1. CSC 2300 Data Structures & Algorithms April 13, 2007 Chapter 9. Graph Algorithms

2. Today Dijkstra’s Algorithm Negative Edge Costs Acyclic Graphs Critical Path

3. Recall Dijkstra’s Algorithm After v 6 is declared known and algorithm terminates:

4. Stages of Dijkstra’s Algorithm

5. Pseudocode of Dijkstra’s Algorithm Running time?

6. Running Time Running time depends on how vertices are handled. If we sequentially scan the vertices to find the minimum d v, each phase will take O(|V|) time to find the minimum. The time to find the minima is then O(|V| 2 ). The time to update d w is constant per update, and there is at most one update per edge for a total of O(|E|). Total running time is O(|E| + |V| 2 ) = O(|V| 2 ). If the graph is dense, with |E| = Θ(|V| 2 ), the algorithm is optimal. What if the graph were sparse, with |E| = Θ(|V|)?

7. Sparse Graph Recall: If we sequentially scan the vertices to find the minimum d v, each phase will take O(|V|) time to find the minimum. The time to find the minima is then O(|V| 2 ). How can we do better? Keep the distances in a priority queue. Select vertex v: deleteMin. Update w’s distance: decreaseKey. Why is it difficult to update the distances? The find operation in priority queues. Is there an alternate method? Insert w and the new value d w into the priority queue every time w’s distance changes. Thus, there may be more than one representative for each vertex. How to make sure that alternate method will work correctly?

8. Correctness Does Dijkstra’s algorithm always give the correct solution? Yes, as long as no edge has a negative cost.

9. Animation and Proof See this site for animation and proof: http://www.cs.auckland.ac.nz/software/AlgAnim/dijkstra.html http://www.cs.auckland.ac.nz/software/AlgAnim/dijkstra.html Proof is by contradiction. Let us explain how it works.

10. Variants Dijkstra’s algorithm solves which shortest path problem? One source to all other nodes. What are some possible variants? Homework problem: one destination from all other nodes. Another variant: from one given node to another. Can you come up with an example that has multiple nodes but needs just one step? Can you come up with an example that requires us to go through all the other nodes?

11. Negative Edge Costs If a graph has negative edge costs, does Dijkstra’s algorithm work? If not, can you show with an example? Proposed scheme: add a constant Δ to each edge cost, to remove all negative edges. Does this procedure work? If not, can you show with an example?

12. Negative Edge Costs What must we do to develop an algorithm that works? Allow our algorithm to change its mind. Forget about the concept of known vertices. Start by placing s on a queue. At each stage, dequeue a vertex v. Find all vertices w adjacent to v such that d w > d v + c vw. Update d w and p w, and place w on the queue if it is not already there. Do you remember p w ? What is the running time? O(|E|.|V|).

13. Pseudocode

14. Acyclic Graph If the graph is acyclic, we can improve Dijkstra’s algorithm by changing the order in which the vertices are declared known. How? Select vertices in topological order. The algorithm can be performed in one pass, since the selections and updates can take place at the same time as the topological sort. The selection works because when a vertex is selected, its distance can no longer be lowered. Why not? There is no need for a priority queue. Why not? Consider example. The running time is O(|E|+|V|).

15. Critical Path Analysis Each node represents an activity that must be performed, along with the time it takes to complete the activity. Called an activity-node graph. Possible application: construction projects. Some important questions:  What is the earliest completion time for the project?  Which activities can be delayed, and for how long, without affecting the minimum completion time?

16. Activity-node and Event-node Graphs What is the role of the dummy nodes?

17. Earliest Completion Times How to calculate the earliest completion time of the project? Find the length of the longest path from the first event to the last event. What if there are positive-cost cycles? Rules: EC 1 = 0, EC w = max (EC v + c vw ). Can you suggest an algorithm? Graph is acyclic. Can you suggest an improvement? Topological ordering of the nodes.

18. Latest Completion Times These are earliest completion times: Now, we want the latest times that each event can finish without affecting the final completion time. How to calculate the latest completion time of the events? EC Rules: EC 1 = 0, EC w = max (EC v + c vw ). What should be the rules for latest completion times? LC Rules: LC n = EC n, LC v = max (LC w – c vw ). In which order should we compute latest completion times?

19. Slack Times Slack time for each node is the amount of time that completion at a node can be delayed without delaying the overall completion. Formula: Slack vw = LC w – EC v – c vw. Some activities have zero slack. These are crtitical activities, which must finish on time. There is at least one path consisting entirely of zero-slack edges. Such a path is a critical path.