最小生成树
- WBOY原创
- 2024-09-06 06:08:021156浏览
图的最小生成树是总权重最小的生成树。
一个图可能有许多生成树。假设边缘被加权。最小生成树的总权重最小。例如，下图 b、c、d 中的树是图 a 中图的生成树。图c和d中的树是最小生成树。
寻找最小生成树的问题有很多应用。考虑一家在许多城市设有分支机构的公司。该公司希望租用电话线将所有分支机构连接在一起。电话公司对连接不同的城市收取不同的费用。有很多方法可以将所有分支连接在一起。最便宜的方法是找到总费率最小的生成树。
最小生成树算法
如何找到最小生成树？有几种众所周知的算法可以做到这一点。本节介绍Prim算法。 Prim 的算法从包含任意顶点的生成树 T 开始。该算法通过重复添加具有与树中已有顶点相关的最低成本边的顶点来扩展树。 Prim的算法是一种贪心算法，其代码如下所示。
Input: A connected undirected weighted G = (V, E) with non-negative weights
Output: MST (a minimum spanning tree)
1 MST minimumSpanningTree() {
2 Let T be a set for the vertices in the spanning tree;
3 Initially, add the starting vertex to T;
4
5 while (size of T < n) {
6 Find u in T and v in V – T with the smallest weight
7 on the edge (u, v), as shown in Figure 29.6;
8 Add v to T and set parent[v] = u;
9 }
10 }
算法首先将起始顶点添加到T中。然后，它不断地将一个顶点（例如 v）从 V – T 添加到 T 中。 v 是与 T 中的顶点相邻且边权重最小的顶点。例如，T 和 V – T 中的顶点有 5 条边连接，如下图所示，并且 (u, v ) 是权重最小的一个。
考虑下图中的图表。该算法按以下顺序将顶点添加到 T：
- 将顶点 0 添加到 T。
- 将顶点 5 添加到 T，因为 Edge(5, 0, 5) 在与 <🎜 中的顶点关联的所有边中具有最小的权重>T，如图a所示。从 0 到 5 的箭头线表示 0 是 5 的父级。 将顶点
- 1 添加到 T，因为 Edge(1, 0, 6) 在与 <🎜 中的顶点关联的所有边中具有最小的权重>T，如图b. 将顶点 6
- 添加到 T，因为 Edge(6, 1, 7) 在与 <🎜 中的顶点关联的所有边中具有最小的权重>T，如图c. 将顶点2 添加到
- T，因为边（2,6,5）在与<🎜中的顶点相关的所有边中具有最小的权重>T，如图d. 将顶点4添加到 T
- ，因为边（4,6,7）在与<🎜中的顶点关联的所有边中具有最小的权重>T，如图e. 将顶点3添加到T ，因为
- 边（3,2,8）在与<🎜中的顶点相关的所有边中具有最小的权重>T，如图f. 最小生成树不是唯一的。例如，下图中的 (c) 和 (d) 都是图 a 中的图的最小生成树。但是，如果权重不同，则该图具有唯一的最小生成树。
Refining Prim’s MST Algorithm
To make it easy to identify the next vertex to add into the tree, we use cost[v] to store the cost of adding a vertex v to the spanning tree T. Initially cost[s] is 0 for a starting vertex and assign infinity to cost[v] for all other vertices. The algorithm repeatedly finds a vertex u in V – T with the smallest cost[u] and moves u to T. The refined version of the alogrithm is given in code below.
Input: A connected undirected weighted G = (V, E) with non-negative weights
Output: a minimum spanning tree with the starting vertex s as the root
1 MST getMinimumSpanngingTree(s) {
2 Let T be a set that contains the vertices in the spanning tree;
3 Initially T is empty;
4 Set cost[s] = 0; and cost[v] = infinity for all other vertices in V;
5
6 while (size of T < n) {
7 Find u not in T with the smallest cost[u];
8 Add u to T;
9 for (each v not in T and (u, v) in E)
10 if (cost[v] > w(u, v)) { // Adjust cost[v]
11 cost[v] = w(u, v); parent[v] = u;
12 }
13 }
14 }
Implementation of the MST Algorithm
The getMinimumSpanningTree(int v) method is defined in the WeightedGraph class. It returns an instance of the MST class, as shown in Figure below.
The MST class is defined as an inner class in the WeightedGraph class, which extends the Tree class, as shown in Figure below.
The Tree class was shown in Figure below. The MST class was implemented in lines 141–153 in WeightedGraph.java.
The refined version of the Prim’s algoruthm greatly simplifies the implementation. The getMinimumSpanningTree method was implemented using the refined version of the Prim’s algorithm in lines 99–138 in Listing 29.2. The getMinimumSpanningTree(int startingVertex) method sets cost[startingVertex] to 0 (line 105) and cost[v] to infinity for all other vertices (lines 102–104). The parent of startingVertex is set to -1 (line 108). T is a list that stores the vertices added into the spanning tree (line 111). We use a list for T rather than a set in order to record the order of the vertices added to T.
