Dijkstra’s – Shortest Path Algorithm (SPT) – Adjacency List and Min Heap – Java Implementation

Earlier we have seen the basics of Dijkstra algorithm. In this article, we will see its implementation using the adjacency list and Min Heap.

brief: What is Dijkstra’s algorithm?

  • Dijkstra algorithm is a greedy algorithm.
  • It finds a shortest-path tree for a weighted undirected graph.
  • This means it finds the shortest paths between nodes in a graph, which may represent, for example, road networks
  • For a given source node in the graph, the algorithm finds the shortest path between the source node and every other node.
  • This algorithm also used for finding the shortest paths from a single node to a single destination node by stopping the algorithm once the shortest path to the destination node has been determined.
  • Dijkstra’s algorithm is very similar to Prim’s algorithm. In Prim’s algorithm, we create minimum spanning tree (MST) and in the Dijkstra algorithm, we create a shortest-path tree (SPT) from the given source.

We strongly recommend reading the following articles

  1. Dijkstra algorithm and how it works
  2. Adjacency List
  3. Implementation of min-Heap

Example:
Dijkstra - Shortest Path Algorithm

Implementation – Adjacency List and Min Heap

  • Create min Heap of size = no of vertices.
  • Create a heapNode for each vertex which will store two pieces of information. a). vertex b). Distance from vertex from source vertex.
  • Use spt[] to keep track of the vertices which are currently in min-heap.
  • For each heapNode, initialize distance as +∞ except the heapNode for the source vertex for which distance will be 0.
  • while minHeap is not empty
    1. Extract the min node from the heap, say it vertex u, and add it to the SPT.
    2. Decrease distance: For adjacent vertex v, if v is not in SPT[] and distance[v] > distance[u] + edge u-v weight then update distance[v] = distance[u] + edge u-v weight

Time Complexity:

Total vertices: V, Total Edges : E

  • O(logV) – to extract each vertex from heap. So for V vertices – O(VlogV)
  • O(logV) – each time decrease the distance of a vertex. Decrease distance will be called for at most once for each edge. So for total E edge – O(ElogV)
  • So over all complexity: O(VlogV) + O(ElogV) = O((E+V)logV) = O(ElogV)

See the animation below for more understanding

Complete Code:

