# Updating distances in dynamic graphs

1) Initialize dist[] = and dist[s] = 0 where s is the source vertex. 3) Do following for every vertex u in topological order. Do following for every adjacent vertex v of u ………………if (dist[v] #define INF INT_MAX using namespace std; // Graph is represented using adjacency list.

Every node of adjacency list // contains vertex number of the vertex to which edge connects.

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Once we have topological order (or linear representation), we one by one process all vertices in topological order.

Floyd–Warshall's Algorithm is used to find the shortest paths between between all pairs of vertices in a graph, where each edge in the graph has a weight which is positive or negative.

The biggest advantage of using this algorithm is that all the shortest distances between any $$$ vertices could be calculated in $$O(V ^ 3)$$, where $$V$$ is the number of vertices in a graph.

This algorithm depends on the relaxation principle where the shortest distance for all vertices is gradually replaced by more accurate values until eventually reaching the optimum solution.

In the beginning all vertices have a distance of "Infinity", but only the distance of the source vertex = $

Once we have topological order (or linear representation), we one by one process all vertices in topological order.Floyd–Warshall's Algorithm is used to find the shortest paths between between all pairs of vertices in a graph, where each edge in the graph has a weight which is positive or negative.The biggest advantage of using this algorithm is that all the shortest distances between any $$$ vertices could be calculated in $$O(V ^ 3)$$, where $$V$$ is the number of vertices in a graph.This algorithm depends on the relaxation principle where the shortest distance for all vertices is gradually replaced by more accurate values until eventually reaching the optimum solution.In the beginning all vertices have a distance of "Infinity", but only the distance of the source vertex = $[[

Once we have topological order (or linear representation), we one by one process all vertices in topological order.

Floyd–Warshall's Algorithm is used to find the shortest paths between between all pairs of vertices in a graph, where each edge in the graph has a weight which is positive or negative.

The biggest advantage of using this algorithm is that all the shortest distances between any $$2$$ vertices could be calculated in $$O(V ^ 3)$$, where $$V$$ is the number of vertices in a graph.

This algorithm depends on the relaxation principle where the shortest distance for all vertices is gradually replaced by more accurate values until eventually reaching the optimum solution.

In the beginning all vertices have a distance of "Infinity", but only the distance of the source vertex = $$0$$, then update all the connected vertices with the new distances (source vertex distance edge weights), then apply the same concept for the new vertices with new distances and so on.

||Once we have topological order (or linear representation), we one by one process all vertices in topological order.Floyd–Warshall's Algorithm is used to find the shortest paths between between all pairs of vertices in a graph, where each edge in the graph has a weight which is positive or negative.The biggest advantage of using this algorithm is that all the shortest distances between any $$2$$ vertices could be calculated in $$O(V ^ 3)$$, where $$V$$ is the number of vertices in a graph.This algorithm depends on the relaxation principle where the shortest distance for all vertices is gradually replaced by more accurate values until eventually reaching the optimum solution.In the beginning all vertices have a distance of "Infinity", but only the distance of the source vertex = $$0$$, then update all the connected vertices with the new distances (source vertex distance edge weights), then apply the same concept for the new vertices with new distances and so on.

]]$$, then update all the connected vertices with the new distances (source vertex distance edge weights), then apply the same concept for the new vertices with new distances and so on. $$, then update all the connected vertices with the new distances (source vertex distance edge weights), then apply the same concept for the new vertices with new distances and so on.
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