Algorithm to Find Which Numbers from a List of Size N Sum to Another Number

Algorithm to find which numbers from a list of size n sum to another number

Interesting answers. Thank you for the pointers to Wikipedia - whilst interesting - they don't actually solve the problem as stated as I was looking for exact matches - more of an accounting/book balancing problem than a traditional bin-packing / knapsack problem.

I have been following the development of stack overflow with interest and wondered how useful it would be. This problem came up at work and I wondered whether stack overflow could provide a ready-made answer (or a better answer) quicker than I could write it myself. Thanks also for the comments suggesting this be tagged homework - I guess that is reasonably accurate in light of the above.

For those who are interested, here is my solution which uses recursion (naturally) I also changed my mind about the method signature and went for List> rather than decimal[][] as the return type:

public class Solver {

    private List<List<decimal>> mResults;

    public List<List<decimal>> Solve(decimal goal, decimal[] elements) {

        mResults = new List<List<decimal>>();
        RecursiveSolve(goal, 0.0m, 
            new List<decimal>(), new List<decimal>(elements), 0);
        return mResults; 
    }

    private void RecursiveSolve(decimal goal, decimal currentSum, 
        List<decimal> included, List<decimal> notIncluded, int startIndex) {

        for (int index = startIndex; index < notIncluded.Count; index++) {

            decimal nextValue = notIncluded[index];
            if (currentSum + nextValue == goal) {
                List<decimal> newResult = new List<decimal>(included);
                newResult.Add(nextValue);
                mResults.Add(newResult);
            }
            else if (currentSum + nextValue < goal) {
                List<decimal> nextIncluded = new List<decimal>(included);
                nextIncluded.Add(nextValue);
                List<decimal> nextNotIncluded = new List<decimal>(notIncluded);
                nextNotIncluded.Remove(nextValue);
                RecursiveSolve(goal, currentSum + nextValue,
                    nextIncluded, nextNotIncluded, startIndex++);
            }
        }
    }
}

If you want an app to test this works, try this console app code:

class Program {
    static void Main(string[] args) {

        string input;
        decimal goal;
        decimal element;

        do {
            Console.WriteLine("Please enter the goal:");
            input = Console.ReadLine();
        }
        while (!decimal.TryParse(input, out goal));

        Console.WriteLine("Please enter the elements (separated by spaces)");
        input = Console.ReadLine();
        string[] elementsText = input.Split(' ');
        List<decimal> elementsList = new List<decimal>();
        foreach (string elementText in elementsText) {
            if (decimal.TryParse(elementText, out element)) {
                elementsList.Add(element);
            }
        }

        Solver solver = new Solver();
        List<List<decimal>> results = solver.Solve(goal, elementsList.ToArray());
        foreach(List<decimal> result in results) {
            foreach (decimal value in result) {
                Console.Write("{0}\t", value);
            }
            Console.WriteLine();
        }

        Console.ReadLine();
    }
}

I hope this helps someone else get their answer more quickly (whether for homework or otherwise).

Cheers...

Algorithm to find which number in a list sum up to a certain number

This problem reduces to the 0-1 Knapsack Problem, where you are trying to find a set with an exact sum. The solution depends on the constraints, in the general case this problem is NP-Complete.

However, if the maximum search sum (let's call it S) is not too high, then you can solve the problem using dynamic programming. I will explain it using a recursive function and memoization, which is easier to understand than a bottom-up approach.

Let's code a function f(v, i, S), such that it returns the number of subsets in v[i:] that sums exactly to S. To solve it recursively, first we have to analyze the base (i.e.: v[i:] is empty):

• S == 0: The only subset of [] has sum 0, so it is a valid subset. Because of this, the function should return 1.

• S != 0: As the only subset of [] has sum 0, there is not a valid subset. Because of this, the function should return 0.

