# Minimum spanning tree Prim’s algorithm

In the last post, we discussed how to find minimum spanning tree in a graph using Kruskal algorithm. Let’s take a look at another algorithm to find the minimum spanning tree in a graph, this algorithm is called Prim’s algorithm. Problem statement remains the same as the Kruskal algorithm, given a graph with weighted edges, find MST in it.

## Prim’s algorithm for minimum spanning tree

Basic idea pf Prim’s algorithm is to maintain two sets of nodes of the graph, one contains all the nodes which are already included into the minimum spanning tree and the other contains all nodes which are not included in MST yet. Now find an edge with least weight which connects any node in the first set to any node in the second set. Such an edge is commonly called as cut.

Let two sets be A which includes all vertices of the graph which are included in MST and V is set which contains all vertices which are not included in MST yet. Let S be set of all vertices of a graph.

To start with A will be empty and V will be equal to S. Start with any one vertex and add it to A. Also, the cost of reaching any node initially is `INFINITE`. Once we add the vertex to A, update the cost of all neighbor vertex based on cost to reach vertex added in A and cost of edge between this vertex and neighboring vertices. Remove the vertex from V.

Now, select an edge which is minimum and connects one vertex in A with a vertex in V, it does not form a cycle. (If both the ends of an edge are in same component of the graph there are bound form a cycle.)

Let’s see prim’s algorithm and then we will see how can we implement it.

