Dijkstra's algorithm works slightly differently. It treats all edges the same, so that when it considers the edges leaving a vertex, it processes the adjacent vertices together, and in no particular order.For example, when Dijkstra's algorithm processes the edges leaving vertex s, it declares that y.dist = 4, t.dist = 6, and y.pred and t.pred are both s—so farr* (please see Week7#2 for images). When Dijkstra's algorithm later considers the edge (y, t), it decreases the weight of the shortest path to vertex t that it has found so far, so that t.dist goes from 6 to 5 and t.pred switches from s to y. Dijkstra's algorithm maintains a min-priority queue of vertices, with their dist values as the keys.t repeatedly extracts from the min-priority queue the vertex u with the minimum dist value of all those in the queue, and then it examines all edges leaving u. If v is adjacent to u and taking the edge (u, v) can decrease v's dist value, then we put edge (u, v) into the shortest-path tree (for now, at least), and adjust v.dist and v.pred. Let's denote the weight of edge (u, v) by w(u,v). We can encapsulate what we do for each edge in a relaxation step, with the following pseudocode (See crib sheet) In the simulation with runners, once a vertex receives dist and pred values, they cannot change, but here a vertex could receive dist and pred values in one iteration of
the while-loop, and a later iteration of the while-loop for some other vertex u could change these values. For example, after edge (y, x) is relaxed in part (c) of the figure, the value of x.dist decreases from ∞ to 13, and x.pred becomes y. The next iteration of the while-loop in (part (d)) relaxes edge (t, x), and x.dist decreases further, to 8, and x.pred becomes t. In the next iteration (part (e)), edge (z, x) is relaxed, but this time x.dist does not change, because its value, 8, is less than
z.dist + w(z,x), which equals 12.