Graphs: Dijkstra's Algorithm
Why Stack Overflow recommends it
Stack Overflow users link it as a worked example of Dijkstra's algorithm when explaining the table notation, supplying sample graph inputs, or asking follow-up questions about shortest paths.
Recommended by 0 people across 0 different months, 2010 to 2015.
What it is
A short 2008 lecture video that walks through Dijkstra's algorithm for finding least-cost paths in a weighted graph, using a worked example with a table of node costs. It is posted by the channel distanceedjohn under a Creative Commons license.
What people said
Cites Meir · 2010-02 · Stack Overflow · on “Cheapest path algorithm”
I looked up Dijkstra's algorithm here: [this video] I can see how that would let me find a single quickest path, but I'm looking to find all of the alternative cheapest paths. How can I do this?
Cites Adalee · 2015-05 · Stack Overflow · on “How to apply Dijktra algorithm on this graph?”
You apply the Dijkstra algorithm the normal way and the shortest path from a to c is really 3. However, you should read the pseudocode [LINK] for Dijkstra to understand that table. Firstly, Dijkstra returns an array (or another data structure, depending on implementation, for our purposes just say it's an array) of all shortest paths from one given vertex to all vertices in graph . In this case, if you want to use Dijkstra for determining the shortest path from a to c , you run it from a and then check the returned array for the path to c . That table just shows the whole computing of shortest paths from a . It uses similar notation that this video [this video] , just that in the video they use n
Cites samol · 2015-06 · Stack Overflow · on “Dijkstra algorithm python”
There are far simpler ways to implement Dijkstra's algorithm. You can think of it as the same as a BFS, except: Instead of a queue, you use a min-priority queue. We only considered a node 'visited', after we have found the minimum cost path to it. Please see below a python implementation with comments: The example inputs is taken from this [this video] import heapq graph = { 'A': {'B': 20, 'D': 80, 'G': 90}, 'B': {'F': 10}, 'F': {'C': 10, 'D': 40}, 'C': {'F': 50, 'D': 10, 'H': 20}, 'D': {'G': 20, 'C': 10}, 'H': {}, 'G': {'A': 20}, 'E': {'B': 50, 'G': 30} } def dijkstra(graph, source): priority_queue = [] # The cost is 0, because the distance between source to itself is 0 heapq.heappush(priority_q