The knapsack problem is NP-hard and can be formulated as: $$\begin{align}&\text{maximize } \sum_{i=1}^n v_i x_i,\tag{P1}\\& \text{subject to } \sum_{i=1}^n w_i x_i \leq W,\\&\text{and } x_i \in \{0,1\}.\end{align}$$

What if $v_i=i$? Is it still NP-hard?

$$\begin{align}&\text{maximize } \sum_{i=1}^n ix_i,\tag{P2}\\& \text{subject to } \sum_{i=1}^n w_i x_i \leq W,\\&\text{and } x_i \in \{0,1\}.\end{align}$$

I am trying to reduce (P1) to (P2). Given an instance of (P1), I create the same number of items, same weights. Now, I have to relate the solutions to (P1) and (P2).

  • $\begingroup$ You cannot use the same weights... One problem is that values might be quite large. Suppose you have a value of $2^n$ (takes $n$ bits to encode). Then you will need $2^n$ variables, which presumably would take at least $2^n$ bits to encode (unless you allow sparse encodings – you don't specify this). Another problem is that weights could repeat. $\endgroup$ Apr 12, 2020 at 21:51

1 Answer 1


No, this problem is in $\mathbf{P}$. The main point being that the sum of all the values is $1 + 2 \ldots + n = \frac{n(n+1)}{2} = \mathcal{O}(n^2)$, which is polynomial in the input size. Without the extra restriction of $v_i = i$, the sum of all the values could be exponential in the input size, as the values are represented in binary in the input.

So we can define $DP[i][v]$, where $1 \leq i \leq n$ and $0 \leq v \leq \frac{n(n+1)}{2}$, to be the minimum weight needed to get a value of at least $v$, using only the first $i$ items. If it cannot be achieved, we'll set $DP[i][v] = INF$. The final answer is the maximum $v$ such that $DP[n][v] \leq W$.

The DP values can be calculated as follows:

$DP[i][v] = \min(DP[i-1][v], w_i + DP[i-1][v - v_i])$.

The first term considers the possibility of the $i^{th}$ item not being chosen, and the second term is the possibility that it is chosen.

So the entire problem can be solved in $\mathcal{O}(n^3)$ time and $\mathcal{O}(n^3)$ space, which can be reduced to $\mathcal{O}(n^2)$ space by just reusing two arrays of size $\frac{n(n+1)}{2} + 1$.

  • $\begingroup$ What makes this polytime and solving the normal 0-1 knapsack problem using the same approach not polytime? I mean if I define the same table $DP[i][v]$ for (P1) in the question, what would the complexity be? Is it $O(nV)$, where $V$ is the sum of values? $\endgroup$
    – zdm
    Apr 14, 2020 at 21:31
  • $\begingroup$ @zdm Yes, the exact same algorithm would work for the normal problem as well, but in time $\mathcal{O}(nV)$, as you point out. That would be a pseudo-polynomial time algorithm in the general problem, but is polynomial time in this setting because we have the extra bound on its values. The other more common pseudo-poly time algo for knapsack is $\mathcal{O}(nW)$, as can be seen here. $\endgroup$
    – CodeChef
    Apr 15, 2020 at 1:24

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