Complexity Analysis of Bellman-Ford Algorithm
- Time Complexity when graph is connected:
- Best Case: O(E), when distance array after 1st and 2nd relaxation are same , we can simply stop further processing
- Average Case: O(V*E)
- Worst Case: O(V*E)
- Time Complexity when graph is disconnected:
- All the cases: O(E*(V^2))
- Auxiliary Space: O(V), where V is the number of vertices in the graph.
Bellman–Ford Algorithm
Imagine you have a map with different cities connected by roads, each road having a certain distance. The Bellman–Ford algorithm is like a guide that helps you find the shortest path from one city to all other cities, even if some roads have negative lengths. It’s like a GPS for computers, useful for figuring out the quickest way to get from one point to another in a network. In this article, we’ll take a closer look at how this algorithm works and why it’s so handy in solving everyday problems.
Table of Content
- Bellman-Ford Algorithm
- The idea behind Bellman Ford Algorithm
- Principle of Relaxation of Edges for Bellman-Ford
- Why Relaxing Edges N-1 times, gives us Single Source Shortest Path?
- Why Does the Reduction of Distance in the N’th Relaxation Indicates the Existence of a Negative Cycle?
- Working of Bellman-Ford Algorithm to Detect the Negative cycle in the graph
- Algorithm to Find Negative Cycle in a Directed Weighted Graph Using Bellman-Ford
- Handling Disconnected Graphs in the Algorithm
- Complexity Analysis of Bellman-Ford Algorithm
- Bellman Ford’s Algorithm Applications
- Drawback of Bellman Ford’s Algorithm
Contact Us