0/1 Knapsack is a typical problem that is used to demonstrate the application of greedy algorithms as well as dynamic programming. There are cases when applying the greedy algorithm does not give an optimal solution. There are many flavors in which *Knapsack problem* can be asked.

1. A thief enters a museum and wants to steal artifacts from there. Every artifact has a `weight`

and `value`

associated with it. Thief carries a knapsack (bag) which can take only a specific weight. Problem is to find the combination of artifacts thief steals so that he gets maximum value and weight of all taken artifacts is less capacity of knapsack he has. A thief cannot take any artifact partially. Either he takes it or leaves it. Hence the problem is 0/1 knapsack.

2. We have `N`

files each having a size say S_{i}. We have a total storage capacity of `W`

bytes. For each file to be stored re-computation cost is V_{i}. Problem is to store as many files on storage that combined size of all files is less than `W`

and their re-computation value is maximum. We can either store or leave a file, we cannot store a partial file. Hence this is a case of 0/1 knapsack problem.

### 0/1 knapsack problem : Line of thoughts

Brute force method would try all subsets of a set of items, whose weight adds up to the maximum capacity of knapsack and see which one gives maximum value. The complexity of the brute force algorithm would be of exponential order as there will be 2^{n} possible subsets for n items.

Can we do better? If we consider each item, there are two possibilities associated with it.

First,

Then we need to find out all the items in remaining N-1 items which can **current item is included in optimal **

**subset**.

**optimize the subproblem**for weight W-wk. Value of this item is added to candidate maximum value.

Second,

. In that case, we need to find out items in remaining N-1 items which can **current item is not included in optimal subset****optimize the the original problem**. Value of current item is not added into candidate maximum value.

Inclusion depends on two conditions :

- Weight of the item is less bthan the total capacity of knapsack.
- Inclusion of item increases current max value with K-1 items with W-Wk weight.

Since every steps reduces the problem to a smaller problem in terms of items of weight, recursive solution would be our first refuge. To implement this problem, what are the base cases? First, we cannot add any items to knapsack capacity is zero i.e. `W == 0`

. Second, no item can be taken if there are no items remaining, i.e. `n == 0.`

**Recursive implementation of 0/1 knapsack problem**

package com.company; /** * Created by sangar on 19.8.18. */ public class KnapsackProblem { static int knapSack(int W, int[] weights, int[] val, int n) { /* If there is no item or weight that can be carried is zero, return zero */ if (n < 0 || W == 0) return 0; /* If weight of the nth item is more than Knapsack capacity W,then this item cannot be included in the optimal solution */ if (weights[n] > W) return knapSack(W, weights, val, n - 1); /* Consider two cases, including item and excluding item.*/ else return Integer.max( (val[n] + knapSack(W - weights[n], weights, val, n - 1)), (knapSack(W, weights, val, n - 1)) ); } public static void main(String args[]) { int[] val = {60, 100, 120}; int[] wt = {10, 20, 30}; int W = 50; int n = val.length; System.out.println(knapSack(W, wt, val, n - 1)); } }

If we look at the execution trace of the function, it looks like this.

There are seven problems to be solved at the leaf level. For N = 3, there are 7 problems to be solved before we start optimizing for the max value. For N, in general, it will take 2^{N} subproblems to be solved. Hence, complexity of recursive implementation is O(2^{N}).

If we take another example, it will become evident that there are some subproblems that are solved again and again. Overlapping subproblems is one of the criteria, we should be thinking about dynamic programming. Also, notice that the optimal solution to a smaller problem leads to the optimal solution to a bigger problem, which is the second condition for DP. This problem satisfy both these conditions, hence let’s design DP solution for it.

## 0/1 knapsack problem : Dynamic programming approach

We define two dimensional array V[N,W] where N is number of items and W is capacity. For *1<= i <= n ** and 0<=w<=W*, V[i,w] represents the optimal solution for items I

_{1}, I

_{2}, ..I

_{n}with maximum weight of w. If we can compute all the entries of this array, then the array entry V[N,W] is the solution to our problem

For i =0 and w=0, all values will be zero. So, the first column and first row will be filled with all zero values.

Recursively, we can fill the table bottom-up as follows.

V[i, w ] = max (V[i-1, w], V[i-1, w-w[i]) + V[i] )

V[0, w ] = 0; there are no items

V[i, 0 ] = 0; no items can be picked.

package com.company; /** * Created by sangar on 19.8.18. */ public class KnapsackProblem { public static int knapsackDP(int W, int[] weights, int[] val, int n) { int[][] V = new int[n+1][W + 1]; for(int i = 1 ; i < V[0].length; i++){ /* If weight of item is less than current value we can achieve minimum value V[i] with 1..i items */ if(weights[0] <= i){ V[0][i] = val[0]; }else{ V[0][i] = 0; } } //Loop for all items for (int i = 1; i < V.length; i++) { for (int j = 1; j < V[i].length; j++) { /*if a weight is more than the allowed weight, that weight cannot be picked. */ if(weights[i] > j){ V[i][j] = V[i-1][j]; }else{ V[i][j] = Math.max(V[i-1][j], val[i] + V[i-1][j-weights[i]]); } } } return V[V.length-1][W]; } public static void main(String args[]) { int[] val = {60, 100, 120}; int[] wt = {10, 20, 30}; int W = 50; int n = val.length; System.out.println(knapsackDP(W, wt, val, n - 1)); } }

One similar problem which can be solved with the same approach is the minimum number of coins to be used to get a change of a particular amount.

Complexity of the dynamic programming implementation of *knapsack problem* is **O(N *W)**. Space complexity is again **O(N*W)**. It is thumb rule that we trade space for time in dynamic programming.

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