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334. Dijkstra Algorithm Implementation – TreeSet
Earlier we have seen what Dijkstra algorithm is and how it works. In this article, we will see its implementation using adjacency list and TreeSet.
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 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.
 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.
Example:
Implementation – Adjacency List and Priority Queue
Prerequisites: Dijkstra Algorithm
Complete Algorithm:
 Will create a pair object for each vertex with two information, vertex and distance. (similar to heap node)
 Override the Comparator for TreeSet and sort them based on the key
 Use SPT[] to keep track of the vertices that are currently in the Shortest Path Tree(SPT).
 Create distance [] to keep track of the distance for each vertex from the source. , initialize all distances as MAX_VAL except the first vertex for which the distance will 0. (Start from the first vertex).
 Create a pair object for vertex 0 with distance 0 and insert it into the priority queue.
 while tree set is not empty
 Extract the min node from the tree set, 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 tree set.
Total vertices: V, Total Edges : E
 O(logV) – to extract each vertex from the queue. So for V vertices – O(VlogV)
 O(logV) – each time a new pair object with a new key value of a vertex will be done 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
Output:
Dijkstra Algorithm: (Adjacency List + TreeSet) 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