我周围有一些Scrabble代码，因此我可以将它们组合在一起。我使用的字典是sowpods（267751个单词）。下面的代码将字典作为文本文件读取，每行带有一个大写单词。

代码是C＃：

using System;
using System.Collections.Generic;
using System.Linq;
using System.Text;
using System.IO;
using System.Diagnostics;

namespace SO_6022848
{
  public struct Letter
  {
    public const string Chars = "ABCDEFGHIJKLMNOPQRSTUVWXYZ";
    public static implicit operator Letter(char c)
    {
      return new Letter() { Index = Chars.IndexOf(c) };
    }
    public int Index;
    public char ToChar()
    {
      return Chars[Index];
    }
    public override string ToString()
    {
      return Chars[Index].ToString();
    }
  }

  public class Trie
  {
    public class Node
    {
      public string Word;
      public bool IsTerminal { get { return Word != null; } }
      public Dictionary<Letter, Node> Edges = new Dictionary<Letter, Node>();
    }

    public Node Root = new Node();

    public Trie(string[] words)
    {
      for (int w = 0; w < words.Length; w++)
      {
        var word = words[w];
        var node = Root;
        for (int len = 1; len <= word.Length; len++)
        {
          var letter = word[len - 1];
          Node next;
          if (!node.Edges.TryGetValue(letter, out next))
          {
            next = new Node();
            if (len == word.Length)
            {
              next.Word = word;
            }
            node.Edges.Add(letter, next);
          }
          node = next;
        }
      }
    }

  }

  class Program
  {
    static void GenWords(Trie.Node n, HashSet<Letter>[] sets, int currentArrayIndex, List<string> wordsFound)
    {
      if (currentArrayIndex < sets.Length)
      {
        foreach (var edge in n.Edges)
        {
          if (sets[currentArrayIndex].Contains(edge.Key))
          {
            if (edge.Value.IsTerminal)
            {
              wordsFound.Add(edge.Value.Word);
            }
            GenWords(edge.Value, sets, currentArrayIndex + 1, wordsFound);
          }
        }
      }
    }

    static void Main(string[] args)
    {
      const int minArraySize = 3;
      const int maxArraySize = 4;
      const int setCount = 10;
      const bool generateRandomInput = true;

      var trie = new Trie(File.ReadAllLines("sowpods.txt"));
      var watch = new Stopwatch();
      var trials = 10000;
      var wordCountSum = 0;
      var rand = new Random(37);

      for (int t = 0; t < trials; t++)
      {
        HashSet<Letter>[] sets;
        if (generateRandomInput)
        {
          sets = new HashSet<Letter>[setCount];
          for (int i = 0; i < setCount; i++)
          {
            sets[i] = new HashSet<Letter>();
            var size = minArraySize + rand.Next(maxArraySize - minArraySize + 1);
            while (sets[i].Count < size)
            {
              sets[i].Add(Letter.Chars[rand.Next(Letter.Chars.Length)]);
            }
          }
        }
        else
        {
          sets = new HashSet<Letter>[] { 
          new HashSet<Letter>(new Letter[] { 'P', 'Q', 'R', 'S' }), 
          new HashSet<Letter>(new Letter[] { 'A', 'B', 'C' }), 
          new HashSet<Letter>(new Letter[] { 'T', 'U', 'V' }), 
          new HashSet<Letter>(new Letter[] { 'M', 'N', 'O' }) };
        }

        watch.Start();
        var wordsFound = new List<string>();
        for (int i = 0; i < sets.Length - 1; i++)
        {
          GenWords(trie.Root, sets, i, wordsFound);
        }
        watch.Stop();
        wordCountSum += wordsFound.Count;
        if (!generateRandomInput && t == 0)
        {
          foreach (var word in wordsFound)
          {
            Console.WriteLine(word);
          }
        }
      }
      Console.WriteLine("Elapsed per trial = {0}", new TimeSpan(watch.Elapsed.Ticks / trials));
      Console.WriteLine("Average word count per trial = {0:0.0}", (float)wordCountSum / trials);
    }

  }

}

这是使用测试数据时的输出：

PA
PAT
PAV
QAT
RAT
RATO
RAUN
SAT
SAU
SAV
SCUM
AT
AVO
BUM
BUN
CUM
TO
UM
UN
Elapsed per trial = 00:00:00.0000725
Average word count per trial = 19.0

使用随机数据时的输出（不打印每个单词）：

Elapsed per trial = 00:00:00.0002910
Average word count per trial = 62.2

编辑：
我通过以下两项更改使其速度更快：将单词存储在trie的每个终端节点上，这样就不必重建它了。并将输入的字母存储为散列集数组而不是数组数组，这样Contains（）调用很快。