Prim 最小生成树算法

Prim 算法是一种解决最小生成树问题（Minimum Spanning Tree）的算法。和 Kruskal 算法类似，Prim 算法的设计也是基于贪心算法（Greedy algorithm）。

Prim 算法的思想很简单，一棵生成树必须连接所有的顶点，而要保持最小权重则每次选择邻接的边时要选择较小权重的边。Prim 算法看起来非常类似于单源最短路径 Dijkstra 算法，从源点出发，寻找当前的最短路径，每次比较当前可达邻接顶点中最小的一个边加入到生成树中。

例如，下面这张连通的无向图 G，包含 9 个顶点和 14 条边，所以期待的最小生成树应包含 (9 - 1) = 8 条边。

创建 mstSet 包含到所有顶点的距离，初始为 INF，源点 0 的距离为 0，{0, INF, INF, INF, INF, INF, INF, INF, INF}。

选择当前最短距离的顶点，即还是顶点 0，将 0 加入 MST，此时邻接顶点为 1 和 7。

选择当前最小距离的顶点 1，将 1 加入 MST，此时邻接顶点为 2。

选择 2 和 7 中最小距离的顶点为 7，将 7 加入 MST，此时邻接顶点为 6 和 8。

选择 2, 6, 8 中最小距离的顶点为 6，将 6 加入 MST，此时邻接顶点为 5。

重复上面步骤直到遍历完所有顶点为止，会得到如下 MST。

C# 实现 Prim 算法如下。Prim 算法可以达到 O(ElogV) 的运行时间，如果采用斐波那契堆实现，运行时间可以减少到 O(E + VlogV)，如果 V 远小于 E 的话，将是对算法较大的改进。

using System;
using System.Collections.Generic;
using System.Linq;

namespace GraphAlgorithmTesting
{
  class Program
  {
    static void Main(string[] args)
    {
      Graph g = new Graph(9);
      g.AddEdge(0, 1, 4);
      g.AddEdge(0, 7, 8);
      g.AddEdge(1, 2, 8);
      g.AddEdge(1, 7, 11);
      g.AddEdge(2, 3, 7);
      g.AddEdge(2, 5, 4);
      g.AddEdge(3, 4, 9);
      g.AddEdge(3, 5, 14);
      g.AddEdge(5, 4, 10);
      g.AddEdge(6, 5, 2);
      g.AddEdge(7, 6, 1);
      g.AddEdge(7, 8, 7);
      g.AddEdge(8, 2, 2);
      g.AddEdge(8, 6, 6);

      // sorry, this is an undirect graph,
      // so, you know that this is not a good idea.
      List<Edge> edges = g.Edges
        .Select(e => new Edge(e.End, e.Begin, e.Weight))
        .ToList();
      foreach (var edge in edges)
      {
        g.AddEdge(edge.Begin, edge.End, edge.Weight);
      }

      Console.WriteLine();
      Console.WriteLine("Graph Vertex Count : {0}", g.VertexCount);
      Console.WriteLine("Graph Edge Count : {0}", g.EdgeCount);
      Console.WriteLine();

      List<Edge> mst = g.Prim();
      Console.WriteLine("MST Edges:");
      foreach (var edge in mst.OrderBy(e => e.Weight))
      {
        Console.WriteLine("	{0}", edge);
      }

      Console.ReadKey();
    }

    class Edge
    {
      public Edge(int begin, int end, int weight)
      {
        this.Begin = begin;
        this.End = end;
        this.Weight = weight;
      }

      public int Begin { get; private set; }
      public int End { get; private set; }
      public int Weight { get; private set; }

      public override string ToString()
      {
        return string.Format(
          "Begin[{0}], End[{1}], Weight[{2}]",
          Begin, End, Weight);
      }
    }

    class Graph
    {
      private Dictionary<int, List<Edge>> _adjacentEdges
        = new Dictionary<int, List<Edge>>();

      public Graph(int vertexCount)
      {
        this.VertexCount = vertexCount;
      }

      public int VertexCount { get; private set; }

      public IEnumerable<int> Vertices { get { return _adjacentEdges.Keys; } }

      public IEnumerable<Edge> Edges
      {
        get { return _adjacentEdges.Values.SelectMany(e => e); }
      }

      public int EdgeCount { get { return this.Edges.Count(); } }

      public void AddEdge(int begin, int end, int weight)
      {
        if (!_adjacentEdges.ContainsKey(begin))
        {
          var edges = new List<Edge>();
          _adjacentEdges.Add(begin, edges);
        }

        _adjacentEdges[begin].Add(new Edge(begin, end, weight));
      }

      public List<Edge> Prim()
      {
        // Array to store constructed MST
        int[] parent = new int[VertexCount];

        // Key values used to pick minimum weight edge in cut
        int[] keySet = new int[VertexCount];

        // To represent set of vertices not yet included in MST
        bool[] mstSet = new bool[VertexCount];

        // Initialize all keys as INFINITE
        for (int i = 0; i < VertexCount; i++)
        {
          keySet[i] = int.MaxValue;
          mstSet[i] = false;
        }

        // Always include first 1st vertex in MST.
        // Make key 0 so that this vertex is picked as first vertex
        keySet[0] = 0;
        parent[0] = -1; // First node is always root of MST 

        // The MST will have V vertices
        for (int i = 0; i < VertexCount - 1; i++)
        {
          // Pick thd minimum key vertex from the set of vertices
          // not yet included in MST
          int u = CalculateMinDistance(keySet, mstSet);

          // Add the picked vertex to the MST Set
          mstSet[u] = true;

          // Update key value and parent index of the adjacent vertices of
          // the picked vertex. Consider only those vertices which are not yet
          // included in MST
          for (int v = 0; v < VertexCount; v++)
          {
            // graph[u, v] is non zero only for adjacent vertices of m
            // mstSet[v] is false for vertices not yet included in MST
            // Update the key only if graph[u, v] is smaller than key[v]
            if (!mstSet[v]
              && _adjacentEdges.ContainsKey(u)
              && _adjacentEdges[u].Exists(e => e.End == v))
            {
              int d = _adjacentEdges[u].Single(e => e.End == v).Weight;
              if (d < keySet[v])
              {
                keySet[v] = d;
                parent[v] = u;
              }
            }
          }
        }

        // get all MST edges
        List<Edge> mst = new List<Edge>();
        for (int i = 1; i < VertexCount; i++)
          mst.Add(_adjacentEdges[parent[i]].Single(e => e.End == i));

        return mst;
      }

      private int CalculateMinDistance(int[] keySet, bool[] mstSet)
      {
        int minDistance = int.MaxValue;
        int minDistanceIndex = -1;

        for (int v = 0; v < VertexCount; v++)
        {
          if (!mstSet[v] && keySet[v] <= minDistance)
          {
            minDistance = keySet[v];
            minDistanceIndex = v;
          }
        }

        return minDistanceIndex;
      }
    }
  }
}

输出结果如下：

参考资料

• Connectivity in a directed graph
• Strongly Connected Components
• Tarjan's Algorithm to find Strongly Connected Components

本篇文章《Prim 最小生成树算法》由 Dennis Gao 发表自博客园，未经作者本人同意禁止任何形式的转载，任何自动或人为的爬虫转载行为均为耍流氓。