Fractional Knapsack Problem

Given weights and values of n items, we need to put these items in a knapsack of capacity W to get the maximum total value in the knapsack.

In the 0-1 Knapsack problem, we are not allowed to break items. We either take the whole item or don’t take it.

  Items as (value, weight) pairs
  arr[] = {{60, 10}, {100, 20}, {120, 30}}
  Knapsack Capacity, W = 50;
  Maximum possible value = 220
  by taking items of weight 20 and 30 kg 

In Fractional Knapsack, we can break items for maximizing the total value of knapsack. This problem in which we can break an item is also called the fractional knapsack problem.

Input : 
   Same as above
Output :
   Maximum possible value = 240
   By taking full items of 10 kg, 20 kg and 
   2/3rd of last item of 30 kg

A brute-force solution would be to try all possible subset with all different fraction but that will be too much time taking.

An efficient solution is to use Greedy approach. The basic idea of the greedy approach is to calculate the ratio value/weight for each item and sort the item on basis of this ratio. Then take the item with the highest ratio and add them until we can’t add the next item as a whole and at the end add the next item as much as we can. Which will always be the optimal solution to this problem.

A simple code with our own comparison function can be written as follows, please see sort function more closely, the third argument to sort function is our comparison function which sorts the item according to value/weight ratio in non-decreasing order.
After sorting we need to loop over these items and add them in our knapsack satisfying above-mentioned criteria.


// C/C++ program to solve fractional Knapsack Problem
#include <bits/stdc++.h>
using namespace std;
// Structure for an item which stores weight and corresponding
// value of Item
struct Item
    int value, weight;
    // Constructor
    Item(int value, int weight) : value(value), weight(weight)
// Comparison function to sort Item according to val/weight ratio
bool cmp(struct Item a, struct Item b)
    double r1 = (double)a.value / a.weight;
    double r2 = (double)b.value / b.weight;
    return r1 > r2;
// Main greedy function to solve problem
double fractionalKnapsack(int W, struct Item arr[], int n)
    //    sorting Item on basis of ratio
    sort(arr, arr + n, cmp);
    //    Uncomment to see new order of Items with their ratio
    for (int i = 0; i < n; i++)
        cout << arr[i].value << "  " << arr[i].weight << " : "
             << ((double)arr[i].value / arr[i].weight) << endl;
    int curWeight = 0;  // Current weight in knapsack
    double finalvalue = 0.0; // Result (value in Knapsack)
    // Looping through all Items
    for (int i = 0; i < n; i++)
        // If adding Item won't overflow, add it completely
        if (curWeight + arr[i].weight <= W)
            curWeight += arr[i].weight;
            finalvalue += arr[i].value;
        // If we can't add current Item, add fractional part of it
            int remain = W - curWeight;
            finalvalue += arr[i].value * ((double) remain / arr[i].weight);
    // Returning final value
    return finalvalue;
// driver program to test above function
int main()
    int W = 50;   //    Weight of knapsack
    Item arr[] = {{60, 10}, {100, 20}, {120, 30}};
    int n = sizeof(arr) / sizeof(arr[0]);
    cout << "Maximum value we can obtain = "
         << fractionalKnapsack(W, arr, n);
    return 0;


// Java program to solve fractional Knapsack Problem
import java.util.Arrays;
import java.util.Comparator;
//Greedy approach
public class FractionalKnapSack {
//Time complexity O(n log n)
    public static void main(String[] args){
        int[] wt = {10, 40, 20, 30};
        int[] val = {60, 40, 100, 120};
        int capacity = 50;
        double maxValue = getMaxValue(wt, val, capacity);
        System.out.println("Maximum value we can obtain = "+maxValue);
// // function to get maximum value
    private static double getMaxValue(int[] wt, int[] val, int capacity){
    ItemValue[] iVal = new ItemValue[wt.length];
        for(int i = 0; i < wt.length; i++){
            iVal[i] = new ItemValue(wt[i], val[i], i);
        //sorting items by value;
        Arrays.sort(iVal, new Comparator<ItemValue>() {
            public int compare(ItemValue o1, ItemValue o2) {
                return o2.cost.compareTo(o1.cost) ;
        double totalValue = 0d;
        for(ItemValue i: iVal){
            int curWt = (int) i.wt;
            int curVal = (int) i.val;
            if (capacity - curWt >= 0){//this weight can be picked while
                capacity = capacity-curWt;
                totalValue += curVal;
            }else{//item cant be picked whole
                double fraction = ((double)capacity/(double)curWt);
                totalValue += (curVal*fraction);
                capacity = (int)(capacity - (curWt*fraction));
        return totalValue;
    // item value class
    static class ItemValue {
        Double cost;
        double wt, val, ind;
        // item value function
        public ItemValue(int wt, int val, int ind){
            this.wt = wt;
            this.val = val;
            this.ind = ind;
            cost = new Double(val/wt );

Output :

Maximum value in Knapsack = 240

As main time taking step is sorting, the whole problem can be solved in O(n log n) only.
This article is contributed by Utkarsh Trivedi.

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Improved By : Prashant Mishra 9

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