.size()));
        return true;
    }

    // Driver code
    public static void main(String[] args)
    {
        List<Integer> val
            = new ArrayList<>(Arrays.asList(60, 100, 120));
        List<Integer> wt
            = new ArrayList<>(Arrays.asList(10, 20, 30));
        int W = 50;
        int n = val.size();
        int maximum = knapSack(W, wt, val, n);
        System.out.println("Maximum Profit = " + maximum);
    }
}
// This code was contributed by rutikbhosale

Python3

# Python3 implementation to print all
# the possible solutions of the
# 0/1 Knapsack problem


INT_MIN=-2147483648
def nextPermutation(nums: list) -> None:
        """
        Do not return anything, modify nums in-place instead.
        """
        if sorted(nums,reverse=True)==nums:
            return None
        n=len(nums)
        brk_point=-1
        for pos in range(n-1,0,-1):
            if nums[pos]>nums[pos-1]:
                brk_point=pos
                break
        else:
            nums.sort()
            return
        replace_with=-1
        for j in range(brk_point,n):
            if nums[j]>nums[brk_point-1]:
                replace_with=j
            else:
                break
        nums[replace_with],nums[brk_point-1]=nums[brk_point-1],nums[replace_with]
        nums[brk_point:]=sorted(nums[brk_point:])
        return nums

# Function to find the all the
# possible solutions of the
# 0/1 knapSack problem
def knapSack(W, wt, val, n):
    # Mapping weights with Profits
    umap=dict()

    set_sol=set()
    # Making Pairs and inserting
    # o the map
    for i in range(n) :
        umap[wt[i]]=val[i]


    result = INT_MIN
    remaining_weight=0
    sum = 0

    # Loop to iterate over all the
    # possible permutations of array
    while True:
        sum = 0

        # Initially bag will be empty
        remaining_weight = W
        possible=[]

        # Loop to fill up the bag
        # until there is no weight
        # such which is less than
        # remaining weight of the
        # 0-1 knapSack
        for i in range(n) :
            if (wt[i] <= remaining_weight) :

                remaining_weight -= wt[i]
                sum += (umap[wt[i]])
                possible.append((wt[i],
                     umap[wt[i]])
                )


        possible.sort()
        if (sum > result) :
            result = sum

        if (tuple(possible) not in set_sol):
            for sol in possible:
                print(sol[0], ": ", sol[1], ", ",end='')

            print()
            set_sol.add(tuple(possible))


        if not nextPermutation(wt):
            break
    return result


# Driver Code
if __name__ == '__main__':
    val=[60, 100, 120]
    wt=[10, 20, 30]
    W = 50
    n = len(val)
    maximum = knapSack(W, wt, val, n)
    print("Maximum Profit =",maximum)

#This code was contributed by Amartya Ghosh

Javascript

// Utility function to find the maximum of the two elements
function max(a, b) {
    return (a > b) ? a : b;
}

// Function to find the all the possible solutions of the 0/1 knapSack problem
function knapSack(W, wt, val, n) {
    // Mapping weights with Profits
    let umap = new Map();
    let set_sol = new Set();

    // Making Pairs and inserting into the map
    for (let i = 0; i < n; i++) {
        umap.set(wt[i], val[i]);
    }

    let result = Number.MIN_SAFE_INTEGER;
    let remaining_weight, sum;

    // Loop to iterate over all the possible permutations of array
    do {
        sum = 0;

        // Initially bag will be empty
        remaining_weight = W;
        let possible = [];

        // Loop to fill up the bag until there is no weight such which is less than remaining weight of the 0-1 knapSack
        for (let i = 0; i < n; i++) {
            if (wt[i] <= remaining_weight) {
                remaining_weight -= wt[i];
                let val = umap.get(wt[i]);
                sum += val;
                possible.push([wt[i], val]);
            }
        }

        possible.sort((a, b) => a[0] - b[0]);

        if (sum > result) {
            result = sum;
        }

        if (!set_sol.has(JSON.stringify(possible))) {
            for (let i = 0; i < possible.length; i++) {
                console.log(possible[i][0] + ": " + possible[i][1] + ", ");
            }

