// C++ program for Dijkstra's single source shortest path // algorithm. The program is for adjacency matrix // representation of the graph #include  using namespace std; #include  // 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[]) {  cout << 'Vertex 	 Distance from Source' << endl;  for (int i = 0; i < V; i++)  cout << i << ' 				' << dist[i] << endl; } // 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); } // driver's code 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 } };  // Function call  dijkstra(graph, 0);  return 0; } // This code is contributed by shivanisinghss2110>

// C program for Dijkstra's single source shortest path // algorithm. The program is for adjacency matrix // representation of the graph #include  #include  #include  // 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[]) {  printf('Vertex 		 Distance from Source
');  for (int i = 0; i < V; i++)  printf('%d 				 %d
', 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); } // driver's code 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 } };  // Function call  dijkstra(graph, 0);  return 0; }>

// A Java program for Dijkstra's single source shortest path // algorithm. The program is for adjacency matrix // representation of the graph import java.io.*; import java.lang.*; import java.util.*; class ShortestPath {  // A utility function to find the vertex with minimum  // distance value, from the set of vertices not yet  // included in shortest path tree  static final int V = 9;  int minDistance(int dist[], Boolean sptSet[])  {  // Initialize min value  int min = Integer.MAX_VALUE, min_index = -1;  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[])  {  System.out.println(  'Vertex 		 Distance from Source');  for (int i = 0; i < V; i++)  System.out.println(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[][], int src)  {  int dist[] = new int[V]; // The output array.  // dist[i] will hold  // the shortest distance from src to i  // sptSet[i] will true if vertex i is included in  // shortest path tree or shortest distance from src  // to i is finalized  Boolean sptSet[] = new Boolean[V];  // Initialize all distances as INFINITE and stpSet[]  // as false  for (int i = 0; i < V; i++) {  dist[i] = Integer.MAX_VALUE;  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 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] != 0  && dist[u] != Integer.MAX_VALUE  && dist[u] + graph[u][v] < dist[v])  dist[v] = dist[u] + graph[u][v];  }  // print the constructed distance array  printSolution(dist);  }  // Driver's code  public static void main(String[] args)  {  /* Let us create the example graph discussed above  */  int graph[][]  = new int[][] { { 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 } };  ShortestPath t = new ShortestPath();  // Function call  t.dijkstra(graph, 0);  } } // This code is contributed by Aakash Hasija>

# Python program for Dijkstra's single # source shortest path algorithm. The program is # for adjacency matrix representation of the graph # Library for INT_MAX import sys class Graph(): def __init__(self, vertices): self.V = vertices self.graph = [[0 for column in range(vertices)] for row in range(vertices)] def printSolution(self, dist): print('Vertex 	Distance from Source') for node in range(self.V): print(node, '	', dist[node]) # A utility function to find the vertex with # minimum distance value, from the set of vertices # not yet included in shortest path tree def minDistance(self, dist, sptSet): # Initialize minimum distance for next node min = sys.maxsize # Search not nearest vertex not in the # shortest path tree for u in range(self.V): if dist[u] < min and sptSet[u] == False: min = dist[u] min_index = u return min_index # Function that implements Dijkstra's single source # shortest path algorithm for a graph represented # using adjacency matrix representation def dijkstra(self, src): dist = [sys.maxsize] * self.V dist[src] = 0 sptSet = [False] * self.V for cout in range(self.V): # Pick the minimum distance vertex from # the set of vertices not yet processed. # x is always equal to src in first iteration x = self.minDistance(dist, sptSet) # Put the minimum distance vertex in the # shortest path tree sptSet[x] = True # Update dist value of the adjacent vertices # of the picked vertex only if the current # distance is greater than new distance and # the vertex in not in the shortest path tree for y in range(self.V): if self.graph[x][y]>0 ir sptSet[y] == Netiesa ir  dist[y]> dist[x] + self.graph[x][y]: dist[y] = dist[x] + self.graph[x][y] self.printSolution(dist) # Tvarkyklės kodas if __name__ == '__main__': g = Graph(9) g.graph = [[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] ] g.dijkstra(0) # Šį kodą sukūrė Divyanshu Mehta ir atnaujino Pranav Singh Sambyal>>C#

