# 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

## Recommended: Please try your approach on {IDE} first, before moving on to the solution.

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++ implementation to print all ` `// the possible solutions of the ` `// 0/1 Knapsack problem ` ` `  `#include ` ` `  `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>> 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> possible; ` `         `  `        ``// Loop to fill up the bag  ` `        ``// untill 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; ` `} `

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

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