1. Initialize cost of each vertex with `INFINITE`. Except from the first vertex v where we will start.
2. For each neighbor u, `cost(u) < cost(v) + w(u,v), update the cost(u).`
3. ``` Select a minimum cost neighbor vertex among these vertex and see if edge forms a cycle. If it forms a cycle take another vertex till we considered every neighboring vertices. If it does not form cycle, add it to MST and go to step 2. Repeat all above steps till all the vertices are not included in tree. ```
``` See the working of algorithm.  Minimum spanning tree algorithm's implementation. First thing we need to do is to keep track of two sets. We can do so by keeping track of each node in MST. Create an array and every time we add the node in MST, mark it as added. While considering any edge in future, just check if MST[i] is true, if it is then node is already added in MST and should not be added again.Second thing is to keep track of the cost of each node. Initially all nodes are at INFINITE cost. As we traverse the graph, update each node's cost from the current node. Keep an array of integers initialized with INFINITE and every time we add a node in MST, update distance of neighboring nodes based on cost of edge from current node to them. Last step is to find the minimum cost edge between two sets. We can scan through the distance array which we discussed in above paragraph and get a node which has least cost and not already in MST (using the array values in discussed in paragraph 1) Finding minimum can be implemented using min heap or priority queues, that would reduce the complexity of overall algorithm. #include<stdio.h> #include<stdlib.h> #define NUM_NODE 6 #define MAX_VALUE 999 #define true 1 #define false 0 //Definition of node in adjacency list formation typedef struct node{ int value; int wt; struct node *next; }Node; //Edge definition for sorting and storing minimum spanning tree typedef struct edge{ int start; int end; int wt; }Edge; Node *graph[NUM_NODE + 1]; //This function creates a node in adjancency list representation with weight Node * createNode(int j, int wt){ Node *newNode = (Node *)malloc(sizeof(Node)); if(newNode){ newNode->value = j; newNode->next = NULL; newNode->wt = wt; } else{ printf("\n Node cannot be allocated"); } return newNode; } //Adds edge in graph void addEdge(int i, int j, int wt){ Node *currentNode = graph[i]; if(currentNode == NULL){ graph[i] = createNode(j, wt); } else{ while(currentNode->next){ currentNode = currentNode->next; } currentNode->next = createNode(j, wt); } } void updateWeights(int currentVertex, int weights[], int predecessors[], int visited[]){ Node *currentNode = graph[currentVertex]; if(currentNode){ //update distance for all neighbor nodes Node * itr = currentNode->next; while(itr){ if(weights[itr->value] > itr->wt && visited[itr->value] == false ){ weights[itr->value] = itr->wt; predecessors[itr->value] = currentVertex; } itr = itr->next; } } } int getMinimumVertex( int weights[], int visited[], int numNodes ){ int currentMinWeight = MAX_VALUE; int currentMinVertex = 1; for( int i = 2; i<=numNodes; i++ ){ if( !visited[i] && weights[i] < currentMinWeight ){ currentMinWeight = weights[i]; currentMinVertex = i; } } return currentMinVertex; } //Real implementation of prims algorithm int primsMSTAlgorithm(Node * graph[], int numNodes){ //Step 1 : Initialize all arrays int weights[numNodes+1]; int visited[numNodes+1]; int predecessors[numNodes+1]; for( int i=1; i<=numNodes; i++ ){ weights[i] = MAX_VALUE; } for( int i=1; i<=numNodes; i++ ){ visited[i] = 0; } for( int i=1; i<=numNodes; i++ ){ predecessors[i] = MAX_VALUE; } weights = 0; int totalCost = 0; //Step 2 : for( int i=0; i <=numNodes; i++ ){ //Greedy strategy int closestVertex = getMinimumVertex( weights, visited, numNodes ); totalCost+= weights[closestVertex]; visited[closestVertex] = true; updateWeights( closestVertex, weights, predecessors, visited ); } return totalCost; } //Driver program int main(){ int i; for(i=1; i<=NUM_NODE; i++){ graph[i] = createNode(i,0); } addEdge(1,2,4); addEdge(2,1,4); addEdge(2,3,3); addEdge(3,2,3); addEdge(4,3,2); addEdge(3,4,2); addEdge(4,1,3); addEdge(1,4,3); addEdge(4,5,3); addEdge(5,4,3); addEdge(6,5,1); addEdge(5,6,1); addEdge(1,6,5); addEdge(6,1,5); addEdge(2,5,4); addEdge(5,2,4); int totalCost = primsMSTAlgorithm(graph, NUM_NODE); printf("Cost of minimum spanning tree : %d", totalCost); return 0; } The complexity of Prim's algorithm to find minimum spanning tree in graph is O(V2). Please share if there is anything is wrong or missing. If you are preparing for an interview and need 1 to 1 coaching for preparation, please reach out to us at communications@algorithmsandme.com or book a free session with us. ```
``` Author jitsceaitPosted on June 15, 2014February 21, 2019Categories GraphTags graphs, minimum spanning tree, msp in graph, prims algorithm to find minimum spanning treeLeave a comment on Minimum spanning tree Prim’s algorithm ```
