in Computer Programming, Data Structures and Algorithms, Greedy Algorithms

Dijkstra’s Algorithm

Dijkstra's Algorithm
Dijkstra's Algorithm

Dijkstra’s algorithm allows us to find the briefest way between any two vertices of a graph.

It differs from the minimum spanning tree in light of the fact that the shortest distance between two vertices might not include all the vertices of the graph.

In this article, you will learn-

What is Dijkstra’s calculation?

Dijkstra’s Algorithm allows you to calculate the shortest path between one node of your choosing and all other nodes in a graph.

How Dijkstra’s Algorithm works

Dijkstra’s Algorithm works on the basis that any subpath B – > D of the briefest way A – > D between vertices An and D is likewise the shortest way between vertices B and D.

  • shortest-subpath

Djikstra used this property the other way i.e we overestimate the distance of every vertex from the beginning vertex. At that point, we visit every node and its neighbors to find the briefest subpath to those neighbors.

The algorithm uses a greedy methodology as in we find the next best arrangement hoping that the final result is the best solution for the entire issue.

Illustration of Dijkstra’s algorithm

It is simpler to begin with an example and afterward consider the algorithm.

  • dj-1
  • dj-2
  • dj-3
  • dj-4
  • dj-5
  • dj-6
  • dj-7
  • dj-8

Djikstra’s algorithm pseudocode

We need to keep up the way distance of each vertex. We can store that in an array of size v, where v is the number of vertices.

We additionally need to have the option to get the briefest way, not just know the length of the shortest way. For this, we map every vertex to the vertex that last updated its way length.

When the algorithm is finished, we can backtrack from the destination vertex to the source vertex to discover the way.

A minimum priority queue can be used to productively get the vertex with least way distance.

function dijkstra(G, S)
    for each vertex V in G
        distance[V] <- infinite
        previous[V] <- NULL
        If V != S, add V to Priority Queue Q
    distance[S] <- 0
	
    while Q IS NOT EMPTY
        U <- Extract MIN from Q
        for each unvisited neighbour V of U
            tempDistance <- distance[U] + edge_weight(U, V)
            if tempDistance < distance[V]
                distance[V] <- tempDistance
                previous[V] <- U
    return distance[], previous[]

Code for Dijkstra’s Algorithm

The implementation of Dijkstra’s Algorithm in C++ is given underneath. The complexity of the code can be improved, however, the abstractions are convenient to relate the code with the algorithm.

Python

# Dijkstra's Algorithm in Python


import sys

# Providing the graph
vertices = [[0, 0, 1, 1, 0, 0, 0],
            [0, 0, 1, 0, 0, 1, 0],
            [1, 1, 0, 1, 1, 0, 0],
            [1, 0, 1, 0, 0, 0, 1],
            [0, 0, 1, 0, 0, 1, 0],
            [0, 1, 0, 0, 1, 0, 1],
            [0, 0, 0, 1, 0, 1, 0]]

edges = [[0, 0, 1, 2, 0, 0, 0],
         [0, 0, 2, 0, 0, 3, 0],
         [1, 2, 0, 1, 3, 0, 0],
         [2, 0, 1, 0, 0, 0, 1],
         [0, 0, 3, 0, 0, 2, 0],
         [0, 3, 0, 0, 2, 0, 1],
         [0, 0, 0, 1, 0, 1, 0]]

# Find which vertex is to be visited next
def to_be_visited():
    global visited_and_distance
    v = -10
    for index in range(num_of_vertices):
        if visited_and_distance[index][0] == 0 \
            and (v < 0 or visited_and_distance[index][1] <=
                 visited_and_distance[v][1]):
            v = index
    return v


num_of_vertices = len(vertices[0])

visited_and_distance = [[0, 0]]
for i in range(num_of_vertices-1):
    visited_and_distance.append([0, sys.maxsize])

for vertex in range(num_of_vertices):

