1. Data Structures and Algorithms1 Data Structures and algorithms (IS ZC361) Weighted Graphs – Shortest path algorithms – MST S.P.Vimal BITS-Pilani http://discovery.bits-pilani.ac.in/discipline/csis/vimal/ Source :This presentation is composed from the presentation materials provided by the authors (GOODRICH and TAMASSIA) of text book -1 specified in the handout

2. Data Structures and Algorithms2 Topics today 1.DAGs, topological sorting 2.Weighted Graphs Dijkstra’s algorithm The Bellman-Ford algorithm Shortest paths in dags All-pairs shortest paths 3.Minimum Spanning Trees

3. Data Structures and Algorithms3 DAGS, Topological Sorting (Text book Reference 6.4.4)

4. Data Structures and Algorithms4 DAGs and Topological Ordering A directed acyclic graph (DAG) is a digraph that has no directed cycles A topological ordering of a digraph is a numbering v 1, …, v n of the vertices such that for every edge (v i, v j ), we have i  j Example: in a task scheduling digraph, a topological ordering a task sequence that satisfies the precedence constraints Theorem A digraph admits a topological ordering if and only if it is a DAG B A D C E DAG G B A D C E Topological ordering of G v1v1 v2v2 v3v3 v4v4 v5v5

5. Data Structures and Algorithms5 write c.s. program play Topological Sorting Number vertices, so that (u,v) in E implies u < v wake up eat nap study computer sci. more c.s. work out sleep dream about graphs A typical student day 1 2 3 4 5 6 7 8 9 10 11 make cookies for professors

6. Data Structures and Algorithms6 Note: This algorithm is different than the one in Goodrich-Tamassia Running time: O(n + m). How…? Algorithm for Topological Sorting Method TopologicalSort(G) H  G// Temporary copy of G n  G.numVertices() while H is not empty do Let v be a vertex with no outgoing edges Label v  n n  n - 1 Remove v from H

7. Data Structures and Algorithms7 Topological Sorting Algorithm using DFS Simulate the algorithm by using depth- first search O(n+m) time. Algorithm topologicalDFS(G, v) Input graph G and a start vertex v of G Output labeling of the vertices of G in the connected component of v setLabel(v, VISITED) for all e  G.incidentEdges(v) if getLabel(e)  UNEXPLORED w  opposite(v,e) if getLabel(w)  UNEXPLORED setLabel(e, DISCOVERY) topologicalDFS(G, w) else {e is a forward or cross edge} Label v with topological number n n  n - 1 Algorithm topologicalDFS(G) Input dag G Output topological ordering of G n  G.numVertices() for all u  G.vertices() setLabel(u, UNEXPLORED) for all e  G.edges() setLabel(e, UNEXPLORED) for all v  G.vertices() if getLabel(v)  UNEXPLORED topologicalDFS(G, v)

8. Data Structures and Algorithms8 Topological Sorting Example

9. Data Structures and Algorithms9 Topological Sorting Example 9

10. Data Structures and Algorithms10 Topological Sorting Example 8 9

11. Data Structures and Algorithms11 Topological Sorting Example 7 8 9

12. Data Structures and Algorithms12 Topological Sorting Example 7 8 6 9

13. Data Structures and Algorithms13 Topological Sorting Example 7 8 5 6 9

14. Data Structures and Algorithms14 Topological Sorting Example 7 4 8 5 6 9

15. Data Structures and Algorithms15 Topological Sorting Example 7 4 8 5 6 3 9

16. Data Structures and Algorithms16 Topological Sorting Example 2 7 4 8 5 6 3 9

17. Data Structures and Algorithms17 Topological Sorting Example 2 7 4 8 5 6 1 3 9

18. Data Structures and Algorithms18 Weighted graphs (Minimum Spanning Tree) (Text book Reference 7.3)

19. Data Structures and Algorithms19 Minimum Spanning Tree Spanning subgraph –Subgraph of a graph G containing all the vertices of G Spanning tree –Spanning subgraph that is itself a (free) tree Minimum spanning tree (MST) –Spanning tree of a weighted graph with minimum total edge weight Applications –Communications networks –Transportation networks ORD PIT ATL STL DEN DFW DCA 10 1 9 8 6 3 2 5 7 4

