Dijkstra’s Problem with Negative Edge

46th Friday Fun Session – 12th Jan 2018

Dijkstra’s algorithm cannot work with negative edge. Also, we cannot trivially add a constant to each of the edge weights and make them non-negative to proceed further.

Why Does Dijkstra’s Algorithm not Work with Negative Edge?

negative edge

In the above figure, we are trying to get shortest paths from source node 1 to all other nodes (node 2 and node 3). Since Dijkstra’s algorithm works by employing a greedy process, it outputs 20 as the shortest path cost to node 2.

As we can see, from node 1, we can go to two nodes – node 2 and node 3, at a cost of 20 and 40 respectively. Hence, going to node 2 is cheaper. And that is why, it outputs 20 to be the cheapest cost to reach node 2.

However, we know that the cheapest cost to reach node 2 is through node 3. And the associated cost is: 40 + (-30) = 10. So Dijkstra’s algorithm gets it wrong. It gets it wrong because it cannot foresee that later, a negative edge can bring down the total cost to below 20.

If we carefully observe, we see that the wrong calculation by Dijkstra’s algorithm happens due to the negative edge. Had cost from node 3 to node 2 not been negative, it could never bring down the total cost to lower than 20, after getting added to 40.

Why Does Adding a Constant Cost to Each Edge not Work?

Now that we realize, Dijkstra’s algorithm fails due to the negative edge from node 3 to node 2, having the value -30, we might be tempted to add 30 to each of the edges. We might think, this way we can remove the negative edge. And doing so would be fair; after all, we are adding the same value to each of the edges. Let’s do it and see what happens.

adjusting negative edge.png

After updating the edge costs, the graph looks as shown above. So what is the cheapest path from node 1 to node 3 now?

Well, now the cheapest cost is 50, which uses the direct edge from node 1 to node 2. But this is not supposed to be the cheapest path, right? The cheapest path was node 1 -> node 3 -> node 2, before we adjusted the edge cost. Adjusting edge cost should not change the graph. It must not change the cheapest path, right?

So why does that happen? Well, if we observe, we find that path node 1 -> node 3 -> node 2 uses two edges/segments – node 1 to node 3 and node 3 to node 2. On the other hand, path node 1 -> node 2 uses just one edge/segment. The way we have updated the edge cost – adding a constant to each path segment – is not fair to a path using more path segments. For the path that uses two path segments, which was originally the cheapest path, we have added the constant 30 twice. On the other hand, for the path that uses just one path segment, we have added 30 only once. That way, we are unfair to the path using more path segments.

We must add a constant to each of the paths, not to each of the path segments.

Solution

Johnson’s algorithm does this – add a constant cost to each path with a certain source s to a certain target t. It does so, by finding a special set of offset values to remove the negative edges from a graph. Once that is done Dijkstra’s algorithm can work. But that works in absence of a negative cycle in the graph.

Index

Author: Gopal Das

I am working as a Principal Consultant at JLT Interactive Pte Ltd, Singapore. I have a BS in Computer Science & Engineering from Khulna University and ME in Internet Science & Engineering from Indian Institute of Science (IISc). I have a few publications on Query Optimization in RDBMS in ACM SIGMOD, IEEE ICDE etc. I was a founding team member and VP Engineering of iTwin, a spinoff from A*STAR. I am working as a software engineer for 14 years. I am interested in Algorithms, Database, Security, Machine Learning, and Blockchain among others. I am a father of two children and live in Singapore. www.linkedin.com/in/dasgopal https://github.com/gopalcdas

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