    # Find next vertex to be visited
    to_visit = to_be_visited()
    for neighbor_index in range(num_of_vertices):

        # Updating new distances
        if vertices[to_visit][neighbor_index] == 1 and \
                visited_and_distance[neighbor_index][0] == 0:
            new_distance = visited_and_distance[to_visit][1] \
                + edges[to_visit][neighbor_index]
            if visited_and_distance[neighbor_index][1] > new_distance:
                visited_and_distance[neighbor_index][1] = new_distance
        
        visited_and_distance[to_visit][0] = 1

i = 0

# Printing the distance
for distance in visited_and_distance:
    print("Distance of ", chr(ord('a') + i),
          " from source vertex: ", distance[1])
    i = i + 1

Java

// Dijkstra's Algorithm in Java

public class Dijkstra {

  public static void dijkstra(int[][] graph, int source) {
    int count = graph.length;
    boolean[] visitedVertex = new boolean[count];
    int[] distance = new int[count];
    for (int i = 0; i < count; i++) {
      visitedVertex[i] = false;
      distance[i] = Integer.MAX_VALUE;
    }

    // Distance of self loop is zero
    distance[source] = 0;
    for (int i = 0; i < count; i++) {

      // Update the distance between neighbouring vertex and source vertex
      int u = findMinDistance(distance, visitedVertex);
      visitedVertex[u] = true;

      // Update all the neighbouring vertex distances
      for (int v = 0; v < count; v++) {
        if (!visitedVertex[v] && graph[u][v] != 0 && (distance[u] + graph[u][v] < distance[v])) {
          distance[v] = distance[u] + graph[u][v];
        }
      }
    }
    for (int i = 0; i < distance.length; i++) {
      System.out.println(String.format("Distance from %s to %s is %s", source, i, distance[i]));
    }

  }

  // Finding the minimum distance
  private static int findMinDistance(int[] distance, boolean[] visitedVertex) {
    int minDistance = Integer.MAX_VALUE;
    int minDistanceVertex = -1;
    for (int i = 0; i < distance.length; i++) {
      if (!visitedVertex[i] && distance[i] < minDistance) {
        minDistance = distance[i];
        minDistanceVertex = i;
      }
    }
    return minDistanceVertex;
  }

  public static void main(String[] args) {
    int graph[][] = new int[][] { { 0, 0, 1, 2, 0, 0, 0 }, { 0, 0, 2, 0, 0, 3, 0 }, { 1, 2, 0, 1, 3, 0, 0 },
        { 2, 0, 1, 0, 0, 0, 1 }, { 0, 0, 3, 0, 0, 2, 0 }, { 0, 3, 0, 0, 2, 0, 1 }, { 0, 0, 0, 1, 0, 1, 0 } };
    Dijkstra T = new Dijkstra();
    T.dijkstra(graph, 0);
  }
}

C

// Dijkstra's Algorithm in C

#include <stdio.h>
#define INFINITY 9999
#define MAX 10

void Dijkstra(int Graph[MAX][MAX], int n, int start);

void Dijkstra(int Graph[MAX][MAX], int n, int start) {
  int cost[MAX][MAX], distance[MAX], pred[MAX];
  int visited[MAX], count, mindistance, nextnode, i, j;

  // Creating cost matrix
  for (i = 0; i < n; i++)
    for (j = 0; j < n; j++)
      if (Graph[i][j] == 0)
        cost[i][j] = INFINITY;
      else
        cost[i][j] = Graph[i][j];

  for (i = 0; i < n; i++) {
    distance[i] = cost[start][i];
    pred[i] = start;
    visited[i] = 0;
  }

  distance[start] = 0;
  visited[start] = 1;
  count = 1;

  while (count < n - 1) {
    mindistance = INFINITY;

    for (i = 0; i < n; i++)
      if (distance[i] < mindistance && !visited[i]) {
        mindistance = distance[i];
        nextnode = i;
      }

    visited[nextnode] = 1;
    for (i = 0; i < n; i++)
      if (!visited[i])
        if (mindistance + cost[nextnode][i] < distance[i]) {
          distance[i] = mindistance + cost[nextnode][i];
          pred[i] = nextnode;
        }
    count++;
  }

  // Printing the distance
  for (i = 0; i < n; i++)
    if (i != start) {
      printf("\nDistance from source to %d: %d", i, distance[i]);
    }
}
int main() {
  int Graph[MAX][MAX], i, j, n, u;
  n = 7;