20. Data Structures and Algorithms20 UV Partition Property Partition Property: –Consider a partition of the vertices of G into subsets U and V –Let e be an edge of minimum weight across the partition –There is a minimum spanning tree of G containing edge e Proof: –Let T be an MST of G –If T does not contain e, consider the cycle C formed by e with T and let f be an edge of C across the partition – weight(f)  weight(e) –Thus, weight(f)  weight(e) –We obtain another MST by replacing f with e 7 4 2 8 5 7 3 9 8 e f 7 4 2 8 5 7 3 9 8 e f Replacing f with e yields another MST UV

21. Data Structures and Algorithms21 Prim-Jarnik’s Algorithm We pick an arbitrary vertex s and we grow the MST as a cloud of vertices, starting from s We store with each vertex v a label d(v) = the smallest weight of an edge connecting v to a vertex in the cloud At each step: We add to the cloud the vertex u outside the cloud with the smallest distance label We update the labels of the vertices adjacent to u

22. Data Structures and Algorithms22 Prim-Jarnik’s Algorithm (cont.) A priority queue stores the vertices outside the cloud –Key: distance –Element: vertex Locator-based methods –insert(k,e) returns a locator –replaceKey(l,k) changes the key of an item We store three labels with each vertex: –Distance –Parent edge in MST –Locator in priority queue Algorithm PrimJarnikMST(G) Q  new heap-based priority queue s  a vertex of G for all v  G.vertices() if v  s setDistance(v, 0) else setDistance(v,  ) setParent(v,  ) l  Q.insert(getDistance(v), v) setLocator(v,l) while  Q.isEmpty() u  Q.removeMin() for all e  G.incidentEdges(u) z  G.opposite(u,e) r  weight(e) if r  getDistance(z) setDistance(z,r) setParent(z,e) Q.replaceKey(getLocator(z),r)

23. Data Structures and Algorithms23 Example B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 8   B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 5  7 B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 5  7 B D C A F E 7 4 2 8 5 7 3 9 8 0 7 2 5 4 7

24. Data Structures and Algorithms24 Example (contd.) B D C A F E 7 4 2 8 5 7 3 9 8 0 3 2 5 4 7 B D C A F E 7 4 2 8 5 7 3 9 8 0 3 2 5 4 7

25. Data Structures and Algorithms25 Analysis Graph operations –Method incidentEdges is called once for each vertex Label operations –We set/get the distance, parent and locator labels of vertex z O(deg(z)) times –Setting/getting a label takes O(1) time Priority queue operations –Each vertex is inserted once into and removed once from the priority queue, where each insertion or removal takes O(log n) time –The key of a vertex w in the priority queue is modified at most deg(w) times, where each key change takes O(log n) time Prim-Jarnik’s algorithm runs in O((n  m) log n) time provided the graph is represented by the adjacency list structure –Recall that  v deg(v)  2m The running time is O(m log n) since the graph is connected

26. Data Structures and Algorithms26 Kruskal’s Algorithm A priority queue stores the edges outside the cloud Key: weight Element: edge At the end of the algorithm We are left with one cloud that encompasses the MST A tree T which is our MST Algorithm KruskalMST(G) for each vertex V in G do define a Cloud(v) of  {v} let Q be a priority queue. Insert all edges into Q using their weights as the key T   while T has fewer than n-1 edges do edge e = T.removeMin() Let u, v be the endpoints of e if Cloud(v)  Cloud(u) then Add edge e to T Merge Cloud(v) and Cloud(u) return T

27. Data Structures and Algorithms27 Data Structure for Kruskal Algortihm The algorithm maintains a forest of trees An edge is accepted it if connects distinct trees We need a data structure that maintains a partition, i.e., a collection of disjoint sets, with the operations: -find(u): return the set storing u -union(u,v): replace the sets storing u and v with their union

28. Data Structures and Algorithms28 Representation of a Partition Each set is stored in a sequence Each element has a reference back to the set –operation find(u) takes O(1) time, and returns the set of which u is a member. –in operation union(u,v), we move the elements of the smaller set to the sequence of the larger set and update their references –the time for operation union(u,v) is min(n u,n v ), where n u and n v are the sizes of the sets storing u and v Whenever an element is processed, it goes into a set of size at least double, hence each element is processed at most log n times

