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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
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



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

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