// A Javascript program for Dijkstra's single  // source shortest path algorithm.  // The program is for adjacency matrix  // representation of the graph  let 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  function minDistance(dist,sptSet) {    // Initialize min value   let min = Number.MAX_VALUE;  let min_index = -1;    for(let 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  function printSolution(dist) {  console.log('Vertex 		 Distance from Source ');  for(let i = 0; i < V; i++)  {  console.log(i + ' 		 ' +   dist[i] + ' ');  } } // Function that implements Dijkstra's  // single source shortest path algorithm  // for a graph represented using adjacency  // matrix representation  function dijkstra(graph, src) {  let dist = new Array(V);  let sptSet = new Array(V);    // Initialize all distances as   // INFINITE and stpSet[] as false   for(let i = 0; i < V; i++)  {  dist[i] = Number.MAX_VALUE;  sptSet[i] = false;  }    // Distance of source vertex   // from itself is always 0   dist[src] = 0;    // Find shortest path for all vertices   for(let 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 first iteration.   let 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(let 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] != 0 &&   dist[u] != Number.MAX_VALUE &&  dist[u] + graph[u][v] < dist[v])  {  dist[v] = dist[u] + graph[u][v];  }  }  }    // Print the constructed distance array  printSolution(dist); } // Driver code let graph = [ [ 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); // This code is contributed by rag2127>

// C++ Program to find Dijkstra's shortest path using // priority_queue in STL #include  using namespace std; #define INF 0x3f3f3f3f // iPair ==>Integer Pair typedef pora iPair; // Ši klasė vaizduoja nukreiptą grafiką naudojant // gretimų sąrašo reprezentacijos klasė Graph { int V; // Viršūnių skaičius // Svertiniame grafe turime išsaugoti kiekvieno briaunų sąrašo viršūnę // ir svorio porą>> adj; vieša: Grafas(int V); // Konstruktorius // funkcija pridėti briauną į grafiką void addEdge(int u, int v, int w);  // spausdina trumpiausią kelią iš s void shortestPath(int s); }; // Skiria atmintį gretimų sąrašui Graph::Graph(int V) { this->V = V;  adj = naujas sąrašas [V]; } void Graph::addEdge(int u, int v, int w) { adj[u].push_back(make_pair(v, w));  adj[v].push_back(make_pair(u, w)); } // Spausdina trumpiausius kelius nuo src iki visų kitų viršūnių void Graph::shortestPath(int src) { // Sukurkite prioritetinę eilę viršūnėms, kurios // yra iš anksto apdorojamos, saugoti. Tai keista C++ sintaksė.  // Norėdami sužinoti daugiau apie šią sintaksę, žr. toliau pateiktą nuorodą // https://www.techcodeview.com priority_queue , didesnis > pq;  // Sukurkite atstumų vektorių ir inicijuokite visus // atstumus kaip begalinį (INF) vektorių dist(V, INF);  // Įterpti patį šaltinį į prioritetinę eilę ir inicijuoti // jo atstumą kaip 0. pq.push(make_pair(0, src));  dist[src] = 0;  /* Perėjimas, kol prioritetinė eilė tampa tuščia (arba visi atstumai nėra užbaigti) */ while (!pq.empty()) { // Pirmoji poros viršūnė yra mažiausias atstumas // viršūnė, ištraukite ją iš prioritetinės eilės.  // viršūnių etiketė saugoma antroje poroje (tai // turi būti padaryta taip, kad viršūnės išliktų // surūšiuotas atstumas (atstumas turi būti pirmasis elementas // poroje) int u = pq.top().second; pq.pop(); // 'i' naudojamas visoms gretimoms // viršūnių sąrašo viršūnėms gauti>::iteratorius i;  for (i = adj[u].begin(); i != adj[u].end(); ++i) { // Gauti viršūnės etiketę ir srovės svorį // greta u.  int v = (*i).pirma;  int svoris = (*i).sekundė;  // Jei yra sutrumpintas kelias į v per u.  if (dist[v]> dist[u] + svoris) { // Atnaujinamas v atstumas dist[v] = dist[u] + svoris;  pq.push(make_pair(dist[v], v));  } } } // Spausdinti trumpiausius atstumus, saugomus dist[] printf('Vertex Distance from Source
');  už (int i = 0; i< V; ++i)  printf('%d 		 %d
', i, dist[i]); } // Driver's code int main() {  // create the graph given in above figure  int V = 9;  Graph g(V);  // making above shown graph  g.addEdge(0, 1, 4);  g.addEdge(0, 7, 8);  g.addEdge(1, 2, 8);  g.addEdge(1, 7, 11);  g.addEdge(2, 3, 7);  g.addEdge(2, 8, 2);  g.addEdge(2, 5, 4);  g.addEdge(3, 4, 9);  g.addEdge(3, 5, 14);  g.addEdge(4, 5, 10);  g.addEdge(5, 6, 2);  g.addEdge(6, 7, 1);  g.addEdge(6, 8, 6);  g.addEdge(7, 8, 7);  // Function call  g.shortestPath(0);  return 0; }>