29. Data Structures and Algorithms29 Partition-Based Implementation A partition-based version of Kruskal’s Algorithm performs cloud merges as unions and tests as finds. Algorithm Kruskal(G): Input: A weighted graph G. Output: An MST T for G. Let P be a partition of the vertices of G, where each vertex forms a separate set. Let Q be a priority queue storing the edges of G, sorted by their weights Let T be an initially-empty tree while Q is not empty do (u,v)  Q.removeMinElement() if P.find(u) != P.find(v) then Add (u,v) to T P.union(u,v) return T Running time: O((n+m)log n)

30. Data Structures and Algorithms30 Kruskal Example JFK BOS MIA ORD LAX DFW SFO BWI PVD 867 2704 187 1258 849 144 740 1391 184 946 1090 1121 2342 1846 621 802 1464 1235 337

31. Data Structures and Algorithms31 Example

32. Data Structures and Algorithms32 Example

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41. Data Structures and Algorithms41 Example

42. Data Structures and Algorithms42 Example

43. Data Structures and Algorithms43 Example 144 740 1391 184 946 1090 1121 2342 1846 621 802 1464 1235 337

44. Data Structures and Algorithms44 Weighted graphs (Shortest Path Algorithms) (Text book Reference 6.44)

45. Data Structures and Algorithms45 Weighted Graphs In a weighted graph, each edge has an associated numerical value, called the weight of the edge Edge weights may represent, distances, costs, etc. Example: –In a flight route graph, the weight of an edge represents the distance in miles between the endpoint airports ORD PVD MIA DFW SFO LAX LGA HNL 849 802 1387 1743 1843 1099 1120 1233 337 2555 142 1205

46. Data Structures and Algorithms46 Shortest Path Problem Given a weighted graph and two vertices u and v, we want to find a path of minimum total weight between u and v. –Length of a path is the sum of the weights of its edges. Example: –Shortest path between Providence and Honolulu Applications –Internet packet routing –Flight reservations –Driving directions ORD PVD MIA DFW SFO LAX LGA HNL 849 802 1387 1743 1843 1099 1120 1233 337 2555 142 1205

47. Data Structures and Algorithms47 Shortest Path Properties Property 1: A subpath of a shortest path is itself a shortest path Property 2: There is a tree of shortest paths from a start vertex to all the other vertices Example: Tree of shortest paths from Providence ORD PVD MIA DFW SFO LAX LGA HNL 849 802 1387 1743 1843 1099 1120 1233 337 2555 142 1205

48. Data Structures and Algorithms48 Dijkstra’s Algorithm The distance of a vertex v from a vertex s is the length of a shortest path between s and v Dijkstra’s algorithm computes the distances of all the vertices from a given start vertex s Assumptions: –the graph is connected –the edges are undirected –the edge weights are nonnegative We grow a “cloud” of vertices, beginning with s and eventually covering all the vertices We store with each vertex v a label d(v) representing the distance of v from s in the subgraph consisting of the cloud and its adjacent vertices At each step –We add to the cloud the vertex u outside the cloud with the smallest distance label, d(u) –We update the labels of the vertices adjacent to u

49. Data Structures and Algorithms49 Edge Relaxation Consider an edge e  (u,z) such that –u is the vertex most recently added to the cloud –z is not in the cloud The relaxation of edge e updates distance d(z) as follows: d(z)  min{d(z),d(u)  weight(e)} d(z)  75 d(u)  50 10 z s u d(z)  60 d(u)  50 10 z s u e e

50. Data Structures and Algorithms50 Example CB A E D F 0 428  4 8 71 25 2 39 C B A E D F 0 328 511 4 8 71 25 2 39 C B A E D F 0 328 58 4 8 71 25 2 39 C B A E D F 0 327 58 4 8 71 25 2 39

51. Data Structures and Algorithms51 Example (cont.) CB A E D F 0 327 58 4 8 71 25 2 39 CB A E D F 0 327 58 4 8 71 25 2 39

