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There is an approach given in this article Shortest Path in Directed Acyclic Graph to find the shortest path in O(V+E) using topological sort.

I have another approach which I think is more efficient. If use dynamic programming to store the minimum distance from a vertex to a destination than I don't need to explore that node again. Now, time complexity of this solution would be O(V+E) if I am using top to bottom approach (bottom to top approach would be O(V^2).

The advantage of this method is I don't have to find topological order first to find the minimum distance, I can find it directly.

The only disadvantage I can see of this method is, If the depth of my recursion tree is large, then I can run out of stack space and I have to use bottom to top approach. Now, if I have a sparse graph (E=O(V)), time complexity using this method would be O(V^2) but using topological sort method would be O(V) and I can use kahn's algorithm to find topological sort which is done iteratively so running out of stack space wouldn't be an issue.

Is there anything wrong in whatever I have written above?

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If your algorithm runs in $O(V+E)$ time, and their algorithm runs in $O(V+E)$ time, I don't see any basis for calling your approach more efficient. At least, asymptotic running time does not seem precise enough to determine which will be more efficient in practice.

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No, the time complexity using top to bottom approach would also be $O(V^2)$. So the topological sort method is more efficient as it can find the shortest path in $O(V+E)$.

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