Interview Questions

Find minimum spanning tree using Prim's algorithm

A minimum spanning tree of a graph is a subgraph that connects all vertices in the graph with a minimum total weight for the edges. Each edge between the vertices has a weight corresponding to it and your goal is to connect every vertex while minimizing the total edge weight. Graphs can have more than one minimum spanning tree. Below is an example of a graph with 5 vertices and weighted edges that we will be running Prim's algorithm on.
## Example graph with edge weights

## Prim's algorithm

Prim's algorithm is a classic greedy algorithm for finding the MST of a graph. The general algorithm is:
## Example of Prim's algorithm

## Data structure to store edges

There are a few different options we have in what data structure we decide to use to represent the graph and how we decide to store all the edges. In this implementation we'll represent the graph as an adjacency matrix, and we have two common options in how we can store the edges:
## Code

## Running time

Because we are using implementation (1) to store the edges and we are representing the graph as an adjacency matrix, the running time is O(|V|^{2}) where V is the number of vertices the graph contains. This is because in the worst case, when we add a new vertex to the MST and we store its edges, the edge with the smallest weight might be at the end of the list requiring us to loop through the entire array of edges.
This makes the running time V * V = V^{2} where the first V represents every vertex in the graph that is being looped through in the while loop and the second V represents every other vertex that the first vertex may be connected to in the graph via an edge.
## Sources

https://www.glassdoor.com/Interview/Implement-a-graph-class-find-the-minimum-spanning-tree-QTN_143116.htm

(1) Create an empty tree, *M*, which will act as the MST.
(2) Choose a random vertex *v* from the graph.
(3) Add the edges that are connected to *v* into some data structure *E*.
(4) Find the edge in *E* with the minimum weight, and add this edge to *M*. Now, make *v* equal to the other vertex in the edge and repeat from step 3.

This algorithm runs until the number of edges in MST is equal to the number of vertices in the graph minus 1. So in the example below, the number of vertices in the graph is 6, so Prim's algorithm will run until the MST contains 5 edges. Once the algorithm is complete, the MST will have successfully connected all vertices in the graph with the minimum weighted edges.
(1) Store the edges in an array and search through it each time to find the edge with the smallest weight.
(2) Store the edges in a binary heap which improves the running time because edges can be found faster.

The implementation below uses method (1). It first converts the graph into a matrix and then on each iteration of Prim's algorithm, it adds edges to an array which is then searched through linearly to find the edge with the smallest weight. If there is a tie between edge weights, it simply chooses the first edge it encounters.
/* create adjacency matrix for use in prims algorithm note: we could improve the running time of prims algorithm by implementing a priority queue data structure instead of a matrix */ function createAdjMatrix(V, G) { var adjMatrix = []; // create N x N matrix filled with 0 edge weights between all vertices for (var i = 0; i < V; i++) { adjMatrix.push([]); for (var j = 0; j < V; j++) { adjMatrix[i].push(0); } } // populate adjacency matrix with correct edge weights for (var i = 0; i < G.length; i++) { adjMatrix[G[i][0]][G[i][1]] = G[i][2]; adjMatrix[G[i][1]][G[i][0]] = G[i][2]; } return adjMatrix; } function prims(V, G) { // create adj matrix from graph var adjMatrix = createAdjMatrix(V, G); // arbitrarily choose initial vertex from graph var vertex = 0; // initialize empty edges array and empty MST var MST = []; var edges = []; var visited = []; var minEdge = [null,null,Infinity]; // run prims algorithm until we create an MST // that contains every vertex from the graph while (MST.length !== V-1) { // mark this vertex as visited visited.push(vertex); // add each edge to list of potential edges for (var r = 0; r < V; r++) { if (adjMatrix[vertex][r] !== 0) { edges.push([vertex,r,adjMatrix[vertex][r]]); } } // find edge with the smallest weight to a vertex // that has not yet been visited for (var e = 0; e < edges.length; e++) { if (edges[e][2] < minEdge[2] && visited.indexOf(edges[e][1]) === -1) { minEdge = edges[e]; } } // remove min weight edge from list of edges edges.splice(edges.indexOf(minEdge), 1); // push min edge to MST MST.push(minEdge); // start at new vertex and reset min edge vertex = minEdge[1]; minEdge = [null,null,Infinity]; } return MST; } // graph vertices are actually represented as numbers // like so: 0, 1, 2, ... V-1 var a = 0, b = 1, c = 2, d = 3, e = 4, f = 5; // graph edges with weights // diagram of graph is shown above var graph = [ [a,b,2], [a,c,3], [b,d,3], [b,c,5], [b,e,4], [c,e,4], [d,e,2], [d,f,3], [e,f,5] ]; // pass the # of vertices and the graph to run prims algorithm prims(6, graph);

# create adjacency matrix for use in prims algorithm # note: we could improve the running time of prims algorithm by # implementing a priority queue data structure instead of a matrix def createAdjMatrix(V, G): adjMatrix = [] # create N x N matrix filled with 0 edge weights between all vertices for i in range(0, V): adjMatrix.append([]) for j in range(0, V): adjMatrix[i].append(0) # populate adjacency matrix with correct edge weights for i in range(0, len(G)): adjMatrix[G[i][0]][G[i][1]] = G[i][2] adjMatrix[G[i][1]][G[i][0]] = G[i][2] return adjMatrix def prims(V, G): # create adj matrix from graph adjMatrix = createAdjMatrix(V, G) # arbitrarily choose initial vertex from graph vertex = 0 # initialize empty edges array and empty MST MST = [] edges = [] visited = [] minEdge = [None,None,float('inf')] # run prims algorithm until we create an MST # that contains every vertex from the graph while len(MST) != V-1: # mark this vertex as visited visited.append(vertex) # add each edge to list of potential edges for r in range(0, V): if adjMatrix[vertex][r] != 0: edges.append([vertex,r,adjMatrix[vertex][r]]) # find edge with the smallest weight to a vertex # that has not yet been visited for e in range(0, len(edges)): if edges[e][2] < minEdge[2] and edges[e][1] not in visited: minEdge = edges[e] # remove min weight edge from list of edges edges.remove(minEdge) # push min edge to MST MST.append(minEdge) # start at new vertex and reset min edge vertex = minEdge[1] minEdge = [None,None,float('inf')] return MST # graph vertices are actually represented as numbers # like so: 0, 1, 2, ... V-1 a, b, c, d, e, f = 0, 1, 2, 3, 4, 5 # graph edges with weights # diagram of graph is shown above graph = [ [a,b,2], [a,c,3], [b,d,3], [b,c,5], [b,e,4], [c,e,4], [d,e,2], [d,f,3], [e,f,5] ] # pass the # of vertices and the graph to run prims algorithm print prims(6, graph)

mrdaniel
published this on 11/30/15 **|**

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