52. Data Structures and Algorithms52 Dijkstra’s Algorithm A priority queue stores the vertices outside the cloud –Key: distance –Element: vertex Locator-based methods –insert(k,e) returns a locator –replaceKey(l,k) changes the key of an item We store two labels with each vertex: –Distance (d(v) label) –locator in priority queue Algorithm DijkstraDistances(G, s) Q  new heap-based priority queue for all v  G.vertices() if v  s setDistance(v, 0) else setDistance(v,  ) l  Q.insert(getDistance(v), v) setLocator(v,l) while  Q.isEmpty() u  Q.removeMin() for all e  G.incidentEdges(u) { relax edge e } z  G.opposite(u,e) r  getDistance(u)  weight(e) if r  getDistance(z) setDistance(z,r) Q.replaceKey(getLocator(z),r)

53. Data Structures and Algorithms53 Analysis Graph operations –Method incidentEdges is called once for each vertex Label operations –We set/get the distance and locator labels of vertex z O(deg(z)) times –Setting/getting a label takes O(1) time Priority queue operations –Each vertex is inserted once into and removed once from the priority queue, where each insertion or removal takes O(log n) time –The key of a vertex in the priority queue is modified at most deg(w) times, where each key change takes O(log n) time Dijkstra’s algorithm runs in O((n  m) log n) time provided the graph is represented by the adjacency list structure –Recall that  v deg(v)  2m The running time can also be expressed as O(m log n) since the graph is connected

54. Data Structures and Algorithms54 Extension Using the template method pattern, we can extend Dijkstra’s algorithm to return a tree of shortest paths from the start vertex to all other vertices We store with each vertex a third label: –parent edge in the shortest path tree In the edge relaxation step, we update the parent label Algorithm DijkstraShortestPathsTree(G, s) … for all v  G.vertices() … setParent(v,  ) … for all e  G.incidentEdges(u) { relax edge e } z  G.opposite(u,e) r  getDistance(u)  weight(e) if r  getDistance(z) setDistance(z,r) setParent(z,e) Q.replaceKey(getLocator(z),r)

55. Data Structures and Algorithms55 Bellman-Ford Algorithm Works even with negative-weight edges Must assume directed edges (for otherwise we would have negative-weight cycles) Iteration i finds all shortest paths that use i edges. Running time: O(nm). Can be extended to detect a negative-weight cycle if it exists –How? Algorithm BellmanFord(G, s) for all v  G.vertices() if v  s setDistance(v, 0) else setDistance(v,  ) for i  1 to n-1 do for each e  G.edges() { relax edge e } u  G.origin(e) z  G.opposite(u,e) r  getDistance(u)  weight(e) if r  getDistance(z) setDistance(z,r)

56. Data Structures and Algorithms56  -2 Bellman-Ford Example  0    4 8 71 -25 39  0    4 8 71 5 39 Nodes are labeled with their d(v) values -2 8 0 4  4 8 71 5 39  8 4 5 6 1 9 -25 0 1 9 4 8 71 -25 39 4

57. Data Structures and Algorithms57 DAG-based Algorithm Works even with negative- weight edges Uses topological order Doesn’t use any fancy data structures Is much faster than Dijkstra’s algorithm Running time: O(n+m). Algorithm DagDistances(G, s) for all v  G.vertices() if v  s setDistance(v, 0) else setDistance(v,  ) Perform a topological sort of the vertices for u  1 to n do {in topological order} for each e  G.outEdges(u) { relax edge e } z  G.opposite(u,e) r  getDistance(u)  weight(e) if r  getDistance(z) setDistance(z,r)

58. Data Structures and Algorithms58  -2 DAG Example  0    4 8 71 -55 -2 39  0    4 8 71 -55 39 Nodes are labeled with their d(v) values -2 8 0 4  4 8 71 -55 3 9  -24 17 -25 0 1 7 4 8 71 -55 -2 39 4 1 1 2 4 3 65 1 2 4 3 65 8 1 2 4 3 65 1 2 4 3 65 5 0 (two steps)

59. Data Structures and Algorithms59 Questions ? This presentation & the practice problems will be available in the course page. http://discovery.bits-pilani.ac.in/discipline/csis/vimal/course%2007-08%20First/DSA_Offcampus/DSA.htm