Implement Dijkstra's Algorithm for Shortest Path #89
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marcosfede
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Aug 26, 2024
graph/dijkstra.py
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| graph = { | ||
| 0: [(1, 4), (2, 1)], | ||
| 1: [(3, 1)], | ||
| 2: [(1, 2), (3, 5)], | ||
| 3: [(4, 3)], | ||
| 4: [] | ||
| } |
marcosfede
reviewed
Aug 26, 2024
| start (int): The starting node. | ||
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| Returns: | ||
| Dict[int, int]: A dictionary with nodes as keys and shortest distances from start as values. |
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| complex_graph = { | ||
| 0: [(1, 4), (2, 2)], | ||
| 1: [(2, 1), (3, 5)], | ||
| 2: [(3, 8), (4, 10)], | ||
| 3: [(4, 2), (5, 6)], | ||
| 4: [(5, 3)], | ||
| 5: [(6, 1)], | ||
| 6: [(4, 4), (7, 2)], | ||
| 7: [] | ||
| } |
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Implement Dijkstra's Algorithm for Shortest Path
This pull request adds an implementation of Dijkstra's algorithm to the
algorithmsrepository. Dijkstra's algorithm is a graph search algorithm that solves the single-source shortest path problem for a graph with non-negative edge weights.Changes
Added
graph/dijkstra.py:dijkstrafunction that takes a graph represented as a dictionary and a start node.__main__section.Added
graph/test_dijkstra.py:dijkstrafunction.Implementation Details
The algorithm uses a priority queue (implemented with Python's
heapqmodule) to efficiently select the next node to process. This ensures an optimal time complexity of O((V + E) log V), where V is the number of vertices and E is the number of edges in the graph.Testing
The implementation has been thoroughly tested with various graph configurations to ensure correctness and robustness. All tests are passing.
Next Steps
This implementation was created as part of a Devin run. You can find the details of the run here: https://staging.itsdev.in/devin/972023a91df94403a18c3e6f869b61a4
@federico, could you please review this implementation and provide any feedback or suggestions for improvement? Thank you!
If you have any feedback, you can leave comments in the PR and I'll address them in the app!