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310. Dijkstra’s – Shortest Path Algorithm (SPT) – Adjacency List and Min Heap

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?

  1. Dijkstra algorithm is a greedy algorithm.
  2. It finds a shortest path tree for a weighted undirected graph.
  3. This means it finds the shortest paths between nodes in a graph, which may represent, for example, road networks
  4. For a given source node in the graph, the algorithm finds the shortest path between the source node and every other node.
  5. This algorithm is 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.
  6. 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 the heap. So for V vertices – O(VlogV)
  • O(logV) – each time decreases 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 overall complexity: O(VlogV) + O(ElogV) = O((E+V)logV) = O(ElogV)

See the animation below for more understanding

Complete Code:



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