import java.util.*; class Graph {  private int V;  private List> adj;  Grafas(int V) { this.V = V;  adj = naujas ArrayList();  už (int i = 0; i< V; i++) {  adj.add(new ArrayList());  }  }  void addEdge(int u, int v, int w) {  adj.get(u).add(new iPair(v, w));  adj.get(v).add(new iPair(u, w));  }  void shortestPath(int src) {  PriorityQueue pq = new PriorityQueue(V, Comparator.comparingInt(o -> o.second));  int[] dist = naujas int[V];  Masyvai.fill(dist, Integer.MAX_VALUE);  pq.add(new iPair(0, src));  dist[src] = 0;  while (!pq.isEmpty()) { int u = pq.poll().second;  for (iPair v : adj.get(u)) { if (dist[v.first]> dist[u] + v.second) { dist[v.first] = dist[u] + v.second;  pq.add(new iPair(dist[v.first], v.first));  } } } System.out.println('Vertex Distance from Source');  už (int i = 0; i< V; i++) {  System.out.println(i + '		' + dist[i]);  }  }  static class iPair {  int first, second;  iPair(int first, int second) {  this.first = first;  this.second = second;  }  } } public class Main {  public static void main(String[] args) {  int V = 9;  Graph g = new Graph(V);  g.addEdge(0, 1, 4);  g.addEdge(0, 7, 8);  g.addEdge(1, 2, 8);  g.addEdge(1, 7, 11);  g.addEdge(2, 3, 7);  g.addEdge(2, 8, 2);  g.addEdge(2, 5, 4);  g.addEdge(3, 4, 9);  g.addEdge(3, 5, 14);  g.addEdge(4, 5, 10);  g.addEdge(5, 6, 2);  g.addEdge(6, 7, 1);  g.addEdge(6, 8, 6);  g.addEdge(7, 8, 7);  g.shortestPath(0);  } }>

import heapq # iPair ==>Sveikųjų skaičių pora iPair = korta # Ši klasė reiškia nukreiptą grafiką naudojant # gretimų sąrašo vaizdavimo klasę Grafikas: def __init__(self, V: int): # Konstruktorius self.V = V self.adj = [[] _ diapazone(V) )] def addEdge(self, u: int, v: int, w: int): self.adj[u].append((v, w)) self.adj[v].append((u, w)) # Spausdina trumpiausius kelius nuo src iki visų kitų viršūnių def shortestPath(self, src: int): # Sukurkite prioritetinę eilę viršūnėms, kurios # yra iš anksto apdorojamos, saugoti pq = [] heapq.heappush(pq, (0, src)) # Sukurkite atstumų vektorių ir inicijuokite visus # atstumus kaip begalinius (INF) dist = [float('inf')] * self.V dist[src] = 0, o pq: # Pirmoji poros viršūnė yra mažiausias atstumas # viršūnė, išskleiskite ją iš prioritetinės eilės. # viršūnės etiketė saugoma antroje poros d, u = heapq.heappop(pq) # 'i' naudojamas norint gauti visas gretimas # viršūnės viršūnes v, svoris self.adj[u]: # Jei yra sutrumpintas kelias į v per u. if dist[v]> dist[u] + svoris: # Atnaujinamas atstumas nuo v dist[v] = dist[u] + svoris heapq.heappush(pq, (dist[v], v)) # Spausdinti trumpiausius atstumus, saugomus dist[] i diapazone(self.V): print(f'{i} 		 {dist[i]}') # Tvarkyklės kodas if __name__ == '__main__': # sukurkite grafiką, pateiktą aukščiau esančiame paveikslėlyje V = 9 g = Grafas(V) # sukurkite aukščiau parodytą grafiką g.addEdge(0, 1, 4) g.addEdge(0, 7, 8) g.addEdge(1, 2, 8) g.pridėtiEdge(1, 7, 11) g.addEdge(2, 3, 7) g.addEdge(2, 8, 2) g.addEdge(2, 5, 4) g.addEdge(3, 4, 9) g.pridėtiEdge(3, 5, 14) g.addEdge(4, 5, 10) g.addEdge(5, 6, 2) g.addEdge(6, 7, 1) g.addEdge(6, 8, 6) g.addEdge(7, 8, 7) g.shortestPath(0)>>C# dist[u] + svoris) { // Atnaujinamas v atstumas dist[v] = dist[u] + svoris;  pq.Add(new int[] { dist[v], v });  } } } // Spausdinti trumpiausius atstumus, saugomus dist[] Console.WriteLine('Vertex Distance from Source');  už (int i = 0; i< V; ++i)  Console.WriteLine(i + '	' + dist[i]);  }  private class DistanceComparer : IComparer { public int Palyginti(int[] x, int[] y) { if (x[0] == y[0]) { return x[1] - y[1];  } grįžti x[0] - y[0];  } } } public class Programa { // Vairuotojo kodas public static void Main() { // sukurti grafiką, pateiktą aukščiau esančiame paveikslėlyje int V = 9;  Grafas g = naujas Grafas(V);  // Aukščiau parodyto grafiko sudarymas g.AddEdge(0, 1, 4);  g.AddEdge(0, 7, 8);  g.AddEdge(1, 2, 8);  g.AddEdge(1, 7, 11);  g.AddEdge(2, 3, 7);  g.AddEdge(2, 8, 2);  g.AddEdge(2, 5, 4);  g.AddEdge(3, 4, 9);  g.AddEdge(3, 5, 14);  g.AddEdge(4, 5, 10);  g.AddEdge(5, 6, 2);  g.AddEdge(6, 7, 1);  g.AddEdge(6, 8, 6);  g.AddEdge(7, 8, 7);  g.ShortestPath(0);  } } // šį kodą pateikė bhardwajji>

