I have a file that has a number (a positive integer) on each row.

Given a number $q$, I want to find a value that's a sum of some 8 numbers in the file, and is as close to $q$ as possible.

So, supposing that I have a file that looks like this:


If the given number is 526 I would like to get a solution like: 3+2+6+7+34+12+48+413 = 525. So those are the 8 numbers that best fit our condition.

Is there an efficient algorithm with a low complexity?

The file can have >1000 rows and the numbers won't be bigger than 10k.

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    $\begingroup$ The way this is phrased, it looks a lot like an off-topic programming question, rather than an on-topic algorithms question. I suggest you read our help centre and edit your question to make it more clearly on-topic. $\endgroup$ Jun 15, 2015 at 18:54
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    $\begingroup$ Isn't this just a 01-backpack? Hmm, let me ask this way: is it ok if the sum is larger than the desired number? $\endgroup$ Jun 15, 2015 at 20:41
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    $\begingroup$ There are multiple possible algorithms: dynamic programming, or a tree-like method of merging sorted lists. The best algorithm will depend upon information you haven't given us. In particular: About how long is the file, i.e., about how many numbers will be in the file? And, about how large is the range the numbers come from? In other words, about how large is the largest number in the file? It's OK to give a rough order-of-magnitude estimate. I suggest editing the question to include this information; this will let us suggest a solution. $\endgroup$
    – D.W.
    Jun 15, 2015 at 22:58
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    $\begingroup$ You may want to have a look at the Meet-in-the-middle algorithm (en.wikipedia.org/wiki/Knapsack_problem) and adjust it to have 2 sets of partial solutions of length 4. This should yield about an O(n^4) algorithm. $\endgroup$ Jun 16, 2015 at 8:14
  • $\begingroup$ It's not off-topic. I've just came along with this problem from somebody that wanted it implemented and I was like "oh, it's easy" but it really is not as you have to handle big files. More, a bigger value won't fit to this case. $\endgroup$
    – Dex
    Jun 16, 2015 at 16:29

2 Answers 2


Dynamic programming

One approach is to use dynamic programming. If you have $n$ numbers ($n$ rows in the file), and each number is in the range $1..m$, then the obvious dynamic programming algorithm has running time about $8mn$. For your parameter size, this might be adequate.

In particular, let $A[1..n]$ be an array of your $n$ numbers. Define $T[x,i,j] = 1$ if there is a way to write $x$ as a sum of $j$ numbers from among $A[1..i]$, or 0 if not. Now we have the relation

$$T[x,i,j] = T[x,i-1,j] \lor T[x-A[i],i-1,j-1]$$

(with the convention that $T[x,i,j]$ always evaluates to 0 if $x$ is negative). Consequently, you can compute all of the $T[x,i,j]$ values iteratively. There are $8mn$ values to compute, so this takes $8mn$ time.

Once you have filled in the $T$ table, now you can answer any query quickly. Suppose you want to find the closest number to $q$ that can be written as a sum of 8 numbers. Then you should scan $T[q,n,8]$, $T[q+1,n,8]$, $T[q-1,n,8]$, $T[q+2,n,8]$, $T[q-2,n,8]$, etc., until you find the first entry that is one. This takes at most $m$ iterations.

In total, the running time is $O(mn)$, where the constant hidden by the big-O notation is small. The space required is $O(mn)$ as well, but this can be reduced to $O(m)$ if you fill in the $T$ table in the right order and discard entries once they are no longer needed.

Two-way merge

There's an alternative algorithm that will probably be significantly faster for your specific problem. The basic idea is to first find an algorithm that works for sums of 2 numbers, then apply that repeatedly.

Let $S$ be a set of numbers. Define $S \bowtie S$ to be the set

$$S \bowtie S = \{s+t : s, t \in T\}.$$

In other words, $S \bowtie S$ is the set of all possible sums of two values from $S$. In our case, we're going to represent $S$ as a sorted list of numbers.