``` Minimum Spanning Tree – Kruskal Algorithm Minimum Spanning Tree – Kruskal Algorithm Given a weighted connected undirected graph, find a minimum spanning tree in the graph. Let’s first understand what is a spanning tree? A spanning tree T of an undirected graph G is a subgraph that is a tree which includes all of the vertices of G, with the minimum possible number of edges. Wikipedia When the graph is weighted i.e each edge of the graph has some weight to move from one node to another, a spanning tree with minimum cost is called the minimum spanning tree. If all the edges contain distinct weights, there will be a unique minimum spanning tree for the graph, however, if two edges can have same weight then there can be more than one minimum spanning tree for the given graph. For the graph shown below, second figure shows spanning trees of it.  Applications of minimum spanning trees 1. Broadcast tree preparation for broadcasting on the internet. It is implemented using spanning tree protocol. 2.  Preparing telecommunication networks, water supply networks etc. There are two widely used algorithms to find a minimum spanning tree for a graph, both based on the greedy algorithm. The basic idea of the greedy algorithm is that at every step we chose the option which gives us maximum profit at that step without worrying about future steps. These algorithms are: 1. Kruskal’s algorithm: Basic idea of the kruskal algorithm to find the minimum spanning tree in the graphs is that we take each edge one by one in increasing order of their weights. For each edge check if it makes a cycle in the existing tree? If it does not create a cycle, add it to the minimum spanning tree formed till now. If it forms a cycle, discard the edge and move to the next edge. Repeat above steps until all nodes are added in the spanning tree. Resulting tree will be the minimum spanning tree. Kruskal algorithm to find minimum spanning tree Sort all edges based on weights Start with minimum cost edge. Add it to T. For each edge in graph, repeat following steps. If current edge forms a cycle, discard the edge. If current edge does not form a cycle, add it to T. Kruskal algorithm: implementation #include<stdio.h> #include<stdlib.h> #define NUM_NODE 5 #define true 1 #define false 0 //Definition of a node in adjacency list formation typedef struct node{ int value; int wt; struct node *next; }Node; //Edge definition for sorting and storing minimum spanning tree typedef struct edge{ int start; int end; int wt; }Edge; Node *graph[NUM_NODE + 1]; //This function creates a node in adjancency list representation with weight Node * createNode(int j, int wt){ Node * new_node = (Node *)malloc(sizeof(Node)); if(new_node){ new_node->value = j; new_node->next = NULL; new_node->wt = wt; } else{ printf("\n Node cannot be allocated"); } return new_node; } //Adds edge in graph void addEdge(int i, int j, int wt){ Node * currentNode = graph[i]; if(!currentNode){ graph[i] = createNode(j, wt); } else{ while(currentNode->next){ currentNode = currentNode->next; } currentNode->next = createNode(j, wt); } } //Below functions are used for Union and Find method on the disjoint set int find(int rep[], int value){ if(rep[value] == -1 || rep[value] == value) return value; else find(rep, rep[value]); } void unionSet(int rep[], int x, int y){ int xroot = find(rep, x); int yroot = find(rep, y); rep[xroot] = yroot; } void makeSet(int rep[]){ int i; for(i=0; i<= NUM_NODE; i++){ rep[i] = -1; } } //This copies all edges to array so as to sort them on weight void copyEdges(Edge edges[], Node *graph[]){ int i, count =0; for(i=1; i<= NUM_NODE; i++){ Node * u = graph[i]; if(u){ Node * v = u->next; while(v){ edges[count].start = u->value; edges[count].end =v->value; edges[count].wt = v->wt; count++; v = v->next; } } } } //Implementation of quick sort void swap(Edge edges[], int i, int j){ int temp_start = edges[i].start; int temp_end = edges[i].end; int wt = edges[i].wt; edges[i].start = edges[j].start ; edges[i].end = edges[j].end ; edges[i].wt = edges[j].wt; edges[j].start = temp_start; edges[j].end = temp_end ; edges[j].wt = wt; } int partition(Edge edges[], int start, int nEdges){ int i,j; int pivot = edges[start].wt; i = start+1; j = nEdges; while(i<=j){ while(i<=nEdges && edges[i].wt < pivot) i++; while(j>start && edges[j].wt >= pivot) j--; if(i<j){ swap(edges, i, j); } } if(j>start) swap(edges,start, j); return j; } void quicksort(Edge edges[], int start, int nEdges){ if(start >=nEdges) return; int pivot = partition(edges, start, nEdges); quicksort(edges, start, pivot); quicksort(edges, pivot+1, nEdges); } //Real implementation of krutskal algorithm void kruskal(Node * graph[], int num_edges){ int i, count =0, wt=0; Edge edges[num_edges]; Edge mst[num_edges]; copyEdges(edges, graph); quicksort(edges, 0, num_edges-1); int rep[NUM_NODE + 1]; makeSet(rep); for(i=0; i<num_edges; i++){ //Check if edge creates cycle in the tree, If not add that to tree if(find(rep, edges[i].start) != find(rep, edges[i].end)){ unionSet(rep, edges[i].start, edges[i].end); mst[count++] = edges[i]; wt += edges[i].wt; } } // Print the minimum spanning tree for(i=0; i<count; i++){ printf("(%d, %d)\n", mst[i].start, mst[i].end); } } //Driver program int main(){ int i; for(i=1; i<=NUM_NODE; i++){ graph[i] = createNode(i,0); } addEdge(1,3,10); addEdge(1,4,7); addEdge(3,4,9); addEdge(4,2,32); addEdge(4,5,23); kruskal(graph, 5); return 0; } The complexity of the above algorithm is dominated by the sorting which takes O(E log E) where E is the number of edges. To detect whether edge forms a cycle or not, we can use the Union and Find method. 2.Prim’s algorithm: It is also based on the greedy algorithm, discussed in the next post Please share if there is anything is wrong or missing. If you are preparing for an interview and need 1 to 1 coaching for preparation, please reach out to us at communications@algorithmsandme.com or book a free session with us. Author jitsceaitPosted on June 14, 2014February 21, 2019Categories GraphTags graphs, kruskal algorithm, krustal minimum spanning tree algorithm, minimum spanning tree, msp in graphLeave a comment on Minimum Spanning Tree – Kruskal Algorithm ```