Then, let's analyze the recursive case (i.e.: v[i:] is not empty). There are two choices: include the number v[i] in the current subset, or not include it. If we include v[i], then we are looking subsets that have sum S - v[i], otherwise, we are still looking for subsets with sum S. The function f might be implemented in the following way:

def f(v, i, S):
  if i >= len(v): return 1 if S == 0 else 0
  count = f(v, i + 1, S)
  count += f(v, i + 1, S - v[i])
  return count

v = [1, 2, 3, 10]
sum = 12
print(f(v, 0, sum))

By checking f(v, 0, S) > 0, you can know if there is a solution to your problem. However, this code is too slow, each recursive call spawns two new calls, which leads to an O(2^n) algorithm. Now, we can apply memoization to make it run in time O(n*S), which is faster if S is not too big:

def f(v, i, S, memo):
  if i >= len(v): return 1 if S == 0 else 0
  if (i, S) not in memo:  # <-- Check if value has not been calculated.
    count = f(v, i + 1, S, memo)
    count += f(v, i + 1, S - v[i], memo)
    memo[(i, S)] = count  # <-- Memoize calculated result.
  return memo[(i, S)]     # <-- Return memoized value.

v = [1, 2, 3, 10]
sum = 12
memo = dict()
print(f(v, 0, sum, memo))

Now, it is possible to code a function g that returns one subset that sums S. To do this, it is enough to add elements only if there is at least one solution including them:

def f(v, i, S, memo):
  # ... same as before ...

def g(v, S, memo):
  subset = []
  for i, x in enumerate(v):
    # Check if there is still a solution if we include v[i]
    if f(v, i + 1, S - x, memo) > 0:
      subset.append(x)
      S -= x
  return subset

v = [1, 2, 3, 10]
sum = 12
memo = dict()
if f(v, 0, sum, memo) == 0: print("There are no valid subsets.")
else: print(g(v, sum, memo))

Disclaimer: This solution says there are two subsets of [10, 10] that sums 10. This is because it assumes that the first ten is different to the second ten. The algorithm can be fixed to assume that both tens are equal (and thus answer one), but that is a bit more complicated.

Algorithm to find a list of numbers that sum up to a certain number

Python has an extensive standard library which is pretty fast and useful.
In this case, itertools' combinations_with_replacement does the trick.

You just need to pick the combinations that sum up to N

For example, this code

import itertools as it

N = 14
SIZE = 6

lst = range(N+1)
sum_n_combs = [
    comb for comb in it.combinations_with_replacement(lst, SIZE)
    if sum(comb) == N
]
print(sum_n_combs)

produces

[(0, 0, 0, 0, 0, 14), (0, 0, 0, 0, 1, 13), (0, 0, 0, 0, 2, 12), (0, 0, 0, 0, 3, 11), (0, 0, 0, 0, 4, 10), (0, 0, 0, 0, 5, 9), (0, 0, 0, 0, 6, 8), (0, 0, 0, 0, 7, 7), (0, 0, 0, 1, 1, 12), (0, 0, 0, 1, 2, 11), (0, 0, 0, 1, 3, 10), (0, 0, 0, 1, 4, 9), (0, 0, 0, 1, 5, 8), (0, 0, 0, 1, 6, 7), (0, 0, 0, 2, 2, 10), (0, 0, 0, 2, 3, 9), (0, 0, 0, 2, 4, 8), (0, 0, 0, 2, 5, 7), (0, 0, 0, 2, 6, 6), (0, 0, 0, 3, 3, 8), (0, 0, 0, 3, 4, 7), (0, 0, 0, 3, 5, 6), (0, 0, 0, 4, 4, 6), (0, 0, 0, 4, 5, 5), (0, 0, 1, 1, 1, 11), (0, 0, 1, 1, 2, 10), (0, 0, 1, 1, 3, 9), (0, 0, 1, 1, 4, 8), (0, 0, 1, 1, 5, 7), (0, 0, 1, 1, 6, 6), (0, 0, 1, 2, 2, 9), (0, 0, 1, 2, 3, 8), (0, 0, 1, 2, 4, 7), (0, 0, 1, 2, 5, 6), (0, 0, 1, 3, 3, 7), (0, 0, 1, 3, 4, 6), (0, 0, 1, 3, 5, 5), (0, 0, 1, 4, 4, 5), (0, 0, 2, 2, 2, 8), (0, 0, 2, 2, 3, 7), (0, 0, 2, 2, 4, 6), (0, 0, 2, 2, 5, 5), (0, 0, 2, 3, 3, 6), (0, 0, 2, 3, 4, 5), (0, 0, 2, 4, 4, 4), (0, 0, 3, 3, 3, 5), (0, 0, 3, 3, 4, 4), (0, 1, 1, 1, 1, 10), (0, 1, 1, 1, 2, 9), (0, 1, 1, 1, 3, 8), (0, 1, 1, 1, 4, 7), (0, 1, 1, 1, 5, 6), (0, 1, 1, 2, 2, 8), (0, 1, 1, 2, 3, 7), (0, 1, 1, 2, 4, 6), (0, 1, 1, 2, 5, 5), (0, 1, 1, 3, 3, 6), (0, 1, 1, 3, 4, 5), (0, 1, 1, 4, 4, 4), (0, 1, 2, 2, 2, 7), (0, 1, 2, 2, 3, 6), (0, 1, 2, 2, 4, 5), (0, 1, 2, 3, 3, 5), (0, 1, 2, 3, 4, 4), (0, 1, 3, 3, 3, 4), (0, 2, 2, 2, 2, 6), (0, 2, 2, 2, 3, 5), (0, 2, 2, 2, 4, 4), (0, 2, 2, 3, 3, 4), (0, 2, 3, 3, 3, 3), (1, 1, 1, 1, 1, 9), (1, 1, 1, 1, 2, 8), (1, 1, 1, 1, 3, 7), (1, 1, 1, 1, 4, 6), (1, 1, 1, 1, 5, 5), (1, 1, 1, 2, 2, 7), (1, 1, 1, 2, 3, 6), (1, 1, 1, 2, 4, 5), (1, 1, 1, 3, 3, 5), (1, 1, 1, 3, 4, 4), (1, 1, 2, 2, 2, 6), (1, 1, 2, 2, 3, 5), (1, 1, 2, 2, 4, 4), (1, 1, 2, 3, 3, 4), (1, 1, 3, 3, 3, 3), (1, 2, 2, 2, 2, 5), (1, 2, 2, 2, 3, 4), (1, 2, 2, 3, 3, 3), (2, 2, 2, 2, 2, 4), (2, 2, 2, 2, 3, 3)]

