Input : Source = 0, Maximum Weight W = 14 Output : Vertex Distance from Source 0 0 1 4 2 12 3 19 4 21 5 11 6 9 7 8 8 14
Can we optimize Dijkstra’s shortest path algorithm to work better than O(E log V) if maximum weight is small (or range of edge weights is small)?
For example, in the above diagram, maximum weight is 14. Many a times the range of weights on edges in is in small range (i.e. all edge weight can be mapped to 0, 1, 2.. w where w is a small number). In that case, Dijkstra’s algorithm can be modified by using different data structure, buckets, which is called dial implementation of dijkstra’s algorithm. time complexity is O(E + WV) where W is maximum weight on any edge of graph, so we can see that, if W is small then this implementation runs much faster than traditional algorithm. Following are important observations.
- Maximum distance between any two node can be at max w(V – 1) (w is maximum edge weight and we can have at max V-1 edges between two vertices).
- In Dijkstra algorithm, distances are finalized in non-decreasing, i.e., distance of the closer (to given source) vertices is finalized before the distant vertices.
Below is complete algorithm:
- Maintains some buckets, numbered 0, 1, 2,…,wV.
- Bucket k contains all temporarily labeled nodes with distance equal to k.
- Nodes in each bucket are represented by list of vertices.
- Buckets 0, 1, 2,..wV are checked sequentially until the first non-empty bucket is found. Each node contained in the first non-empty bucket has the minimum distance label by definition.
- One by one, these nodes with minimum distance label are permanently labeled and deleted from the bucket during the scanning process.
- Thus operations involving vertex include:
- Checking if a bucket is empty
- Adding a vertex to a bucket
- Deleting a vertex from a bucket.
- The position of a temporarily labeled vertex in the buckets is updated accordingly when the distance label of a vertex changes.
- Process repeated until all vertices are permanently labeled (or distances of all vertices are finalized).
Since the maximum distance can be w(V – 1), we create wV buckets (more for simplicity of code) for implementation of algorithm which can be large if w is big.
Vertex Distance from Source 0 0 1 4 2 12 3 19 4 21 5 11 6 9 7 8 8 14
Below is step by step illustration taken from here.
This article is contributed by Utkarsh Trivedi. Please write comments if you find anything incorrect, or you want to share more information about the topic discussed above.