PowerShell解决方案:

function Get-NChooseK
{
    <#
    .SYNOPSIS
    Returns all the possible combinations by choosing K items at a time from N possible items.

    .DESCRIPTION
    Returns all the possible combinations by choosing K items at a time from N possible items.
    The combinations returned do not consider the order of items as important i.e. 123 is considered to be the same combination as 231, etc.

    .PARAMETER ArrayN
    The array of items to choose from.

    .PARAMETER ChooseK
    The number of items to choose.

    .PARAMETER AllK
    Includes combinations for all lesser values of K above zero i.e. 1 to K.

    .PARAMETER Prefix
    String that will prefix each line of the output.

    .EXAMPLE
    PS C:\> Get-NChooseK -ArrayN '1','2','3' -ChooseK 3
    123

    .EXAMPLE
    PS C:\> Get-NChooseK -ArrayN '1','2','3' -ChooseK 3 -AllK
    1
    2
    3
    12
    13
    23
    123

    .EXAMPLE
    PS C:\> Get-NChooseK -ArrayN '1','2','3' -ChooseK 2 -Prefix 'Combo: '
    Combo: 12
    Combo: 13
    Combo: 23

    .NOTES
    Author : nmbell
    #>

    # Use cmdlet binding
    [CmdletBinding()]

    # Declare parameters
    Param
    (

        [String[]]
        $ArrayN

    ,   [Int]
        $ChooseK

    ,   [Switch]
        $AllK

    ,   [String]
        $Prefix = ''

    )

    BEGIN
    {
    }

    PROCESS
    {
        # Validate the inputs
        $ArrayN = $ArrayN | Sort-Object -Unique

        If ($ChooseK -gt $ArrayN.Length)
        {
            Write-Error "Can't choose $ChooseK items when only $($ArrayN.Length) are available." -ErrorAction Stop
        }

        # Control the output
        $firstK = If ($AllK) { 1 } Else { $ChooseK }

        # Get combinations
        $firstK..$ChooseK | ForEach-Object {

            $thisK = $_

            $ArrayN[0..($ArrayN.Length-($thisK--))] | ForEach-Object {
                If ($thisK -eq 0)
                {
                    Write-Output ($Prefix+$_)
                }
                Else
                {
                    Get-NChooseK -Array ($ArrayN[($ArrayN.IndexOf($_)+1)..($ArrayN.Length-1)]) -Choose $thisK -AllK:$false -Prefix ($Prefix+$_)
                }
            }

        }
    }

    END
    {
    }

}

例如:

PS C:\>Get-NChooseK -ArrayN 'A','B','C','D','E' -ChooseK 3
ABC
ABD
ABE
ACD
ACE
ADE
BCD
BCE
BDE
CDE

最近在IronScripter网站上发布了一个类似于这个问题的挑战，在那里你可以找到我的链接和其他一些解决方案。

简短快速的c#实现

public static IEnumerable<IEnumerable<T>> Combinations<T>(IEnumerable<T> elements, int k)
{
    return Combinations(elements.Count(), k).Select(p => p.Select(q => elements.ElementAt(q)));                
}      

public static List<int[]> Combinations(int setLenght, int subSetLenght) //5, 3
{
    var result = new List<int[]>();

    var lastIndex = subSetLenght - 1;
    var dif = setLenght - subSetLenght;
    var prevSubSet = new int[subSetLenght];
    var lastSubSet = new int[subSetLenght];
    for (int i = 0; i < subSetLenght; i++)
    {
        prevSubSet[i] = i;
        lastSubSet[i] = i + dif;
    }

    while(true)
    {
        //add subSet ad result set
        var n = new int[subSetLenght];
        for (int i = 0; i < subSetLenght; i++)
            n[i] = prevSubSet[i];

        result.Add(n);

        if (prevSubSet[0] >= lastSubSet[0])
            break;

        //start at index 1 because index 0 is checked and breaking in the current loop
        int j = 1;
        for (; j < subSetLenght; j++)
        {
            if (prevSubSet[j] >= lastSubSet[j])
            {
                prevSubSet[j - 1]++;

                for (int p = j; p < subSetLenght; p++)
                    prevSubSet[p] = prevSubSet[p - 1] + 1;

                break;
            }
        }

        if (j > lastIndex)
            prevSubSet[lastIndex]++;
    }

    return result;
}

《计算机程序设计艺术》第4卷第3册有大量这样的内容，它们可能比我描述的更适合你的特定情况。

格雷码

你会遇到的一个问题当然是内存，很快，你会在你的集合中遇到20个元素的问题——20C3 = 1140。如果你想遍历这个集合，最好使用修改过的灰码算法，这样你就不会把它们都保存在内存中。这将从前一个组合中生成下一个组合并避免重复。有很多不同的用途。我们想要最大化连续组合之间的差异吗?最小化?等等。

一些描述灰色代码的原始论文:

Hamilton路径与最小变化算法 相邻交换组合生成算法

以下是涉及该主题的其他一些论文:

Eades、Hickey、Read相邻交换组合生成算法的高效实现(PDF, Pascal代码) 结合发电机 组合灰色编码综述(PostScript) 灰色编码的一种算法

Chase's Twiddle(算法)

菲利普·J·蔡斯，《算法382:N个对象中M个对象的组合》(1970)

该算法在C…

按字典顺序排列的组合索引(Buckles算法515)

还可以通过索引(按字典顺序)引用组合。意识到索引应该是基于索引从右到左的一些变化，我们可以构造一些应该恢复组合的东西。

So, we have a set {1,2,3,4,5,6}... and we want three elements. Let's say {1,2,3} we can say that the difference between the elements is one and in order and minimal. {1,2,4} has one change and is lexicographically number 2. So the number of 'changes' in the last place accounts for one change in the lexicographical ordering. The second place, with one change {1,3,4} has one change but accounts for more change since it's in the second place (proportional to the number of elements in the original set).