Initially, T is empty. To expand T, the method performs the following operations:
- Find the vertex u with the smallest cost[u] (lines 118–123 and add it into T (line 125).
- After adding u in T, update cost[v] and parent[v] for each v adjacent to u in V-T if cost[v] > w(u, v) (lines 129–134).
After a new vertex is added to T, totalWeight is updated (line 126). Once all vertices are added to T, an instance of MST is created (line 137). Note that the method will not work if the graph is not connected. However, you can modify it to obtain a partial MST.
The MST class extends the Tree class (line 141). To create an instance of MST, pass root, parent, T, and totalWeight (lines 144-145). The data fields root, parent, and searchOrder are defined in the Tree class, which is an inner class defined in AbstractGraph.
Note that testing whether a vertex i is in T by invoking T.contains(i) takes O(n) time, since T is a list. Therefore, the overall time complexity for this implemention is O(n3).
The code below gives a test program that displays minimum spanning trees for the graph in Figure below and the graph in Figure below a, respectively.
package demo;
public class TestMinimumSpanningTree {
public static void main(String[] args) {
String[] vertices = {"Seattle", "San Francisco", "Los Angeles", "Denver", "Kansas City", "Chicago", "Boston", "New York", "Atlanta", "Miami", "Dallas", "Houston"};
int[][] edges = {
{0, 1, 807}, {0, 3, 1331}, {0, 5, 2097},
{1, 0, 807}, {1, 2, 381}, {1, 3, 1267},
{2, 1, 381}, {2, 3, 1015}, {2, 4, 1663}, {2, 10, 1435},
{3, 0, 1331}, {3, 1, 1267}, {3, 2, 1015}, {3, 4, 599}, {3, 5, 1003},
{4, 2, 1663}, {4, 3, 599}, {4, 5, 533}, {4, 7, 1260}, {4, 8, 864}, {4, 10, 496},
{5, 0, 2097}, {5, 3, 1003}, {5, 4, 533}, {5, 6, 983}, {5, 7, 787},
{6, 5, 983}, {6, 7, 214},
{7, 4, 1260}, {7, 5, 787}, {7, 6, 214}, {7, 8, 888},
{8, 4, 864}, {8, 7, 888}, {8, 9, 661}, {8, 10, 781}, {8, 11, 810},
{9, 8, 661}, {9, 11, 1187},
{10, 2, 1435}, {10, 4, 496}, {10, 8, 781}, {10, 11, 239},
{11, 8, 810}, {11, 9, 1187}, {11, 10, 239}
};
WeightedGraph<String> graph1 = new WeightedGraph<>(vertices, edges);
WeightedGraph<String>.MST tree1 = graph1.getMinimumSpanningTree();
System.out.println("Total weight is " + tree1.getTotalWeight());
tree1.printTree();
edges = new int[][]{
{0, 1, 2}, {0, 3, 8},
{1, 0, 2}, {1, 2, 7}, {1, 3, 3},
{2, 1, 7}, {2, 3, 4}, {2, 4, 5},
{3, 0, 8}, {3, 1, 3}, {3, 2, 4}, {3, 4, 6},
{4, 2, 5}, {4, 3, 6}
};
WeightedGraph<Integer> graph2 = new WeightedGraph<>(edges, 5);
WeightedGraph<Integer>.MST tree2 = graph2.getMinimumSpanningTree(1);
System.out.println("\nTotal weight is " + tree2.getTotalWeight());
tree2.printTree();
}
}
Total weight is 6513.0
Root is: Seattle
Edges: (Seattle, San Francisco) (San Francisco, Los Angeles)
(Los Angeles, Denver) (Denver, Kansas City) (Kansas City, Chicago)
(New York, Boston) (Chicago, New York) (Dallas, Atlanta)
(Atlanta, Miami) (Kansas City, Dallas) (Dallas, Houston)
Total weight is 14.0
Root is: 1
Edges: (1, 0) (3, 2) (1, 3) (2, 4)
程序为上图第 27 行创建一个加权图。然后调用 getMinimumSpanningTree()（第 28 行）返回一个 MST，它表示图形。在 MST 对象上调用 printTree()（第 30 行）会显示树中的边缘。请注意，MST 是 Tree 的子类。 printTree() 方法定义在 Tree 类中。
最小生成树的图示如下图所示。顶点按以下顺序添加到树中：西雅图、旧金山、洛杉矶、丹佛、堪萨斯城、达拉斯、休斯顿、芝加哥、纽约、波士顿、亚特兰大和迈阿密。
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