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Algorithm to find shortest lightest path in a graph from source
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pathgraphshortestsourcealgorithmfindfromlightest
Problem
Given a directed graph $ G = (V,E)$ with non-negative(zero and positive) weights on the edges, and a vertex $ s \in V $
Problem: Find the lightest path from $s $ to each and every vertex $v \in V$ and that's from the shortest paths from $ s$ to all $v \in V$
Length of a path is the number of edges in the path.
Weight of a path is the sum of all weights of the path's edges
-------- Edit: Elaborating the question: --------
Suppose there are $x$ different paths between $s$ and some $v \in V$.
Each path has its own weight.
Among those $x$ paths, there are $y$ shortest paths(pay attention $ y \subseteq x$ and all the paths in $y$ have the same length).
What I'm trying to find: The lightest path among the above $y$ paths. Also, i want to calculate that weight for every vertex in $V$.
Initially, i thought about this algorithm:
However, i ran into this problem:
With source vertex $A$, the red path and the black path, both lead to the same vertex $D$.
Pay attention how $ A\rightarrow B\rightarrow D$ and $A \rightarrow C \rightarrow D$ are both of length 2
Although they are both the shortest paths from $A$ to $D$, the red path is lighter than the black one.
I thought about 'tweaking' BFS:
Each time i arrive at a vertex $v$ which i already discovered, i check whether the "new" path i already walked to $v$ is lighter than the one already set to $v$.
However, i don't want to change in the algorithm, because i need to prove that it actually work from the start.
How can i modify my algorithm so that the problem i pointed won't pop-out?
Problem: Find the lightest path from $s $ to each and every vertex $v \in V$ and that's from the shortest paths from $ s$ to all $v \in V$
Length of a path is the number of edges in the path.
Weight of a path is the sum of all weights of the path's edges
-------- Edit: Elaborating the question: --------
Suppose there are $x$ different paths between $s$ and some $v \in V$.
Each path has its own weight.
Among those $x$ paths, there are $y$ shortest paths(pay attention $ y \subseteq x$ and all the paths in $y$ have the same length).
What I'm trying to find: The lightest path among the above $y$ paths. Also, i want to calculate that weight for every vertex in $V$.
Initially, i thought about this algorithm:
- run BFS on $G$ from source $s$
- run Dijkestra on the BFS tree from
step 1
However, i ran into this problem:
With source vertex $A$, the red path and the black path, both lead to the same vertex $D$.
Pay attention how $ A\rightarrow B\rightarrow D$ and $A \rightarrow C \rightarrow D$ are both of length 2
Although they are both the shortest paths from $A$ to $D$, the red path is lighter than the black one.
I thought about 'tweaking' BFS:
Each time i arrive at a vertex $v$ which i already discovered, i check whether the "new" path i already walked to $v$ is lighter than the one already set to $v$.
However, i don't want to change in the algorithm, because i need to prove that it actually work from the start.
How can i modify my algorithm so that the problem i pointed won't pop-out?
Solution
The whole point about Dijkestra is that it finds the best path on it's own. You don't need BFS. In fact, Dijkestra is sort of a BFS.
Use tuples as weigths. Then your question becomes simply "find the lightest path".
How it works on your graph:
Start with a lists of undiscovered vertices and discovered ones. I labeled the unlabeled vertices
Find the best one according to your objective function. In your case: smaller length, on equal length smaller edgeweight. Add it to the discovered vertices.
Iteration 1
Iteration 2
Iteration 3
Iteration 4
Iteration 5
Use tuples as weigths. Then your question becomes simply "find the lightest path".
How it works on your graph:
Start with a lists of undiscovered vertices and discovered ones. I labeled the unlabeled vertices
E and F. I use tuples length, edgeweigth as weigth in terms of Dijkestra.undiscovered
[B, C, D, E, F]
discovered (length, edgeweigth)
A (0, 0)
Enumerate all paths from discovered vertices to undiscovered ones along with the resulting weigth
A -(1)-> B (1, 1)
A -(1)-> C (1, 1)
A -(0)-> E (1, 0) <Find the best one according to your objective function. In your case: smaller length, on equal length smaller edgeweight. Add it to the discovered vertices.
Iteration 1
undiscovered
[B, C, D, F]
discovered (length, edgeweigth)
A (0, 0)
E (1, 0)
Paths
A -(1)-> B (1, 1) C (1, 1)Iteration 2
undiscovered
[C, D, F]
discovered (length, edgeweigth)
A (0, 0)
B (1, 1)
E (1, 0)
Paths
A -(1)-> C (1, 1) D (2, 11)Iteration 3
undiscovered
[D, F]
discovered (length, edgeweigth)
A (0, 0)
B (1, 1)
C (1, 1)
E (1, 0)
Paths
C -(1)-> D (2, 2)
C -(0)-> F (2, 1) D (2, 11)Iteration 4
undiscovered
[D]
discovered (length, edgeweigth)
A (0, 0)
B (1, 1)
C (1, 1)
E (1, 0)
F (2, 1)
Paths
C -(1)-> D (2, 2) D (2, 11)Iteration 5
undiscovered
[]
discovered (length, edgeweigth)
A (0, 0)
B (1, 1)
C (1, 1)
D (2, 2)
E (1, 0)
F (2, 1)
No more undiscovered vertices -> we're done.Code Snippets
undiscovered
[B, C, D, E, F]
discovered (length, edgeweigth)
A (0, 0)
Enumerate all paths from discovered vertices to undiscovered ones along with the resulting weigth
A -(1)-> B (1, 1)
A -(1)-> C (1, 1)
A -(0)-> E (1, 0) <undiscovered
[B, C, D, F]
discovered (length, edgeweigth)
A (0, 0)
E (1, 0)
Paths
A -(1)-> B (1, 1) <
A -(1)-> C (1, 1)undiscovered
[C, D, F]
discovered (length, edgeweigth)
A (0, 0)
B (1, 1)
E (1, 0)
Paths
A -(1)-> C (1, 1) <
B -(10)-> D (2, 11)undiscovered
[D, F]
discovered (length, edgeweigth)
A (0, 0)
B (1, 1)
C (1, 1)
E (1, 0)
Paths
C -(1)-> D (2, 2)
C -(0)-> F (2, 1) <
B -(10)-> D (2, 11)undiscovered
[D]
discovered (length, edgeweigth)
A (0, 0)
B (1, 1)
C (1, 1)
E (1, 0)
F (2, 1)
Paths
C -(1)-> D (2, 2) <
B -(10)-> D (2, 11)Context
StackExchange Computer Science Q#51721, answer score: 3
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