我所描述的方法是一种解构，从集合到索引，我们需要做相反的事情——这要复杂得多。这就是巴克尔斯解决问题的方法。我写了一些C来计算它们，做了一些小改动——我使用集合的索引而不是一个数字范围来表示集合，所以我们总是从0…n开始工作。 注意:

由于组合是无序的，{1,3,2}={1,2,3}——我们将它们按字典顺序排列。 该方法有一个隐式的0来开始第一个差值集。

词典顺序组合索引(麦卡弗里)

还有另一种方法:，它的概念更容易掌握和编程，但它没有Buckles的优化。幸运的是，它也不会产生重复的组合:

最大化的集合，其中。

例如:27 = C (6, 4) + C (5,3) + C (2, 2) + C(1, 1)。那么，第27个单词的字典组合是{1,2,5,6}，它们是你想要查找的任何集合的索引。下面的例子(OCaml)，需要选择函数，留给读者:

(* this will find the [x] combination of a [set] list when taking [k] elements *)
let combination_maccaffery set k x =
    (* maximize function -- maximize a that is aCb              *)
    (* return largest c where c < i and choose(c,i) <= z        *)
    let rec maximize a b x =
        if (choose a b ) <= x then a else maximize (a-1) b x
    in
    let rec iterate n x i = match i with
        | 0 -> []
        | i ->
            let max = maximize n i x in
            max :: iterate n (x - (choose max i)) (i-1)
    in
    if x < 0 then failwith "errors" else
    let idxs =  iterate (List.length set) x k in
    List.map (List.nth set) (List.sort (-) idxs)

一个小而简单的组合迭代器

为了教学目的，提供了以下两个算法。它们实现了一个迭代器和(更通用的)文件夹整体组合。 它们尽可能快，复杂度为O(nCk)。内存消耗受k约束。

我们将从迭代器开始，它将为每个组合调用用户提供的函数

let iter_combs n k f =
  let rec iter v s j =
    if j = k then f v
    else for i = s to n - 1 do iter (i::v) (i+1) (j+1) done in
  iter [] 0 0

更通用的版本将从初始状态开始调用用户提供的函数和状态变量。因为我们需要在不同的状态之间传递状态，所以我们不使用for循环，而是使用递归，

let fold_combs n k f x =
  let rec loop i s c x =
    if i < n then
      loop (i+1) s c @@
      let c = i::c and s = s + 1 and i = i + 1 in
      if s < k then loop i s c x else f c x
    else x in
  loop 0 0 [] x

像Andrea Ambu一样用Python写的，但不是硬编码来选择三个。

def combinations(list, k):
    """Choose combinations of list, choosing k elements(no repeats)"""
    if len(list) < k:
        return []
    else:
        seq = [i for i in range(k)]
        while seq:
            print [list[index] for index in seq]
            seq = get_next_combination(len(list), k, seq)

def get_next_combination(num_elements, k, seq):
        index_to_move = find_index_to_move(num_elements, seq)
        if index_to_move == None:
            return None
        else:
            seq[index_to_move] += 1

            #for every element past this sequence, move it down
            for i, elem in enumerate(seq[(index_to_move+1):]):
                seq[i + 1 + index_to_move] = seq[index_to_move] + i + 1

            return seq

def find_index_to_move(num_elements, seq):
        """Tells which index should be moved"""
        for rev_index, elem in enumerate(reversed(seq)):
            if elem < (num_elements - rev_index - 1):
                return len(seq) - rev_index - 1
        return None   

短快C实现

#include <stdio.h>

void main(int argc, char *argv[]) {
  const int n = 6; /* The size of the set; for {1, 2, 3, 4} it's 4 */
  const int p = 4; /* The size of the subsets; for {1, 2}, {1, 3}, ... it's 2 */
  int comb[40] = {0}; /* comb[i] is the index of the i-th element in the combination */

  int i = 0;
  for (int j = 0; j <= n; j++) comb[j] = 0;
  while (i >= 0) {
    if (comb[i] < n + i - p + 1) {
       comb[i]++;
       if (i == p - 1) { for (int j = 0; j < p; j++) printf("%d ", comb[j]); printf("\n"); }
       else            { comb[++i] = comb[i - 1]; }
    } else i--; }
}

要查看它有多快，请使用这段代码并测试它

#include <time.h>
#include <stdio.h>

void main(int argc, char *argv[]) {
  const int n = 32; /* The size of the set; for {1, 2, 3, 4} it's 4 */
  const int p = 16; /* The size of the subsets; for {1, 2}, {1, 3}, ... it's 2 */
  int comb[40] = {0}; /* comb[i] is the index of the i-th element in the combination */

  int c = 0; int i = 0;
  for (int j = 0; j <= n; j++) comb[j] = 0;
  while (i >= 0) {
    if (comb[i] < n + i - p + 1) {
       comb[i]++;
       /* if (i == p - 1) { for (int j = 0; j < p; j++) printf("%d ", comb[j]); printf("\n"); } */
       if (i == p - 1) c++;
       else            { comb[++i] = comb[i - 1]; }
    } else i--; }
  printf("%d!%d == %d combination(s) in %15.3f second(s)\n ", p, n, c, clock()/1000.0);
}

使用cmd.exe (windows)测试:

Microsoft Windows XP [Version 5.1.2600]
(C) Copyright 1985-2001 Microsoft Corp.

c:\Program Files\lcc\projects>combination
16!32 == 601080390 combination(s) in          5.781 second(s)

c:\Program Files\lcc\projects>

祝你有愉快的一天。

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' ]
]