// javascript Program to find Dijkstra's shortest path using // priority_queue in STL const INF = 2147483647; // This class represents a directed graph using // adjacency list representation class Graph {    constructor(V){    // No. of vertices  this.V = V;    // In a weighted graph, we need to store vertex  // and weight pair for every edge  this.adj = new Array(V);  for(let i = 0; i < V; i++){  this.adj[i] = new Array();  }  }  addEdge(u, v, w)  {  this.adj[u].push([v, w]);  this.adj[v].push([u, w]);  }  // Prints shortest paths from src to all other vertices  shortestPath(src)  {  // Create a priority queue to store vertices that  // are being preprocessed. This is weird syntax in C++.  // Refer below link for details of this syntax  // https://www.techcodeview.com  let pq = [];  // Create a vector for distances and initialize all  // distances as infinite (INF)  let dist = new Array(V).fill(INF);  // Insert source itself in priority queue and initialize  // its distance as 0.  pq.push([0, src]);  dist[src] = 0;  /* Looping till priority queue becomes empty (or all  distances are not finalized) */  while (pq.length>0) { // Pirmoji poros viršūnė yra mažiausias atstumas // viršūnė, ištraukite ją iš prioritetinės eilės.  // viršūnių etiketė saugoma antroje poroje (tai // turi būti padaryta taip, kad viršūnės išliktų // surūšiuotas atstumas (atstumas turi būti pirmasis elementas // poroje) tegul u = pq[0][1]; pq.shift(); // 'i' naudojamas norint gauti visas gretimas // viršūnės for(te i = 0; i< this.adj[u].length; i++){    // Get vertex label and weight of current  // adjacent of u.  let v = this.adj[u][i][0];  let weight = this.adj[u][i][1];  // If there is shorted path to v through u.  if (dist[v]>dist[u] + svoris) { // Atnaujinamas v atstumas dist[v] = dist[u] + svoris;  pq.push([dist[v], v]);  pq.sort((a, b) =>{ if(a[0] == b[0]) return a[1] - b[1]; return a[0] - b[0]; });  } } } // Spausdinti trumpiausius atstumus, saugomus dist[] console.log('Vertex Distance from Source');  for (tegul i = 0; i< V; ++i)  console.log(i, ' ', dist[i]);  } } // Driver's code // create the graph given in above figure let V = 9; let g = new Graph(V); // making above shown graph g.addEdge(0, 1, 4); g.addEdge(0, 7, 8); g.addEdge(1, 2, 8); g.addEdge(1, 7, 11); g.addEdge(2, 3, 7); g.addEdge(2, 8, 2); g.addEdge(2, 5, 4); g.addEdge(3, 4, 9); g.addEdge(3, 5, 14); g.addEdge(4, 5, 10); g.addEdge(5, 6, 2); g.addEdge(6, 7, 1); g.addEdge(6, 8, 6); g.addEdge(7, 8, 7); // Function call g.shortestPath(0); // The code is contributed by Nidhi goel.>