Example Shortest Path Problems 8 6 2 1 1 3 3 1 16 7 5 - PDF document

Example Shortest Path Problems 8 6 2 1 1 3 3 1 16 7 5 • Directed weighted graph. 6 4 10 4 • Path length is sum of weights of edges on path. 2 4 7 7 5 3 • The vertex at which the path begins is the source vertex. 14 • The vertex at which the path ends is the destination vertex. A path from 1 to 7. Path length is 14. Example Shortest Path Problems 8 6 2 1 3 3 1 16 7 5 6 4 10 • Single source single destination. 4 • Single source all destinations. 2 4 7 5 3 • All pairs (every vertex is a source and destination). 14 Another path from 1 to 7. Path length is 11. Single Source Single Destination Greedy Shortest 1 To 7 Path 8 6 2 1 3 Possible greedy algorithm: 3 1 16 7 5 6 4 10 � Leave source vertex using cheapest/shortest edge. 4 � Leave new vertex using cheapest edge subject to the 2 4 7 constraint that a new vertex is reached. 5 3 � Continue until destination is reached. 14 Path length is 12. Not shortest path. Algorithm doesn’t work!

Greedy Single Source All Destinations Single Source All Destinations 8 6 2 1 3 Need to generate up to n (n is number of vertices) 3 1 16 paths (including path from source to itself). 7 5 6 4 10 Greedy method: 4 2 4 7 � Construct these up to n paths in order of increasing 5 3 length. 14 � Assume edge costs (lengths) are >= 0. Path Length 6 1 2 � So, no path has length < 0. 1 0 � First shortest path is from the source vertex to itself. 9 1 3 5 4 2 1 3 The length of this path is 0. 10 1 3 6 5 1 3 5 11 1 3 6 7 Greedy Single Source All Destinations Greedy Single Source All Destinations Length Path • • Each path (other than 1 0 first) is a one edge • Let d(i) (distanceFromSource(i)) be the length of 2 1 3 extension of a previous a shortest one edge extension of an already path. generated shortest path, the one edge extension 1 3 5 5 •Next shortest path is ends at vertex i. 6 1 2 the shortest one edge • The next shortest path is to an as yet unreached extension of an already vertex for which the d() value is least. 9 1 3 5 4 generated shortest path. • Let p(i) (predecessor(i)) be the vertex just before 10 1 3 6 vertex i on the shortest one edge extension to i. 11 1 3 6 7 Greedy Single Source All Destinations Greedy Single Source All Destinations 8 8 6 6 6 2 2 1 3 3 1 3 3 3 1 1 16 16 7 7 5 5 5 6 4 10 6 4 10 4 4 2 2 4 4 7 7 2 2 4 7 5 3 5 3 14 14 1 1 [1] [2] [3] [4] [5] [6] [7] [1] [2] [3] [4] [5] [6] [7] 1 3 d 0 6 2 2 16 - - 14 d 0 6 2 16 5 5 - 10 - 14 p - 1 1 1 - - 1 p - 1 1 1 - - 1 3 3

Greedy Single Source All Destinations Greedy Single Source All Destinations 8 8 6 6 2 2 1 3 1 3 3 3 1 1 16 16 7 7 5 5 6 4 10 6 4 10 4 4 2 4 4 7 7 2 4 4 7 5 3 5 3 14 14 1 1 [1] [2] [3] [4] [5] [6] [7] [1] [2] [3] [4] [5] [6] [7] 1 3 1 3 d 0 6 2 16 9 5 - 10 - 14 d 0 6 2 9 5 - 10 - 14 6 9 1 3 5 1 3 5 p - 1 1 5 1 3 - - 3 1 p - 1 1 5 3 - - 3 1 1 2 Greedy Single Source All Destinations Greedy Single Source All Destinations 8 8 6 6 2 2 1 3 1 3 3 3 1 1 16 16 7 7 5 5 6 4 10 6 4 10 4 4 2 4 7 7 2 4 7 7 5 3 5 3 14 14 1 1 3 6 [1] [2] [3] [4] [5] [6] [7] [1] [2] [3] [4] [5] [6] [7] 1 3 10 d 0 6 2 9 5 - - 14 12 d 0 6 2 9 5 - 10 - 14 11 12 1 3 5 p - 1 1 5 3 - - 3 4 1 p - 1 1 5 3 - - 3 4 1 6 1 2 1 3 5 4 Greedy Single Source All Destinations Single Source Single Destination Path Length 1 0 Terminate single source all destinations 2 1 3 greedy algorithm as soon as shortest path to 1 3 5 5 desired vertex has been generated. 1 2 6 [1] [2] [3] [4] [5] [6] [7] 9 1 3 5 4 0 6 2 9 5 - 10 - 11 12 1 4 - 1 1 5 3 - - 3 4 1 6 10 1 3 6 11 1 3 6 7

Complexity Data Structures For Dijkstra’s Algorithm • O(n) to select next destination vertex. • The greedy single source all destinations algorithm is known as Dijkstra’s algorithm. • O(out-degree) to update d() and p() values • Implement d() and p() as 1D arrays. when adjacency lists are used. • Keep a linear list L of reachable vertices to • O(n) to update d() and p() values when which shortest path is yet to be generated. adjacency matrix is used. • Select and remove vertex v in L that has smallest • Selection and update done once for each d() value. vertex to which a shortest path is found. • Update d() and p() values of vertices adjacent to • Total time is O(n 2 + e) = O(n 2 ). v. Complexity • When a min heap of d() values is used in place of the linear list L of reachable vertices, total time is O((n+e) log n), because O(n) remove min operations and O(e) change key (d() value) operations are done. • When e is O(n 2 ), using a min heap is worse than using a linear list. • When a Fibonacci heap is used, the total time is O(n log n + e).