👀 Views: 88 💬 Answers: 1 📅 Created: 2025-06-06

python algorithms dijkstra Python

I'm getting frustrated with I'm stuck on something that should probably be simple. I'm currently implementing Dijkstra's Algorithm to find the shortest path in a weighted graph using Python's `heapq` for priority queue functionality. While my implementation works for smaller graphs, I notice significant performance degradation with larger datasets, resulting in an O(N^2) time complexity instead of the expected O(E log V), where E is the number of edges and V is the number of vertices. Here's a simplified version of my code: ```python import heapq def dijkstra(graph, start): queue = [(0, start)] # (distance, vertex) distances = {vertex: float('infinity') for vertex in graph} distances[start] = 0 while queue: current_distance, current_vertex = heapq.heappop(queue) if current_distance > distances[current_vertex]: continue for neighbor, weight in graph[current_vertex].items(): distance = current_distance + weight if distance < distances[neighbor]: distances[neighbor] = distance heapq.heappush(queue, (distance, neighbor)) return distances ``` I suspect that the way I'm handling the priority queue might be causing the slowdown. Although I'm using `heapq` to maintain the queue, it seems that my approach may still be inefficient, especially with graphs that are dense. For example, with a graph of 10,000 nodes and approximately 100,000 edges, the algorithm takes longer than expected. I tried using a dictionary to store the distances and updating the priority queue accordingly, but it doesn't seem to improve performance significantly. Is there a more optimal way to manage the priority queue or any other best practices I might be missing? Am I perhaps misusing `heapq`, or could the issue lie in the graph representation itself? Any insights or suggestions would be greatly appreciated! Thanks in advance! This is for a microservice running on Ubuntu 22.04. Any help would be greatly appreciated!