1. Problem Solving with Networks 18/08/2012 Jamie Sneddon j.sneddon@auckland.ac.nz

2. Networks, graphs  A graph is collection of points with lines between them  A network is a graph with positive numbers assigned to its edges as weights pointline vertex (vertices)edge nodearc

3. Distances in miles between some US cities

4. Detroit MI Columbus OH Washington DC Pittsburgh PA Philadelphia PA New York NY Rochester NY Buffalo NY Cleveland OH Toledo OH Scranton PA Harrisburg PA Distances in miles between some US cities

5. Detroit MI Columbus OH Washington DC Pittsburgh PA Philadelphia PA New York NY Rochester NY Buffalo NY Cleveland OH Toledo OH Scranton PA Harrisburg PA Distances in miles between NE state US cities

6. Detroit MI Columbus OH Washington DC Pittsburgh PA Philadelphia PA New York NY Rochester NY Buffalo NY Cleveland OH Toledo OH Scranton PA Harrisburg PA Distances in miles between NE state US cities

7. 61 144 117 255 73 190 134 142 185 246 141 204 112 217 121 95 125 121 Distances in miles between NE state US cities 107

8. Detroit MI Columbus OH Washington DC Pittsburgh PA Philadelphia PA New York NY Rochester NY Buffalo NY Cleveland OH Toledo OH Scranton PA 61 144 117 255 73 190 134 142 185 246 141 Harrisburg PA 204 112 217 121 95 125 121 Distances in miles between NE state US cities 107

9. 61 144 117 255 73 190 134 142 185 246 141 204 112 217 121 95 125 121 Distances in miles between NE state US cities 107 T D B R H S N Ph W Pi Cl Co

10. 61 144 117 255 73 190 134 142 185 246 141 204 112 217 121 95 125 121 Spanning Tree with edge deletion 107 T D B R H S N Ph W Pi Cl Co

11. 61 144 117 255 73 190 134 142 185 246 141 204 112 217 121 95 125 121 Spanning Tree with edge inclusion (1) 107 T D B R H S N Ph W Pi Cl Co

12. 61 144 117 255 73 190 134 142 185 246 141 204 112 217 121 95 125 121 Spanning Tree with edge inclusion (2) 107 T D B R H S N Ph W Pi Cl Co Total weight = 1356 miles

13. Problems in networks  (0) Are all the nodes connected?  (1) What is the lowest weight (smallest) tree which connects all the nodes?  (2) What is the shortest distance from A to B?  (3) Can every edge be used exactly once?  (4) Can every node be visited exactly once?  (5) What is the shortest way to visit every node exactly once?

14. (0) Connectedness  It’s usually easy to tell if a real-world network is connected or not. Starting at an arbitrary node, if we list that node’s neighbours, then their neighbours, then theirs, we might eventually visit all the nodes.  [ACTIVITY]  If we don’t, it’s not c0nnected.

15. (1) Spanning Tree  A minimum weight spanning tree is a sub- graph without cycles which connects all the nodes.  A tree with n nodes has n-1 edges.  Sometimes it’s easier to add edges, sometimes remove them.

16. (2) Shortest Path  Starting at a particular node A, what is the shortest path to another vertex B (or to all other vertices).  [ACTIVITY 2]

17. (3) Traversability  Is it possible to start at a node A and follow a route through the network which uses every edge exactly once?  If so, does the path return to A, or end elsewhere?  Easily characterised with node parity (even/odd)

18. (4) Visiting nodes  Is it possible to start at a node A and follow a route through the network visits every NODE exactly once?  If so, does the path return to A, or end elsewhere?  Called Hamiltonian: no characterisation, and hard to find

19. (5) Travelling Salesman  What is the shortest Hamiltonian cycle/path?  This is harder still! TSP (Travelling Salesman Problem) is the definitive “hard” problem of computational mathematics

20. Real World / Mathematical World  Networks don’t always model the real world perfectly – what could go wrong?  One way edges  Different weight directions on edges  Variable data (time of day, day of week)  Roads branching outside towns

21. Questions beyond the procedural  Why did you use this algorithm?  Compare two algorithms  edge inclusion vs edge deletion  How could the network be changed to give (or remove) a property  What does this mean in relation to the given problem

22. How does adding/deleting nodes/edges change the shortest paths/spanning trees?

23. Where to add connections?  Where in the network should edges be added/removed so that it is traversable?

24. Optional nodes  What if we want to allow the possibility of a node outside a town?  Construct two spanning trees: with/without 4 5 5 3 2.5 2 1 5 3

25. For more: Wikipedia!  Prim’s Algorithm (connected sub-tree)  Kruskal’s Algorithm (partial trees)  Reverse-Delete Algorithm (remove edges)  Dijkstra’s Algorithm  Euler Path – Fleury’s Algorithm  Jamie Sneddon j.sneddon@auckland.ac.nz