Finding all possible combinations of numbers to reach a given sum

This problem can be solved with a recursive combinations of all possible sums filtering out those that reach the target. Here is the algorithm in Python:

def subset_sum(numbers, target, partial=[]):
    s = sum(partial)

    # check if the partial sum is equals to target
    if s == target: 
        print "sum(%s)=%s" % (partial, target)
    if s >= target:
        return  # if we reach the number why bother to continue
    
    for i in range(len(numbers)):
        n = numbers[i]
        remaining = numbers[i+1:]
        subset_sum(remaining, target, partial + [n]) 
   

if __name__ == "__main__":
    subset_sum([3,9,8,4,5,7,10],15)

    #Outputs:
    #sum([3, 8, 4])=15
    #sum([3, 5, 7])=15
    #sum([8, 7])=15
    #sum([5, 10])=15

This type of algorithms are very well explained in the following Stanford's Abstract Programming lecture - this video is very recommendable to understand how recursion works to generate permutations of solutions.

Edit

The above as a generator function, making it a bit more useful. Requires Python 3.3+ because of yield from.

def subset_sum(numbers, target, partial=[], partial_sum=0):
    if partial_sum == target:
        yield partial
    if partial_sum >= target:
        return
    for i, n in enumerate(numbers):
        remaining = numbers[i + 1:]
        yield from subset_sum(remaining, target, partial + [n], partial_sum + n)

Here is the Java version of the same algorithm:

package tmp;

import java.util.ArrayList;
import java.util.Arrays;

class SumSet {
    static void sum_up_recursive(ArrayList<Integer> numbers, int target, ArrayList<Integer> partial) {
       int s = 0;
       for (int x: partial) s += x;
       if (s == target)
            System.out.println("sum("+Arrays.toString(partial.toArray())+")="+target);
       if (s >= target)
            return;
       for(int i=0;i<numbers.size();i++) {
             ArrayList<Integer> remaining = new ArrayList<Integer>();
             int n = numbers.get(i);
             for (int j=i+1; j<numbers.size();j++) remaining.add(numbers.get(j));
             ArrayList<Integer> partial_rec = new ArrayList<Integer>(partial);
             partial_rec.add(n);
             sum_up_recursive(remaining,target,partial_rec);
       }
    }
    static void sum_up(ArrayList<Integer> numbers, int target) {
        sum_up_recursive(numbers,target,new ArrayList<Integer>());
    }
    public static void main(String args[]) {
        Integer[] numbers = {3,9,8,4,5,7,10};
        int target = 15;
        sum_up(new ArrayList<Integer>(Arrays.asList(numbers)),target);
    }
}

It is exactly the same heuristic. My Java is a bit rusty but I think is easy to understand.