It turns out you can compute $S \bowtie S$ from $S$, using a FFT-based convolution procedure. Let $N$ be the smallest power of two that is larger than $2m$. Define a $N$-vector $v$ whose $i$th coordinate is 1 if $i\in S$, or 0 otherwise. Compute $v \otimes v$, the convolution of $v$ with itself. It turns out that $v \otimes v$ is the $N$-vector for $S \bowtie S$, so you can recover $S \bowtie S$ from $v \otimes v$. Also, you can compute the convolution $v \otimes v$ using a discrete FFT over the $N$th complex roots of unity: you take the FFT of $v$, square every entry, then take the inverse FFT. The running time of the FFT is $O(N \lg N)$. Since $N=O(m)$, it follows that the running time of this procedure is $O(m \lg m)$.

Now we repeat this recursively three times. In other words, we let $S = $ the set of numbers in the original file, compute $T = S \bowtie S$ (the set of 2-way sums), compute $U = T \bowtie T$ (the set of 4-way sums), and then compute $V = U \bowtie U$ (the set of 8-way sums). This gives us the set of all numbers that can be represented as a 8-way sum.

If you like, a more elegant way to compute the set of 8-way sums is as follows: let $N$ be the smallest power of two that is larger than $8m$, form a $N$-vector $v$ where $v_i=1$ if and only if $i$ is part of the original file, take the discrete FFT of $v$ (over $N$th complex roots of unity), raise each entry to the 8th power, then take the inverse FFT. This gives you a $N$-vector that has a 1 in its $i$th coordinate if and only if $i$ is an attainable 8-way sum.

Finally, given a query $q$, we can quickly look it up in $V$ (using binary search, say) and find the nearest number to $q$ that's in $V$.

The total running time will be $O(m \lg m)$, and the total space will be $O(m)$. For your parameter settings, this should be extremely fast, given that $m$ is no larger than 10,000. However, the implementation complexity is much higher, since you need to implement a FFT, worry about round-off error and precision for complex numbers, and so on.


For the dynamic programming approach, I would first sort the numbers. Let $s_1$ be the sum of the largest 8 numbers. If $s_1 ≤ s$ then the solution is $s_1$.

Let $s_2$ be the sum of the smallest 8 numbers. If $s_2 ≥ s$ then the solution is $s_2$. (These two steps eliminate some extreme cases, especially when s is very small or very large compared to the other numbers)

Otherwise, replace the 8th smallest number with the next larger one as long as the sum is ≤ s. Then replace the 7th smallest number with the next larger one as long as the sum is ≤ s and the 7th smallest is not the same as the 8th smallest, and so on. Let $s_3 ≤ s$ be the sum that you obtained this way. If by coincidence $s_3 = s$ then $s_3$ is the solution. Otherwise, $s_3$ may be quite close to $s$. Let $L = r_3$ and $R = s + (s - r_3)$; then the solution x of the problem is in the range $L ≤ x ≤ R ≤ 2s$. Having an upper bound for the solution will reduce the amount of work that needs doing. And as long as the sum of the 7 smallest and the largest number exceed R, we remove the largest number from the problem.

(In the example (sorted) 2 3 6 7 12 34 48 345 413 1111 1234 3453 we quickly find that the smallest 7 numbers add up to 112, making s_3 = 525, so solutions are in the range 525 ≤ x ≤ 527, which removes all numbers greater than 415, leaving only 9 numbers. )

Then I would sort the numbers in order of (distance to s/8), with the ones closest to s/8 coming first. As a result, the dynamic programming approach will from the start find sums of eight numbers close to s. Especially if we have many numbers, this increases our chances that a sum exactly equal to s is found quickly, in which case we are finished early. And whenever we find a sum between L and R, we can improve the bounds L ≤ x ≤ R for solutions.

Once all small numbers (all numbers ≤ s/8) are processed, and the smallest remaining number is X, the dynamic programming doesn't need to find any sums of seven numbers that are greater than R - X (since adding an eighth number gives a result > R), we don't need to find any sums of six numbers that are greater than R - 2X, and so on, which again will decrease the amount of work needed.

If there are no numbers much larger than s/8, and the largest number is X, then we can ignore all sums of seven numbers less than L - X. If the sum of the largest two numbers is $X_2$ then we can ignore all sums of six numbers less than L - $X_2$ and so on.


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