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same as in Dijkstra's algorithm Also note that vertices visited by the eyeball prior to its reaching the starting vertex are unreachable from the starting vertex and have no influence on the distances of any vertex We do not need a priority queue Instead, we need only to incorporate the topological sort into the shortest-path computation Thus we find that the algorithm runs in linear time and works even with negative edge weights

0.2 3.078 1.886 1.638 1.533 1.476 1.440 1.415 1.397 1.383 1.372 1.363 1.356 1.350 1.345 1.341 1.337 1.333 1.330 1.328 1.325 1.323 1.321 1.319 1.318 1.316 1.315 1.314 1.313 1.311 1.310 1.303 1.296 1.289 1.282

The implementation combines a topological sort calculation and a shortest-path calculationThe indegree information is stored in the scratch data member

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The implementation of the shortest-path algorithm for acyclic graphs is shown in Figure 1533 We use a queue to perform the topological sort and maintain the indegree information in the scratch data member Lines 1318 compute the indegrees, and at lines 20-25 we place any indegree 0 vertices on the queue We then repeatedly remove a vertex from the queue at line 30 Note that, if the queue is empty, the f o r loop is terminated by the test at line 28 If the loop terminates because of a cycle, this fact is reported at line 50 Otherwise, the loop at line 31 steps through the adjacency list and a value of w is obtained at line 34 Immediately we lower w's indegree at line 36 and, if it has fallen to 0, we place it on the queue at line 37 Recall that if the current vertex v appears prior to S in topological order, v must be unreachable from S Consequently, it still has D,, = co and thus cannot hope to provide a path to any adjacent vertex w We perform a test at line 38, and if a path cannot be provided, we do not attempt any distance calculations Otherwise, at lines 41 to 46, we use the same calculations as in Dijkstra's algorithm to update D, if necessary

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An important use of acyclic graphs is critical-path analysis, a form of analysis used to schedule tasks associated with a project The graph shown in Figure 1534 provides an example Each vertex represents an activity that must be completed, along with the time needed to complete it The graph is thus called an activity-node graph, in which vertices represent activities and edges represent precedence relationships An edge (I), MI)indicates that activity v must be completed before activity w may begin, which implies that the graph must be acyclic We assume that any activities that do not depend (either directly or indirectly) on each other can be performed in parallel by different servers This type of graph could be (and frequently is) used to model construction projects Two important questions must be answered First, what is the earliest completion time for the project The answer, as the graph shows, is I0 time units-required along path A, C, E H Second, which activities can be delayed, and by how long, without affecting the minimum completion time For instance, delaying any of A, C, F: or H would push the completion time past 10 time units However, activity B is less critical and can be delayed up to 2 time units without affecting the final completion time

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