  Graph[0][0] = 0;
  Graph[0][1] = 0;
  Graph[0][2] = 1;
  Graph[0][3] = 2;
  Graph[0][4] = 0;
  Graph[0][5] = 0;
  Graph[0][6] = 0;

  Graph[1][0] = 0;
  Graph[1][1] = 0;
  Graph[1][2] = 2;
  Graph[1][3] = 0;
  Graph[1][4] = 0;
  Graph[1][5] = 3;
  Graph[1][6] = 0;

  Graph[2][0] = 1;
  Graph[2][1] = 2;
  Graph[2][2] = 0;
  Graph[2][3] = 1;
  Graph[2][4] = 3;
  Graph[2][5] = 0;
  Graph[2][6] = 0;

  Graph[3][0] = 2;
  Graph[3][1] = 0;
  Graph[3][2] = 1;
  Graph[3][3] = 0;
  Graph[3][4] = 0;
  Graph[3][5] = 0;
  Graph[3][6] = 1;

  Graph[4][0] = 0;
  Graph[4][1] = 0;
  Graph[4][2] = 3;
  Graph[4][3] = 0;
  Graph[4][4] = 0;
  Graph[4][5] = 2;
  Graph[4][6] = 0;

  Graph[5][0] = 0;
  Graph[5][1] = 3;
  Graph[5][2] = 0;
  Graph[5][3] = 0;
  Graph[5][4] = 2;
  Graph[5][5] = 0;
  Graph[5][6] = 1;

  Graph[6][0] = 0;
  Graph[6][1] = 0;
  Graph[6][2] = 0;
  Graph[6][3] = 1;
  Graph[6][4] = 0;
  Graph[6][5] = 1;
  Graph[6][6] = 0;

  u = 0;
  Dijkstra(Graph, n, u);

  return 0;
}

C++

// Dijkstra's Algorithm in C++

#include <iostream>
#include <vector>

#define INT_MAX 10000000

using namespace std;

void DijkstrasTest();

int main() {
  DijkstrasTest();
  return 0;
}

class Node;
class Edge;

void Dijkstras();
vector<Node*>* AdjacentRemainingNodes(Node* node);
Node* ExtractSmallest(vector<Node*>& nodes);
int Distance(Node* node1, Node* node2);
bool Contains(vector<Node*>& nodes, Node* node);
void PrintShortestRouteTo(Node* destination);

vector<Node*> nodes;
vector<Edge*> edges;

class Node {
   public:
  Node(char id)
    : id(id), previous(NULL), distanceFromStart(INT_MAX) {
    nodes.push_back(this);
  }

   public:
  char id;
  Node* previous;
  int distanceFromStart;
};

class Edge {
   public:
  Edge(Node* node1, Node* node2, int distance)
    : node1(node1), node2(node2), distance(distance) {
    edges.push_back(this);
  }
  bool Connects(Node* node1, Node* node2) {
    return (
      (node1 == this->node1 &&
       node2 == this->node2) ||
      (node1 == this->node2 &&
       node2 == this->node1));
  }

   public:
  Node* node1;
  Node* node2;
  int distance;
};

///////////////////
void DijkstrasTest() {
  Node* a = new Node('a');
  Node* b = new Node('b');
  Node* c = new Node('c');
  Node* d = new Node('d');
  Node* e = new Node('e');
  Node* f = new Node('f');
  Node* g = new Node('g');

  Edge* e1 = new Edge(a, c, 1);
  Edge* e2 = new Edge(a, d, 2);
  Edge* e3 = new Edge(b, c, 2);
  Edge* e4 = new Edge(c, d, 1);
  Edge* e5 = new Edge(b, f, 3);
  Edge* e6 = new Edge(c, e, 3);
  Edge* e7 = new Edge(e, f, 2);
  Edge* e8 = new Edge(d, g, 1);
  Edge* e9 = new Edge(g, f, 1);

  a->distanceFromStart = 0;  // set start node
  Dijkstras();
  PrintShortestRouteTo(f);
}

