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Dijkstra’s – Shortest Path Algorithm (SPT) – Adjacency List and Priority Queue – Java Implementation
Earlier we have seen what Dijkstra’s algorithm is and how it works. In this article, we will see its implementation using the adjacency list and Priority Queue.
brief: What is Dijkstra’s algorithm?
 Dijkstra algorithm is a greedy algorithm.
 It finds a shortestpath 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 shortestpath tree (SPT) from the given source.
Prerequisites:
Example:
Implementation – Adjacency List and Priority Queue
Complete Algorithm:
 Create priority queue of size = no of vertices.
 Will create pair object for each vertex with two information’s, vertex and distance. (similar to heap node)
 Override the Comparator of priority queue to sort them based on the key
 Use SPT[] to keep track of the vertices which are currently in Shortest Path Tree(SPT).
 Create distance [] to keep track of distance for each vertex from the source. , initialize all distances as MAX_VAL except the first vertex for which distance will 0. (Start from first vertex).
 Create a pair object for vertex 0 with distance 0 and insert into priority queue.
 while priority queue is not empty
 Extract the min node from the priority queue, say it vertex u and add it to the SPT.
 For adjacent vertex v, if v is not in SPT[] and distance[v] > distance[u] + edge uv weight then update distance[v] = distance[u] + edge uv weight and add it to the priority queue.
Time Complexity:
Total vertices: V, Total Edges: E
 O(logV) – to extract each vertex from queue. So for V vertices – O(VlogV)
 O(logV) – each time new pair object with new key value of a vertex and will be done 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
Code:
Output:
Dijkstra Algorithm: (Adjacency List + Priority Queue) 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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