            console.log();
            set_sol.add(JSON.stringify(possible));
        }
    } while (nextPermutation(wt));

    return result;
}

// Function to generate the next permutation of array
function nextPermutation(a) {
    let i = a.length - 2;
    while (i >= 0 && a[i] >= a[i + 1]) {
        i--;
    }

    if (i < 0) {
        return false;
    }

    let j = a.length - 1;
    while (a[j] <= a[i]) {
        j--;
    }

    let temp = a[i];
    a[i] = a[j];
    a[j] = temp;

    for (let l = i + 1, r = a.length - 1; l < r; l++, r--) {
        temp = a[l];
        a[l] = a[r];
        a[r] = temp;
    }

    return true;
}

// Driver Code
function main() {
    let val = [60, 100, 120];
    let wt = [10, 20, 30];
    let W = 50;
    let n = val.length;
    let maximum = knapSack(W, wt, val, n);
    console.log("Maximum Profit = " + maximum);
}

main();

C#

using System;
using System.Collections.Generic;
using System.Linq;

class MainClass {
    // Utility function to find the maximum of the two elements
    static int max(int a, int b) { return (a > b) ? a : b; }

    // Function to find the all the possible solutions of
    // the 0/1 knapSack problem
    static int knapSack(int W, List<int> wt,
                        List<int> val, int n)
    {

        // Mapping weights with Profits
        Dictionary<int, int> umap = new Dictionary<int, int>();

        HashSet<List<KeyValuePair<int, int>>> setSol
            = new HashSet<List<KeyValuePair<int, int>>>();

        // Making Pairs and inserting into the map
        for (int i = 0; i < n; i++) {
            umap.Add(wt[i], val[i]);
        }

        int result = int.MinValue;
        int remaining_weight;
        int sum = 0;

        // Loop to iterate over all the possible permutations of array
        do {
            sum = 0;

            // Initially bag will be empty
            remaining_weight = W;
            List<KeyValuePair<int, int>> possible
                = new List<KeyValuePair<int, int>>();

            // Loop to fill up the bag until there is no
            // weight such which is less than remaining
            // weight of the 0-1 knapSack
            for (int i = 0; i < n; i++) {
                if (wt[i] <= remaining_weight) {

                    remaining_weight -= wt[i];
                    int valAtWtI = umap[wt[i]];
                    sum += valAtWtI;
                    possible.Add(
                        new KeyValuePair<int, int>(
                            wt[i], valAtWtI));
                }
            }
            possible.Sort(
                (x, y) => x.Key.CompareTo(y.Key));
            if (sum > result) {
                result = sum;
            }
            if (!setSol.Contains(possible)) {
                foreach (KeyValuePair<int, int> sol in possible) {
                    Console.Write(sol.Key + ": " + sol.Value
                                     + ", ");
                }
                Console.WriteLine();
                setSol.Add(possible);
            }

        } while (nextPermutation(wt));

        return result;
    }

    // Utility function to generate the next permutation
    static bool nextPermutation(List<int> arr)
    {
        int i = arr.Count - 2;
        while (i >= 0 && arr[i] >= arr[i + 1]) {
            i--;
        }
        if (i < 0) {
            return false;
        }
        int j = arr.Count - 1;
        while (arr[j] <= arr[i]) {
            j--;
        }
        int temp = arr[i];
        arr[i] = arr[j];
        arr[j] = temp;

        arr.Reverse(i + 1, arr.Count - i - 1);
        return true;
    }

    // Driver code
    public static void Main(string[] args)
    {
        List<int> val = new List<int>{60, 100, 120};
        List<int> wt = new List<int>{10, 20, 30};
        int W = 50;
        int n = val.Count;
        int maximum = knapSack(W, wt, val, n);
        Console.WriteLine("Maximum Profit = " + maximum);
    }
}

Output

10: 60, 20: 100, 
10: 60, 30: 120, 
20: 100, 30: 120, 
Maximum Profit = 220

Time complexity : O(N! * N), where N is the number of items. The code uses permutation to generate all possible combinations of items and then performs a search operation to find the optimal solution. 

Space complexity : O(N), as the code uses a map and set to store the solution, which has a maximum size of N.