You are given a bag of size W kg and you are provided costs of packets different weights of oranges in array cost[] where **cost[i]** is basically cost of **‘i’** kg packet of oranges. Where cost[i] = -1 means that **‘i’** kg packet of orange is unavailable

Find the minimum total cost to buy exactly W kg oranges and if it is not possible to buy exactly W kg oranges then print -1. It may be assumed that there is infinite supply of all available packet types.

** Note :** array starts from index 1.

Examples:

Input : W = 5, cost[] = {20, 10, 4, 50, 100} Output : 14 We can choose two oranges to minimize cost. First orange of 2Kg and cost 10. Second orange of 3Kg and cost 4. Input : W = 5, cost[] = {1, 10, 4, 50, 100} Output : 5 We can choose five oranges of weight 1 kg. Input : W = 5, cost[] = {1, 2, 3, 4, 5} Output : 5 Costs of 1, 2, 3, 4 and 5 kg packets are 1, 2, 3, 4 and 5 Rs respectively. We choose packet of 5kg having cost 5 for minimum cost to get 5Kg oranges. Input : W = 5, cost[] = {-1, -1, 4, 5, -1} Output : -1 Packets of size 1, 2 and 5 kg are unavailable because they have cost -1. Cost of 3 kg packet is 4 Rs and of 4 kg is 5 Rs. Here we have only weights 3 and 4 so by using these two we can not make exactly W kg weight, therefore answer is -1.

This problem is can be reduced to 0-1 Knapsack Problem. So in cost array, we first ignore those packets which are not available i.e; cost is -1 and then traverse the cost array and create two array val[] for storing cost of **‘i’** kg packet of orange and wt[] for storing weight of corresponding packet. Suppose cost[i] = 50 so weight of packet will be i and cost will be 50.

**Algorithm :**

- Create matrix min_cost[n+1][W+1], where n is number of distinct weighted packets of orange and W is maximum capacity of bag.
- Initialize 0th row with INF (infinity) and 0th Column with 0.
- Now fill the matrix
- if wt[i-1] > j then min_cost[i][j] = min_cost[i-1][j] ;
- if wt[i-1] >= j then min_cost[i][j] = min(min_cost[i-1][j], val[i-1] + min_cost[i][j-wt[i-1]]);

- If min_cost[n][W]==INF then output will be -1 because this means that we cant not make make weight W by using these weights else output will be
**min_cost[n][W]**.

## C++

// C++ program to find minimum cost to get exactly // W Kg with given packets #include<bits/stdc++.h> #define INF 1000000 using namespace std; // cost[] initial cost array including unavailable packet // W capacity of bag int MinimumCost(int cost[], int n, int W) { // val[] and wt[] arrays // val[] array to store cost of 'i' kg packet of orange // wt[] array weight of packet of orange vector<int> val, wt; // traverse the original cost[] array and skip // unavailable packets and make val[] and wt[] // array. size variable tells the available number // of distinct weighted packets int size = 0; for (int i=0; i<n; i++) { if (cost[i]!= -1) { val.push_back(cost[i]); wt.push_back(i+1); size++; } } n = size; int min_cost[n+1][W+1]; // fill 0th row with infinity for (int i=0; i<=W; i++) min_cost[0][i] = INF; // fill 0'th column with 0 for (int i=1; i<=n; i++) min_cost[i][0] = 0; // now check for each weight one by one and fill the // matrix according to the condition for (int i=1; i<=n; i++) { for (int j=1; j<=W; j++) { // wt[i-1]>j means capacity of bag is // less then weight of item if (wt[i-1] > j) min_cost[i][j] = min_cost[i-1][j]; // here we check we get minimum cost either // by including it or excluding it else min_cost[i][j] = min(min_cost[i-1][j], min_cost[i][j-wt[i-1]] + val[i-1]); } } // exactly weight W can not be made by given weights return (min_cost[n][W]==INF)? -1: min_cost[n][W]; } // Driver program to run the test case int main() { int cost[] = {1, 2, 3, 4, 5}, W = 5; int n = sizeof(cost)/sizeof(cost[0]); cout << MinimumCost(cost, n, W); return 0; }

