The shortest path is a path between two vertices so that the sum of the weights of the edges that constitute the path is minimized. The shortest path problem has many applications in the real world, from finding directions on mapping applications to minimizing the moves to solve a puzzle.
In this section, we shall look at two different strategies for computing shortest paths: one that finds the shortest paths from a single source to every other vertex in the graph, and another that finds shortest paths between all pairs of vertices in a graph.
When we explored BFS, we saw that it was able to solve the shortest path problem for unweighted graphs, or graphs whose edges have the same (positive) weight. What if we are dealing with weighted graphs? Can we do better? We shall see that Dijkstra's algorithm provides an improvement over the ideas presented in BFS and that it is an efficient algorithm for solving the single...