下面是一个简单易懂的递归c++解决方案:

#include<vector>
using namespace std;

template<typename T>
void ksubsets(const vector<T>& arr, unsigned left, unsigned idx,
    vector<T>& lst, vector<vector<T>>& res)
{
    if (left < 1) {
        res.push_back(lst);
        return;
    }
    for (unsigned i = idx; i < arr.size(); i++) {
        lst.push_back(arr[i]);
        ksubsets(arr, left - 1, i + 1, lst, res);
        lst.pop_back();
    }
}

int main()
{
    vector<int> arr = { 1, 2, 3, 4, 5 };
    unsigned left = 3;
    vector<int> lst;
    vector<vector<int>> res;
    ksubsets<int>(arr, left, 0, lst, res);
    // now res has all the combinations
}

遵循Haskell代码同时计算组合数和组合，由于Haskell的惰性，您可以得到其中的一部分而无需计算另一部分。

import Data.Semigroup
import Data.Monoid

data Comb = MkComb {count :: Int, combinations :: [[Int]]} deriving (Show, Eq, Ord)

instance Semigroup Comb where
    (MkComb c1 cs1) <> (MkComb c2 cs2) = MkComb (c1 + c2) (cs1 ++ cs2)

instance Monoid Comb where
    mempty = MkComb 0 []

addElem :: Comb -> Int -> Comb
addElem (MkComb c cs) x = MkComb c (map (x :) cs)

comb :: Int -> Int -> Comb
comb n k | n < 0 || k < 0 = error "error in `comb n k`, n and k should be natural number"
comb n k | k == 0 || k == n = MkComb 1 [(take k [k-1,k-2..0])]
comb n k | n < k = mempty
comb n k = comb (n-1) k <> (comb (n-1) (k-1) `addElem` (n-1))

它是这样工作的:

*Main> comb 0 1
MkComb {count = 0, combinations = []}

*Main> comb 0 0
MkComb {count = 1, combinations = [[]]}

*Main> comb 1 1
MkComb {count = 1, combinations = [[0]]}

*Main> comb 4 2
MkComb {count = 6, combinations = [[1,0],[2,0],[2,1],[3,0],[3,1],[3,2]]}

*Main> count (comb 10 5)
252

在c#中:

public static IEnumerable<IEnumerable<T>> Combinations<T>(this IEnumerable<T> elements, int k)
{
  return k == 0 ? new[] { new T[0] } :
    elements.SelectMany((e, i) =>
      elements.Skip(i + 1).Combinations(k - 1).Select(c => (new[] {e}).Concat(c)));
}

用法:

var result = Combinations(new[] { 1, 2, 3, 4, 5 }, 3);

结果:

123
124
125
134
135
145
234
235
245
345

Haskell中的简单递归算法

import Data.List

combinations 0 lst = [[]]
combinations n lst = do
    (x:xs) <- tails lst
    rest   <- combinations (n-1) xs
    return $ x : rest

我们首先定义特殊情况，即选择零元素。它产生一个单一的结果，这是一个空列表(即一个包含空列表的列表)。

对于n> 0, x遍历列表中的每一个元素xs是x之后的每一个元素。

Rest通过对组合的递归调用从xs中选取n - 1个元素。该函数的最终结果是一个列表，其中每个元素都是x: rest(即对于x和rest的每个不同值，x为头部，rest为尾部的列表)。

> combinations 3 "abcde"
["abc","abd","abe","acd","ace","ade","bcd","bce","bde","cde"]

当然，由于Haskell是懒惰的，列表是根据需要逐渐生成的，因此您可以部分计算指数级的大组合。

> let c = combinations 8 "abcdefghijklmnopqrstuvwxyz"
> take 10 c
["abcdefgh","abcdefgi","abcdefgj","abcdefgk","abcdefgl","abcdefgm","abcdefgn",
 "abcdefgo","abcdefgp","abcdefgq"]

下面是一个coffeescript实现

combinations: (list, n) ->
        permuations = Math.pow(2, list.length) - 1
        out = []
        combinations = []

        while permuations
            out = []

            for i in [0..list.length]
                y = ( 1 << i )
                if( y & permuations and (y isnt permuations))
                    out.push(list[i])

            if out.length <= n and out.length > 0
                combinations.push(out)

            permuations--

        return combinations 

在Python中，利用递归的优势和所有事情都是通过引用完成的事实。对于非常大的集合，这将占用大量内存，但其优点是初始集合可以是一个复杂的对象。它只会找到唯一的组合。

import copy

def find_combinations( length, set, combinations = None, candidate = None ):
    # recursive function to calculate all unique combinations of unique values
    # from [set], given combinations of [length].  The result is populated
    # into the 'combinations' list.
    #
    if combinations == None:
        combinations = []
    if candidate == None:
        candidate = []

    for item in set:
        if item in candidate:
            # this item already appears in the current combination somewhere.
            # skip it
            continue

        attempt = copy.deepcopy(candidate)
        attempt.append(item)
        # sorting the subset is what gives us completely unique combinations,
        # so that [1, 2, 3] and [1, 3, 2] will be treated as equals
        attempt.sort()

        if len(attempt) < length:
            # the current attempt at finding a new combination is still too
            # short, so add another item to the end of the set
            # yay recursion!
            find_combinations( length, set, combinations, attempt )
        else:
            # the current combination attempt is the right length.  If it
            # already appears in the list of found combinations then we'll
            # skip it.
            if attempt in combinations:
                continue
            else:
                # otherwise, we append it to the list of found combinations
                # and move on.
                combinations.append(attempt)
                continue
    return len(combinations)

你可以这样使用它。传递'result'是可选的，所以你可以用它来获取可能组合的数量…尽管这样做效率很低(最好通过计算来完成)。

size = 3
set = [1, 2, 3, 4, 5]
result = []

num = find_combinations( size, set, result ) 
print "size %d results in %d sets" % (size, num)
print "result: %s" % (result,)

您应该从测试数据中得到以下输出:

size 3 results in 10 sets
result: [[1, 2, 3], [1, 2, 4], [1, 2, 5], [1, 3, 4], [1, 3, 5], [1, 4, 5], [2, 3, 4], [2, 3, 5], [2, 4, 5], [3, 4, 5]]

如果你的集合是这样的，它也会工作得很好:

set = [
    [ 'vanilla', 'cupcake' ],
    [ 'chocolate', 'pudding' ],
    [ 'vanilla', 'pudding' ],
    [ 'chocolate', 'cookie' ],
    [ 'mint', 'cookie' ]
]