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Java Program for Dijkstra's shortest path algorithm | Greedy Algo-7

C / C++ Program for Dijkstra’s shortest path algorithm | Greedy Algo-7

Last Updated : 09 Oct, 2023

Problem Statement: Given a graph and a source vertex in the graph, find the shortest paths from the source to all vertices in the given graph.

What is Dijkstra’s Algorithm?

Dijkstra’s algorithm is very similar to Prim’s algorithm for minimum spanning tree. Like Prim’s MST, we generate an SPT (shortest path tree) with a given source as the root. We maintain two sets, one set contains vertices included in the shortest path tree, other set includes vertices not yet included in the shortest path tree. At every step of the algorithm, we find a vertex that is in the other set (set of not yet included) and has a minimum distance from the source.

Below are the detailed steps used in Dijkstra’s algorithm to find the shortest path from a single source vertex to all other vertices in the given graph.

Algorithm

Create a set sptSet (shortest path tree set) that keeps track of vertices included in the shortest path tree, i.e., whose minimum distance from the source is calculated and finalized. Initially, this set is empty.

Assign a distance value to all vertices in the input graph. Initialize all distance values as INFINITE. Assign the distance value as 0 for the source vertex so that it is picked first.

While sptSet doesn’t include all vertices

Pick a vertex u which is not there in sptSet and has minimum distance value.

Include u to sptSet.

Update the distance value of all adjacent vertices of u. To update the distance values, iterate through all adjacent vertices. For every adjacent vertex v, if the sum of the distance value of u (from source) and weight of edge u-v, is less than the distance value of v, then update the distance value of v.

C++ Program for Dijkstra’s Single Source Shortest Path Algorithm

C++

   // A C++ program for Dijkstra's single source shortest path
// algorithm. The program is for adjacency matrix
// representation of the graph
 
#include <limits.h>
#include <stdio.h>
 
// Number of vertices in the graph
#define V 9
 
// A utility function to find the vertex with minimum
// distance value, from the set of vertices not yet included
// in shortest path tree
int minDistance(int dist[], bool sptSet[])
{
    // Initialize min value
    int min = INT_MAX, min_index;
 
    for (int v = 0; v < V; v++)
        if (sptSet[v] == false && dist[v] <= min)
            min = dist[v], min_index = v;
 
    return min_index;
}
 
// A utility function to print the constructed distance
// array
void printSolution(int dist[], int n)
{
    printf("Vertex   Distance from Source\n");
    for (int i = 0; i < V; i++)
        printf("\t%d \t\t\t\t %d\n", i, dist[i]);
}
 
// Function that implements Dijkstra's single source
// shortest path algorithm for a graph represented using
// adjacency matrix representation
void dijkstra(int graph[V][V], int src)
{
    int dist[V]; // The output array.  dist[i] will hold the
                 // shortest
    // distance from src to i
 
    bool sptSet[V]; // sptSet[i] will be true if vertex i is
                    // included in shortest
    // path tree or shortest distance from src to i is
    // finalized
 
    // Initialize all distances as INFINITE and stpSet[] as
    // false
    for (int i = 0; i < V; i++)
        dist[i] = INT_MAX, sptSet[i] = false;
 
    // Distance of source vertex from itself is always 0
    dist[src] = 0;
 
    // Find shortest path for all vertices
    for (int count = 0; count < V - 1; count++) {
        // Pick the minimum distance vertex from the set of
        // vertices not yet processed. u is always equal to
        // src in the first iteration.
        int u = minDistance(dist, sptSet);
 
        // Mark the picked vertex as processed
        sptSet[u] = true;
 
        // Update dist value of the adjacent vertices of the
        // picked vertex.
        for (int v = 0; v < V; v++)
 
            // Update dist[v] only if is not in sptSet,
            // there is an edge from u to v, and total
            // weight of path from src to  v through u is
            // smaller than current value of dist[v]
            if (!sptSet[v] && graph[u][v]
                && dist[u] != INT_MAX
                && dist[u] + graph[u][v] < dist[v])
                dist[v] = dist[u] + graph[u][v];
    }
 
    // print the constructed distance array
    printSolution(dist, V);
}
 
// driver program to test above function
int main()
{
    /* Let us create the example graph discussed above */
    int graph[V][V] = { { 0, 4, 0, 0, 0, 0, 0, 8, 0 },
                        { 4, 0, 8, 0, 0, 0, 0, 11, 0 },
                        { 0, 8, 0, 7, 0, 4, 0, 0, 2 },
                        { 0, 0, 7, 0, 9, 14, 0, 0, 0 },
                        { 0, 0, 0, 9, 0, 10, 0, 0, 0 },
                        { 0, 0, 4, 14, 10, 0, 2, 0, 0 },
                        { 0, 0, 0, 0, 0, 2, 0, 1, 6 },
                        { 8, 11, 0, 0, 0, 0, 1, 0, 7 },
                        { 0, 0, 2, 0, 0, 0, 6, 7, 0 } };
 
    dijkstra(graph, 0);
 
    return 0;
}
 
  

Output

Vertex   Distance from Source     0                  0     1                  4     2                  12     3                  19     4                  21     5                  11     6                  9     7                  8     8                  14

Complexity Analysis

Time Complexity: The time complexity of Dijkstra’s algorithm is O(V^2). This is because the algorithm uses two nested loops to traverse the graph and find the shortest path from the source node to all other nodes.

Space Complexity: The space complexity of Dijkstra’s algorithm is O(V), where V is the number of vertices in the graph. This is because the algorithm uses an array of size V to store the distances from the source node to all other nodes.

Please refer complete article on Dijkstra’s shortest path algorithm | Greedy Algo-7 for more details!

Java Program for Dijkstra's shortest path algorithm | Greedy Algo-7

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