///////////////////

void Dijkstras() {
  while (nodes.size() > 0) {
    Node* smallest = ExtractSmallest(nodes);
    vector<Node*>* adjacentNodes =
      AdjacentRemainingNodes(smallest);

    const int size = adjacentNodes->size();
    for (int i = 0; i < size; ++i) {
      Node* adjacent = adjacentNodes->at(i);
      int distance = Distance(smallest, adjacent) +
               smallest->distanceFromStart;

      if (distance < adjacent->distanceFromStart) {
        adjacent->distanceFromStart = distance;
        adjacent->previous = smallest;
      }
    }
    delete adjacentNodes;
  }
}

// Find the node with the smallest distance,
// remove it, and return it.
Node* ExtractSmallest(vector<Node*>& nodes) {
  int size = nodes.size();
  if (size == 0) return NULL;
  int smallestPosition = 0;
  Node* smallest = nodes.at(0);
  for (int i = 1; i < size; ++i) {
    Node* current = nodes.at(i);
    if (current->distanceFromStart <
      smallest->distanceFromStart) {
      smallest = current;
      smallestPosition = i;
    }
  }
  nodes.erase(nodes.begin() + smallestPosition);
  return smallest;
}

// Return all nodes adjacent to 'node' which are still
// in the 'nodes' collection.
vector<Node*>* AdjacentRemainingNodes(Node* node) {
  vector<Node*>* adjacentNodes = new vector<Node*>();
  const int size = edges.size();
  for (int i = 0; i < size; ++i) {
    Edge* edge = edges.at(i);
    Node* adjacent = NULL;
    if (edge->node1 == node) {
      adjacent = edge->node2;
    } else if (edge->node2 == node) {
      adjacent = edge->node1;
    }
    if (adjacent && Contains(nodes, adjacent)) {
      adjacentNodes->push_back(adjacent);
    }
  }
  return adjacentNodes;
}

// Return distance between two connected nodes
int Distance(Node* node1, Node* node2) {
  const int size = edges.size();
  for (int i = 0; i < size; ++i) {
    Edge* edge = edges.at(i);
    if (edge->Connects(node1, node2)) {
      return edge->distance;
    }
  }
  return -1;  // should never happen
}

// Does the 'nodes' vector contain 'node'
bool Contains(vector<Node*>& nodes, Node* node) {
  const int size = nodes.size();
  for (int i = 0; i < size; ++i) {
    if (node == nodes.at(i)) {
      return true;
    }
  }
  return false;
}

///////////////////

void PrintShortestRouteTo(Node* destination) {
  Node* previous = destination;
  cout << "Distance from start: "
     << destination->distanceFromStart << endl;
  while (previous) {
    cout << previous->id << " ";
    previous = previous->previous;
  }
  cout << endl;
}

// these two not needed
vector<Edge*>* AdjacentEdges(vector<Edge*>& Edges, Node* node);
void RemoveEdge(vector<Edge*>& Edges, Edge* edge);

vector<Edge*>* AdjacentEdges(vector<Edge*>& edges, Node* node) {
  vector<Edge*>* adjacentEdges = new vector<Edge*>();

  const int size = edges.size();
  for (int i = 0; i < size; ++i) {
    Edge* edge = edges.at(i);
    if (edge->node1 == node) {
      cout << "adjacent: " << edge->node2->id << endl;
      adjacentEdges->push_back(edge);
    } else if (edge->node2 == node) {
      cout << "adjacent: " << edge->node1->id << endl;
      adjacentEdges->push_back(edge);
    }
  }
  return adjacentEdges;
}

void RemoveEdge(vector<Edge*>& edges, Edge* edge) {
  vector<Edge*>::iterator it;
  for (it = edges.begin(); it < edges.end(); ++it) {
    if (*it == edge) {
      edges.erase(it);
      return;
    }
  }
}

Dijkstra’s Algorithm Complexity

Time Complexity: O(E Log V)

where, E is the number of edges and V is the number of vertices.

Space Complexity: O(V)

Dijkstra’s Algorithm Applications

  • To find the shortest path
  • In social networking applications
  • In a telephone network
  • To find the locations on the map

Thanks for reading! We hope you found this tutorial helpful and we would love to hear your feedback in the Comments section below. And show us what you’ve learned by sharing your photos and creative projects with us.

Greedy Algorithms

salman khan

Written by worldofitech

Leave a Reply

Ford-Fulkerson Algorithm

Ford-Fulkerson Algorithm
Kruskal's Algorithm

Kruskal’s Algorithm