C# conversion of Java solution: (by @JeremyThompson)

public static void Main(string[] args)
{
    List<int> numbers = new List<int>() { 3, 9, 8, 4, 5, 7, 10 };
    int target = 15;
    sum_up(numbers, target);
}

private static void sum_up(List<int> numbers, int target)
{
    sum_up_recursive(numbers, target, new List<int>());
}

private static void sum_up_recursive(List<int> numbers, int target, List<int> partial)
{
    int s = 0;
    foreach (int x in partial) s += x;

    if (s == target)
        Console.WriteLine("sum(" + string.Join(",", partial.ToArray()) + ")=" + target);

    if (s >= target)
        return;

    for (int i = 0; i < numbers.Count; i++)
    {
        List<int> remaining = new List<int>();
        int n = numbers[i];
        for (int j = i + 1; j < numbers.Count; j++) remaining.Add(numbers[j]);

        List<int> partial_rec = new List<int>(partial);
        partial_rec.Add(n);
        sum_up_recursive(remaining, target, partial_rec);
    }
}

Ruby solution: (by @emaillenin)

def subset_sum(numbers, target, partial=[])
  s = partial.inject 0, :+
# check if the partial sum is equals to target

  puts "sum(#{partial})=#{target}" if s == target

  return if s >= target # if we reach the number why bother to continue

  (0..(numbers.length - 1)).each do |i|
    n = numbers[i]
    remaining = numbers.drop(i+1)
    subset_sum(remaining, target, partial + [n])
  end
end

subset_sum([3,9,8,4,5,7,10],15)

Edit: complexity discussion

As others mention this is an NP-hard problem. It can be solved in exponential time O(2^n), for instance for n=10 there will be 1024 possible solutions. If the targets you are trying to reach are in a low range then this algorithm works. So for instance:

subset_sum([1,2,3,4,5,6,7,8,9,10],100000) generates 1024 branches because the target never gets to filter out possible solutions.

On the other hand subset_sum([1,2,3,4,5,6,7,8,9,10],10) generates only 175 branches, because the target to reach 10 gets to filter out many combinations.

If N and Target are big numbers one should move into an approximate version of the solution.

Is there an algorithm for finding amount of sublists whose sum is smaller than given K? Better than O(N^2)

Let the original array be A, and make an array S of cumulative sums, so that S[i] = Sum of the first i elements of A. Note that S will be one element longer than A.

Now, for each sublist of A from A[i] to A[j], the sum of its elements is S[j+1]-S[i]

To get your answer, all we need to do is count the number of pairs of indexes (i,j) in S with i < j and S[j]-S[i] < K.

You can do this with an order statistic tree: https://en.wikipedia.org/wiki/Order_statistic_tree

This is a binary search tree augmented with records of each subtree's size, which lets you answer questions like "how many entries have values <= x" in O(log N) time.

Start with the tree empty, then:

for j=1 to S.length-1:
    add S[j-1] to the tree
    query for the number of elements in the tree with values > S[j] - K
    add that count to the total

Total complexity is O(N log N) (where N is the size of the input!), dominated by the O(log N) add and query operations in the tree for each j.

How to write an algorithm to check if the sum of any two numbers in an array/list matches a given number?

I'm sure there's a better way, but here's an idea:

1. Sort array
2. For every element e in the array, binary search for the complement (sum - e)

Both these operations are O(n log n).

Find set of numbers in one collection that adds up to a number in another

This problem is NP-Complete... This is some variation of the sub-set sum problem which is known to be NP-Complete (actually, the sub-set sum problem is easier than yours).

Read here for more information:
http://en.wikipedia.org/wiki/Subset_sum_problem

Algorithm to find all possible arrays of size L that sum up to N or less

You could modify trincot's solution with a small filter at the end:

function findArrays(maxSize, maxSum) {
  let arr = [];
  let result = []; // <--- will collect all the subarrays

  function recur(maxSum) {
    let k = arr.length;
    result.push([...arr]);
    if (k === maxSize) return;
    for (let i = 0; i <= maxSum; i++) {
      arr[k] = i;
      recur(maxSum - i);
    }
    arr.length = k;
  }

  recur(maxSum);
  return result.filter(({ length }) => length == maxSize);
}

// demo
for (let arr of findArrays(3, 2))
  console.log(JSON.stringify(arr));

---

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