import java.util.LinkedList;
public class DijkstraUsingMinHeap {
static class Edge {
int source;
int destination;
int weight;
public Edge(int source, int destination, int weight) {
this.source = source;
this.destination = destination;
this.weight = weight;
}
}
static class HeapNode{
int vertex;
int distance;
}
static class Graph {
int vertices;
LinkedList<Edge>[] adjacencylist;
Graph(int vertices) {
this.vertices = vertices;
adjacencylist = new LinkedList[vertices];
//initialize adjacency lists for all the vertices
for (int i = 0; i <vertices ; i++) {
adjacencylist[i] = new LinkedList<>();
}
}
public void addEdge(int source, int destination, int weight) {
Edge edge = new Edge(source, destination, weight);
adjacencylist[source].addFirst(edge);
edge = new Edge(destination, source, weight);
adjacencylist[destination].addFirst(edge); //for undirected graph
}
public void dijkstra_GetMinDistances(int sourceVertex){
int INFINITY = Integer.MAX_VALUE;
boolean[] SPT = new boolean[vertices];
// //create heapNode for all the vertices
HeapNode [] heapNodes = new HeapNode[vertices];
for (int i = 0; i <vertices ; i++) {
heapNodes[i] = new HeapNode();
heapNodes[i].vertex = i;
heapNodes[i].distance = INFINITY;
}
//decrease the distance for the first index
heapNodes[sourceVertex].distance = 0;
//add all the vertices to the MinHeap
MinHeap minHeap = new MinHeap(vertices);
for (int i = 0; i <vertices ; i++) {
minHeap.insert(heapNodes[i]);
}
//while minHeap is not empty
while(!minHeap.isEmpty()){
//extract the min
HeapNode extractedNode = minHeap.extractMin();
//extracted vertex
int extractedVertex = extractedNode.vertex;
SPT[extractedVertex] = true;
//iterate through all the adjacent vertices
LinkedList<Edge> list = adjacencylist[extractedVertex];
for (int i = 0; i <list.size() ; i++) {
Edge edge = list.get(i);
int destination = edge.destination;
//only if destination vertex is not present in SPT
if(SPT[destination]==false ) {
///check if distance needs an update or not
//means check total weight from source to vertex_V is less than
//the current distance value, if yes then update the distance
int newKey = heapNodes[extractedVertex].distance + edge.weight ;
int currentKey = heapNodes[destination].distance;
if(currentKey>newKey){
decreaseKey(minHeap, newKey, destination);
heapNodes[destination].distance = newKey;
}
}
}
}
//print SPT
printDijkstra(heapNodes, sourceVertex);
}
public void decreaseKey(MinHeap minHeap, int newKey, int vertex){
//get the index which distance's needs a decrease;
int index = minHeap.indexes[vertex];
//get the node and update its value
HeapNode node = minHeap.mH[index];
node.distance = newKey;
minHeap.bubbleUp(index);
}
public void printDijkstra(HeapNode[] resultSet, int sourceVertex){
System.out.println("Dijkstra Algorithm: (Adjacency List + Min Heap)");
for (int i = 0; i <vertices ; i++) {
System.out.println("Source Vertex: " + sourceVertex + " to vertex " + + i +
" distance: " + resultSet[i].distance);
}
}
}
static class MinHeap{
int capacity;
int currentSize;
HeapNode[] mH;
int [] indexes; //will be used to decrease the distance
public MinHeap(int capacity) {
this.capacity = capacity;
mH = new HeapNode[capacity + 1];
indexes = new int[capacity];
mH[0] = new HeapNode();
mH[0].distance = Integer.MIN_VALUE;
mH[0].vertex=1;
currentSize = 0;
}
public void display() {
for (int i = 0; i <=currentSize; i++) {
System.out.println(" " + mH[i].vertex + " distance " + mH[i].distance);
}
System.out.println("________________________");
}
public void insert(HeapNode x) {
currentSize++;
int idx = currentSize;
mH[idx] = x;
indexes[x.vertex] = idx;
bubbleUp(idx);
}
public void bubbleUp(int pos) {
int parentIdx = pos/2;
int currentIdx = pos;
while (currentIdx > 0 && mH[parentIdx].distance > mH[currentIdx].distance) {
HeapNode currentNode = mH[currentIdx];
HeapNode parentNode = mH[parentIdx];
//swap the positions
indexes[currentNode.vertex] = parentIdx;
indexes[parentNode.vertex] = currentIdx;
swap(currentIdx,parentIdx);
currentIdx = parentIdx;
parentIdx = parentIdx/2;
}
}
public HeapNode extractMin() {
HeapNode min = mH[1];
HeapNode lastNode = mH[currentSize];
// update the indexes[] and move the last node to the top
indexes[lastNode.vertex] = 1;
mH[1] = lastNode;
mH[currentSize] = null;
sinkDown(1);
currentSize;
return min;
}
public void sinkDown(int k) {
int smallest = k;
int leftChildIdx = 2 * k;
int rightChildIdx = 2 * k+1;
if (leftChildIdx < heapSize() && mH[smallest].distance > mH[leftChildIdx].distance) {
smallest = leftChildIdx;
}
if (rightChildIdx < heapSize() && mH[smallest].distance > mH[rightChildIdx].distance) {
smallest = rightChildIdx;
}
if (smallest != k) {
HeapNode smallestNode = mH[smallest];
HeapNode kNode = mH[k];
//swap the positions
indexes[smallestNode.vertex] = k;
indexes[kNode.vertex] = smallest;
swap(k, smallest);
sinkDown(smallest);
}
}
public void swap(int a, int b) {
HeapNode temp = mH[a];
mH[a] = mH[b];
mH[b] = temp;
}
public boolean isEmpty() {
return currentSize == 0;
}
public int heapSize(){
return currentSize;
}
}
public static void main(String[] args) {
int vertices = 6;
Graph graph = new Graph(vertices);
int sourceVertex = 0;
graph.addEdge(0, 1, 4);
graph.addEdge(0, 2, 3);
graph.addEdge(1, 2, 1);
graph.addEdge(1, 3, 2);
graph.addEdge(2, 3, 4);
graph.addEdge(3, 4, 2);
graph.addEdge(4, 5, 6);
graph.dijkstra_GetMinDistances(sourceVertex);
}
}


Output:

Dijkstra Algorithm: (Adjacency List + Min Heap)
Source Vertex: 0 to vertex 0 distance: 0
Source Vertex: 0 to vertex 1 distance: 4
Source Vertex: 0 to vertex 2 distance: 3
Source Vertex: 0 to vertex 3 distance: 6
Source Vertex: 0 to vertex 4 distance: 8
Source Vertex: 0 to vertex 5 distance: 14