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Dijkstra Algorithm – Part V


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See the previous installments in this series to learn more about Dijkstra Algorithm. Part I offers an overview of Dijkstra algorithm and Part II provides pseudo code of Dijkstra algorithm. Part III demonstrates a comparison with other algorithms and Part IV presents the shortest path using the Dijkstra algorithm.

Why does the Dijkstra algorithm fail for negative weights?

Dijkstra algorithm doesn’t work for graphs with negative distances. Negative distances can lead to infinite cycles that must be handled by specialized algorithms such as Bellman-Ford’s algorithm or Johnson’s algorithm.

For us who are trying to do quantitative trading, this is not a minor problem, but quite the opposite. It is a big problem if we want to use this technique for trading.

One of the applications we have of Dijkstra algorithm is currency arbitrage, where each node is a currency and each vertex addresses, telling us the exchange rate. We can therefore construct a graph for this scenario.


As we have seen, the Dijkstra algorithm finds the optimal solution to obtain the shortest path in a graph from an origin to the rest of its nodes.

This algorithm has application in countless problems that can be represented as a graph or tree.

It differs from the Kruskal algorithm and Prim algorithm in that they focus on discovering the minimum coverage tree, i.e., how to cover the entire graph efficiently, while the Dijkstra algorithm focuses on optimizing the shortest path where it is not necessary to cover all the edges of the graph, but to reach all the nodes.

Finally, we have seen that the Dijkstra algorithm has problems such as negative weights for the edges, for which the Bellman-Ford or Johnson algorithms are used.

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