Search code examples
algorithmsearchlanguage-agnosticcombinationssubset-sum

Finding all possible combinations of numbers to reach a given sum


How would you go about testing all possible combinations of additions from a given set N of numbers so they add up to a given final number?

A brief example:

  • Set of numbers to add: N = {1,5,22,15,0,...}
  • Desired result: 12345

Solution

  • 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.