func sum(array : [Int]) -> Int{
    var sum = 0
    array.forEach { (item) in
        sum = item + sum
    }
    return sum
}
func susetNumbers(array :[Int], target : Int, subsetArray: [Int],result : inout [[Int]]) -> [[Int]]{
    let s = sum(array: subsetArray)
    if(s == target){
        print("sum\(subsetArray) = \(target)")
        result.append(subsetArray)
    }
    for i in 0..<array.count{
        let n = array[i]
        let remaning = Array(array[(i+1)..<array.count])
        susetNumbers(array: remaning, target: target, subsetArray: subsetArray + [n], result: &result)
        
    }
    return result
}

 var resultArray = [[Int]]()
    let newA = susetNumbers(array: [1,2,3,4,5], target: 5, subsetArray: [],result:&resultArray)
    print(resultArray)

我将c#示例移植到Objective-c，并没有在响应中看到它:

//Usage
NSMutableArray* numberList = [[NSMutableArray alloc] init];
NSMutableArray* partial = [[NSMutableArray alloc] init];
int target = 16;
for( int i = 1; i<target; i++ )
{ [numberList addObject:@(i)]; }
[self findSums:numberList target:target part:partial];


//*******************************************************************
// Finds combinations of numbers that add up to target recursively
//*******************************************************************
-(void)findSums:(NSMutableArray*)numbers target:(int)target part:(NSMutableArray*)partial
{
    int s = 0;
    for (NSNumber* x in partial)
    { s += [x intValue]; }

    if (s == target)
    { NSLog(@"Sum[%@]", partial); }

    if (s >= target)
    { return; }

    for (int i = 0;i < [numbers count];i++ )
    {
        int n = [numbers[i] intValue];
        NSMutableArray* remaining = [[NSMutableArray alloc] init];
        for (int j = i + 1; j < [numbers count];j++)
        { [remaining addObject:@([numbers[j] intValue])]; }

        NSMutableArray* partRec = [[NSMutableArray alloc] initWithArray:partial];
        [partRec addObject:@(n)];
        [self findSums:remaining target:target part:partRec];
    }
}

在Haskell:

filter ((==) 12345 . sum) $ subsequences [1,5,22,15,0,..]

J:

(]#~12345=+/@>)(]<@#~[:#:@i.2^#)1 5 22 15 0 ...

正如您可能注意到的，两者都采用相同的方法，并将问题分为两部分:生成幂集的每个成员，并检查每个成员与目标的和。

还有其他的解决方案，但这是最直接的。

在这两种方法中，你是否需要帮助，或者找到另一种方法?

Java解决方案的Swift 3转换(by @JeremyThompson)

protocol _IntType { }
extension Int: _IntType {}


extension Array where Element: _IntType {

    func subsets(to: Int) -> [[Element]]? {

        func sum_up_recursive(_ numbers: [Element], _ target: Int, _ partial: [Element], _ solution: inout [[Element]]) {

            var sum: Int = 0
            for x in partial {
                sum += x as! Int
            }

            if sum == target {
                solution.append(partial)
            }

            guard sum < target else {
                return
            }

            for i in stride(from: 0, to: numbers.count, by: 1) {

                var remaining = [Element]()

                for j in stride(from: i + 1, to: numbers.count, by: 1) {
                    remaining.append(numbers[j])
                }

                var partial_rec = [Element](partial)
                partial_rec.append(numbers[i])

                sum_up_recursive(remaining, target, partial_rec, &solution)
            }
        }

        var solutions = [[Element]]()
        sum_up_recursive(self, to, [Element](), &solutions)

        return solutions.count > 0 ? solutions : nil
    }

}

用法:

let numbers = [3, 9, 8, 4, 5, 7, 10]

if let solution = numbers.subsets(to: 15) {
    print(solution) // output: [[3, 8, 4], [3, 5, 7], [8, 7], [5, 10]]
} else {
    print("not possible")
}

下面是一个Java版本，它非常适合小N和非常大的目标和，当复杂度O(t*N)(动态解)大于指数算法时。我的版本在中间攻击中使用了一个meet，并进行了一些调整，以降低复杂度，从经典的naive O(n*2^n)降低到O(2^(n/2))。

如果你想在32到64个元素之间的集合中使用这种方法，你应该将表示step函数中当前子集的int改为long，尽管随着集合大小的增加，性能显然会急剧下降。如果你想对一个有奇数个元素的集合使用这个，你应该给这个集合加上一个0，使它成为偶数。

import java.util.ArrayList;
import java.util.List;

public class SubsetSumMiddleAttack {
    static final int target = 100000000;
    static final int[] set = new int[]{ ... };

    static List<Subset> evens = new ArrayList<>();
    static List<Subset> odds = new ArrayList<>();

    static int[][] split(int[] superSet) {
        int[][] ret = new int[2][superSet.length / 2]; 

        for (int i = 0; i < superSet.length; i++) ret[i % 2][i / 2] = superSet[i];

        return ret;
    }

    static void step(int[] superSet, List<Subset> accumulator, int subset, int sum, int counter) {
        accumulator.add(new Subset(subset, sum));
        if (counter != superSet.length) {
            step(superSet, accumulator, subset + (1 << counter), sum + superSet[counter], counter + 1);
            step(superSet, accumulator, subset, sum, counter + 1);
        }
    }

    static void printSubset(Subset e, Subset o) {
        String ret = "";
        for (int i = 0; i < 32; i++) {
            if (i % 2 == 0) {
                if ((1 & (e.subset >> (i / 2))) == 1) ret += " + " + set[i];
            }
            else {
                if ((1 & (o.subset >> (i / 2))) == 1) ret += " + " + set[i];
            }
        }
        if (ret.startsWith(" ")) ret = ret.substring(3) + " = " + (e.sum + o.sum);
        System.out.println(ret);
    }

    public static void main(String[] args) {
        int[][] superSets = split(set);

        step(superSets[0], evens, 0,0,0);
        step(superSets[1], odds, 0,0,0);

        for (Subset e : evens) {
            for (Subset o : odds) {
                if (e.sum + o.sum == target) printSubset(e, o);
            }
        }
    }
}

class Subset {
    int subset;
    int sum;

    Subset(int subset, int sum) {
        this.subset = subset;
        this.sum = sum;
    }
}

这个问题可以通过所有可能的和的递归组合来解决，过滤掉那些达到目标的和。下面是Python中的算法:

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

这种类型的算法在接下来的斯坦福大学抽象编程课程中有很好的解释-这个视频非常推荐来理解递归是如何产生解决方案的排列的。

Edit

上面作为一个生成器函数，使它更有用一点。需要Python 3.3+，因为yield来自。

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)

下面是相同算法的Java版本:

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);
    }
}

这是完全相同的启发式。我的Java有点生疏，但我认为很容易理解。

Java解决方案的c#转换(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解决方案:(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)

编辑:复杂性讨论

正如其他人提到的，这是一个np难题。它可以在O(2^n)的指数时间内求解，例如n=10，将有1024个可能的解。如果你要达到的目标是在一个较低的范围内，那么这个算法是有效的。例如:

Subset_sum([1,2,3,4,5,6,7,8,9,10]，100000)生成1024个分支，因为目标永远无法过滤出可能的解。

另一方面，subset_sum([1,2,3,4,5,6,7,8,9,10]，10)只生成175个分支，因为达到10的目标要过滤掉许多组合。

如果N和目标都是很大的数字，那么就应该得到近似的解。