1. Geography and CS Philip Chan

2. How do I get there? Navigation Which web sites can give you turn-by-turn directions?

3. Navigation [Problem understanding] Finding a route from the origin to the destination “Static” directions Mapquest, Google maps “Dynamic” on-board directions GPS navigation if the car deviates from the route, it finds a new route

4. Consider a Simpler Problem A national map with only Cities and Highways That is, ignoring smaller streets and intersections in a city small roads between cities …

5. Navigation [Problem Formulation] Given (input) Map (cities and highways) Origin city Destination city Find (output) City-by-city route between origin and destination cities

6. Graph Problem A graph has vertices and edges Cities -> vertices Highways -> edges City-by-city route -> shortest path

7. Shortest Path Problem How would you solve the shortest path problem?

8. Algorithm 1 Greedy algorithm 1. Pick the closest city 2. Go to the city 3. Repeat until the destination city is reached

9. Algorithm 1 Greedy algorithm 1. Pick the closest city 2. Go to the city 3. Repeat until the destination city is reached Does this always find the shortest path? If not, what could be a counter example?

10. Problem with Algorithm 1 What is the main problem?

11. Problem with Algorithm 1 What is the main problem? Committing to the next city too soon Any ideas for improvement?

12. Algorithm 2 “Exhaustive” algorithm Explore/generate all possible paths Not just the ones that look short Compare all possible paths

13. Greedy vs Exhaustive Greedy doesn’t guarantee the shortest path

14. Greedy vs Exhaustive Greedy doesn’t guarantee the shortest path Greedy is faster

15. Greedy vs Exhaustive Greedy doesn’t guarantee the shortest path Greedy is faster Greedy requires less memory

16. Algorithm 3 “smart” algorithm Guarantees the shortest path Faster than Exhaustive algorithm Any ideas on what we can ignore?

17. Algorithm 3 “Smart” algorithm Similar to Exhaustive explore all alternatives from each city Ignore alternative paths that are not shorter

18. Algorithm 4 “Smarter” algorithm Dijkstra’s algorithm Any ideas on additional alternatives that can be ignored?

19. Algorithm 4 “Smarter” algorithm Dijkstra’s algorithm Any ideas on additional alternatives that can be ignored? Hint: we can commit certain cities earlier Can’t have a shorter path to those cities Ignore the committed cities later

20. Algorithm 4 Keep track of alternative paths Commit to the next city when we are sure it is shortest no other paths are shorter

21. Algorithm 4 Let the current city be the origin (I) While the current city is not the destination(C) 1. Explore neighboring non-committed cities X’s of the current city if new path to X is shorter than current path to X  Update current path to X (ie, ignore the longer path) 2. Find the non-committed city that has the shortest path length 3. Commit that city 4. Update the current city to the committed city (U)

22. Algorithm 4 Why does it guarantee to find the shortest path? The shortest path to city X is committed When?

23. Algorithm 4 Why does it guarantee to find the shortest path? The shortest path to city X is committed when every path to the “non-committed” cities is longer

24. Algorithm 4 Why does it guarantee to find the shortest path? The shortest path to city X is committed when every path to the “non-committed” cities is longer  no way to get to city X with a shorter path via “non- committed” cities

25. Dijkstra’s shortest path algorithm Interesting applet to demonstrate the alg: http://www.dgp.toronto.edu/people/JamesStewart/270/9798s/Laffra/DijkstraApplet.html

26. Comparing the Four Algorithms 1. “Greedy” Commit to closest neighboring city 2. “Exhaustive” Consider all possible paths, compare all paths 3. “Smart” Consider all neighboring cities, compare old and new paths, ignore the longer one 4. “Smarter” (Dijkstra’s) Consider only non-committed neighboring cities, compare old and new paths, ignore the worse one

27. Implementation of Dijkstra’s Algorithm Keeping track of information needed by the algorithm

28. Implementation 1 Simplified problem What is the shortest distance between origin and destination? We will worry about the intermediate cities later. Consider what we need to keep track (data) how to keep track (instructions)

29. What to keep track (data)?

30. Whether a city is committed

31. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city

32. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city How to implement the data storage?

33. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city How to implement the data storage? committed[city] pathLength[city] aka shortestDistance[city]

34. How to keep track (instructions)? Sketching on whiteboard

35. Implementation 2 We would like to know the intermediate cities as well the shortest path, not just the shortest distance

36. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city What else?

37. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city What else? What do you notice for each of the intermediate city?

38. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city What else? What do you notice for each of the intermediate city? Each was committed What do you notice when we commit a city and update the shortest distance?

39. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city What else? What do you notice for each of the intermediate city? Each was committed What do you notice when we commit a city and update the shortest distance? We know the previous city

40. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city What is the previous city How to implement the data storage? committed[city] pathLength[city] aka shortestDistance[city]

41. What to keep track (data)? Whether a city is committed What is the shortest distance so far for a city What is the previous city How to implement the data storage? committed[city] pathLength[city] aka shortestDistance[city] parent[city] aka previousCity[city]

42. Algorithm 4 with data tracking Let the current city be the origin (I) While the current city is not the destination(C) 1. Explore neighboring non-committed cities X’s of the current city if new path to X is shorter than current path to X  Update current path to X (pathLength[X], parent[X]) 2. Find the non-committed city that has the shortest path length 3. Commit that city (committed[city]) 4. Update the current city to the committed city (U)

43. Storing the map How to store the distance between two cities so that, given two cities, we can find the distance quickly?

44. How to keep track (instructions)? Sketching on whiteboard

45. Storing the Map How to store the distance between two cities so that, given two cities, we can find the distance quickly?

46. Storing the Map (graph) How to store the distance between two cities so that, given two cities, we can find the distance quickly? Adjacency matrix Table (2D array) Rows and columns are cities Cells have distance

47. Number of comparisons (speed of algorithm) Comparing: Shortest distance so far and Distance of an alternative path For updating what?

48. Number of comparisons (speed of algorithm) Comparing: Shortest distance so far and Distance of an alternative path For updating what? Shortest distance so far

49. Number of comparisons (speed of algorithm) Worst –case scenario When does it occur?

50. Number of comparisons (speed of algorithm) N is the number of cities Worst –case scenario When does it occur? Every city is connected to every city Maximum numbers of neighbors to explore

51. Worst-case scenario (speed of algorithm) How many comparisons? How many non-committed neighbors from the origin (in the first round)?

52. Worst-case scenario (speed of algorithm) How many comparisons? How many non-committed neighbors from the origin (in the first round)? N – 1 comparisons How many in the second round?

53. Worst-case scenario (speed of algorithm) How many comparisons? How many non-committed neighbors from the origin (in the first round)? N – 1 comparisons How many in the second round? N – 2 comparisons... How many in total?

54. Worst-case scenario (speed of algorithm) How many comparisons? How many non-committed neighbors from the origin (in the first round)? N – 1 comparisons How many in the second round? N – 2 comparisons... How many in total? (N-1) + (N-2) + … + 1

55. Worst-case scenario (speed of algorithm) How many comparisons? How many non-committed neighbors from the origin (in the first round)? N – 1 comparisons How many in the second round? N – 2 comparisons... How many in total (N-1) + (N-2) + … + 1 (N-1)N/2 = (N 2 – N)/2

56. Shortest Path Algorithm Dijkstra’s Algorithm In terms of vertices (cities) and edges (highways) in a graph 1959 more than 50 years ago Navigation optimization Cheapest (tolls) route? Least traffic route? Many applications

57. Summary Navigation problem Turn-by-turn directions Simplified: city-by-city directions Algorithms: Greedy: might not yield shortest path Dijkstra’s: always yield shortest path Reasons for guarantee Data structures in implementation Quadratic comparisons in # of cities