## Java

// JAVA Code for Minimum cost to // fill given weight in a bag import java.util.*; class GFG { // cost[] initial cost array including // unavailable packet W capacity of bag public static int MinimumCost(int cost[], int n, int W) { // val[] and wt[] arrays // val[] array to store cost of 'i' kg // packet of orange wt[] array weight of // packet of orange Vector<Integer> val = new Vector<Integer>(); Vector<Integer> wt = new Vector<Integer>(); // traverse the original cost[] array and skip // unavailable packets and make val[] and wt[] // array. size variable tells the available // number of distinct weighted packets int size = 0; for (int i = 0; i < n; i++) { if (cost[i] != -1) { val.add(cost[i]); wt.add(i + 1); size++; } } n = size; int min_cost[][] = new int[n+1][W+1]; // fill 0th row with infinity for (int i = 0; i <= W; i++) min_cost[0][i] = Integer.MAX_VALUE; // fill 0'th column with 0 for (int i = 1; i <= n; i++) min_cost[i][0] = 0; // now check for each weight one by one and // fill the matrix according to the condition for (int i = 1; i <= n; i++) { for (int j = 1; j <= W; j++) { // wt[i-1]>j means capacity of bag is // less then weight of item if (wt.get(i-1) > j) min_cost[i][j] = min_cost[i-1][j]; // here we check we get minimum cost // either by including it or excluding // it else min_cost[i][j] = Math.min(min_cost[i-1][j], min_cost[i][j-wt.get(i-1)] + val.get(i-1)); } } // exactly weight W can not be made by // given weights return (min_cost[n][W] == Integer.MAX_VALUE)? -1: min_cost[n][W]; } /* Driver program to test above function */ public static void main(String[] args) { int cost[] = {1, 2, 3, 4, 5}, W = 5; int n = cost.length; System.out.println(MinimumCost(cost, n, W)); } } // This code is contributed by Arnav Kr. Mandal.

## Python 3

# Python program to find minimum cost to get exactly # W Kg with given packets INF = 1000000 # cost[] initial cost array including unavailable packet # W capacity of bag def MinimumCost(cost, n, W): # val[] and wt[] arrays # val[] array to store cost of 'i' kg packet of orange # wt[] array weight of packet of orange val = list() wt= list() # traverse the original cost[] array and skip # unavailable packets and make val[] and wt[] # array. size variable tells the available number # of distinct weighted packets. size = 0 for i in range(n): if (cost[i] != -1): val.append(cost[i]) wt.append(i+1) size += 1 n = size min_cost = [[0 for i in range(W+1)] for j in range(n+1)] # fill 0th row with infinity for i in range(W+1): min_cost[0][i] = INF # fill 0th column with 0 for i in range(1, n+1): min_cost[i][0] = 0 # now check for each weight one by one and fill the # matrix according to the condition for i in range(1, n+1): for j in range(1, W+1): # wt[i-1]>j means capacity of bag is # less than weight of item if (wt[i-1] > j): min_cost[i][j] = min_cost[i-1][j] # here we check we get minimum cost either # by including it or excluding it else: min_cost[i][j] = min(min_cost[i-1][j], min_cost[i][j-wt[i-1]] + val[i-1]) # exactly weight W can not be made by given weights if(min_cost[n][W] == INF): return -1 else: return min_cost[n][W] # Driver program to run the test case cost = [1, 2, 3, 4, 5] W = 5 n = len(cost) print(MinimumCost(cost, n, W)) # This code is contributed by Soumen Ghosh.

Output:

5

This article is contributed by **Shashank Mishra ( Gullu )**.This article is reviewed by team GeeksForGeeks.

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