1. More Graphs
Lecture 19
CS2110 – Fall 2009

2. Representations of Graphs1 2 4 3
Adjacency List
Adjacency Matrix 1 2 3 4 2 3 4 1

3. Adjacency Matrix or Adjacency List?
n = number of vertices
m = number of edges
d(u) = outdegree of u
Adjacency Matrix
Uses space O(n2)
Can iterate over all edges in time O(n2)
Can answer “Is there an edge from u to v?” in O(1) time
Better for dense graphs (lots of edges)
Adjacency List
Uses space O(m+n)
Can iterate over all edges in time O(m+n)
Can answer “Is there an edge from u to v?” in O(d(u)) time
Better for sparse graphs (fewer edges)

4. Shortest Paths in Graphs
Finding the shortest (min-cost) path in a graph is a problem that occurs often
Find the shortest route between Ithaca and West Lafayette, IN
Result depends on our notion of cost
Least mileage
Least time
Cheapest
Least boring
All of these “costs” can be represented as edge weights
How do we find a shortest path?

5. Dijkstra’s Algorithm
dijkstra(s) { // Note: c(s,t) = cost of the s,t edge if present // Integer.MAX_VALUE otherwise D[s] = 0; D[t] = c(s,t), t ≠ s; mark s; while (some vertices are unmarked) { v = unmarked node with smallest D; mark v; for (each w adjacent to v) { D[w] = min(D[w], D[v] + c(v,w)); }

6. Dijkstra’s Algorithm 1 2 3 4
2.4
0.9
1.5
3.1
0.1 X
2.4
1.5 ∞

7. Dijkstra’s Algorithm X
2.4
2.4 1 2
1.5
0.9
0.1 4 3
3.1
1.5 ∞

8. Dijkstra’s Algorithm X
1.6
2.4 1 2
1.5
0.9
0.1 4 3
3.1
1.5
4.6

9. Dijkstra’s Algorithm X
1.6
2.4 1 2
1.5
0.9
0.1 4 3
3.1
1.5
4.6

10. Dijkstra’s Algorithm X
1.6
2.4 1 2
1.5
0.9
0.1 4 3
3.1
1.5
4.6

11. Dijkstra’s Algorithm X
1.6
2.4 1 2
1.5
0.9
0.1 4 3
3.1
1.5
2.5

12. Dijkstra’s Algorithm X
1.6
2.4 1 2
1.5
0.9
0.1 4 3
3.1
1.5
2.5

13. Dijkstra’s Algorithm X
1.6
2.4 1 2
1.5
0.9
0.1 4 3
3.1
1.5
2.5

14. Proof of Correctness The following are invariants of the loop:
X is the set of marked nodes
For u ∈ X, D(u) = d(s,u)
For u ∈ X and v ∉ X, d(s,u) ≤ d(s,v)
For all u, D(u) is the length of the shortest path from s to u such that all nodes on the path (except possibly u) are in X
Implementation:
Use a priority queue for the nodes not yet taken – priority is D(u)

15. Shortest Paths for Unweighted Graphs – A Special Case
Use breadth-first search
Time is O(n + m) in adj list representation, O(n2) in adj matrix representation S B A C D E F

16. Undirected Trees
An undirected graph is a tree if there is exactly one simple path between any pair of vertices

17. Facts About Trees |E| = |V| – 1 connected no cycles
In fact, any two of these properties imply the third, and imply that the graph is a tree

18. Spanning Trees
A spanning tree of a connected undirected graph (V,E) is a subgraph (V,E') that is a tree

19. Spanning Trees
A spanning tree of a connected undirected graph (V,E) is a subgraph (V,E') that is a tree
Same set of vertices V
E' ⊆ E
(V,E') is a tree

20. Finding a Spanning Tree
A subtractive method
Start with the whole graph – it is connected
If there is a cycle, pick an edge on the cycle, throw it out – the graph is still connected (why?)
Repeat until no more cycles

21. Finding a Spanning Tree
A subtractive method
Start with the whole graph – it is connected
If there is a cycle, pick an edge on the cycle, throw it out – the graph is still connected (why?)
Repeat until no more cycles

22. Finding a Spanning Tree
A subtractive method
Start with the whole graph – it is connected
If there is a cycle, pick an edge on the cycle, throw it out – the graph is still connected (why?)
Repeat until no more cycles

23. Finding a Spanning Tree
An additive method
Start with no edges – there are no cycles
If more than one connected component, insert an edge between them – still no cycles (why?)
Repeat until only one component

24. Finding a Spanning Tree
An additive method
Start with no edges – there are no cycles
If more than one connected component, insert an edge between them – still no cycles (why?)
Repeat until only one component

25. Finding a Spanning Tree
An additive method
Start with no edges – there are no cycles
If more than one connected component, insert an edge between them – still no cycles (why?)
Repeat until only one component

26. Finding a Spanning Tree
An additive method
Start with no edges – there are no cycles
If more than one connected component, insert an edge between them – still no cycles (why?)
Repeat until only one component

27. Finding a Spanning Tree
An additive method
Start with no edges – there are no cycles
If more than one connected component, insert an edge between them – still no cycles (why?)
Repeat until only one component

28. Finding a Spanning Tree
An additive method
Start with no edges – there are no cycles
If more than one connected component, insert an edge between them – still no cycles (why?)
Repeat until only one component

29. Minimum Spanning Trees
Suppose edges are weighted, and we want a spanning tree of minimum cost (sum of edge weights)
Some graphs have exactly one minimum spanning tree. Others have multiple trees with the same cost, any of which is a minimum spanning tree

30. Minimum Spanning Trees
Suppose edges are weighted, and we want a spanning tree of minimum cost (sum of edge weights) 1 14 2 3 72
Useful in network routing & other applications
For example, to stream a video
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

31. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 3 72
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

32. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 3 72
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

33. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 3 72
100 7 33 34 9 28 64 22 24 54 4 15 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

34. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 3 72
100 7 33 34 9 28 64 22 24 54 4 15 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

35. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 3 72 7 33 34 9 28 64 22 24 54 4 15 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

36. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 3 7 9 22 24 54 4 15 21 11 6 13 12 8 16 10 25 5

37. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 3 7 9 54 4 15 11 6 13 12 8 16 10 25 5

38. 3 Greedy Algorithms
A. Find a max weight edge – if it is on a cycle, throw it out, otherwise keep it 1 14 2 7 9 54 4 11 6 12 8 16 10 25 5

39. 3 Greedy Algorithms
B. Find a min weight edge – if it forms a cycle with edges already taken, throw it out, otherwise keep it 1 14 2 3 72
Kruskal's algorithm
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

40. 3 Greedy Algorithms
B. Find a min weight edge – if it forms a cycle with edges already taken, throw it out, otherwise keep it 1 14 2 3 72
Kruskal's algorithm
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

41. 3 Greedy Algorithms
B. Find a min weight edge – if it forms a cycle with edges already taken, throw it out, otherwise keep it 1 14 2 3 72
Kruskal's algorithm
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

42. 3 Greedy Algorithms
B. Find a min weight edge – if it forms a cycle with edges already taken, throw it out, otherwise keep it 1 14 2 3 72
Kruskal's algorithm
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

43. 3 Greedy Algorithms
B. Find a min weight edge – if it forms a cycle with edges already taken, throw it out, otherwise keep it 1 14 2 3 72
Kruskal's algorithm
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

44. 3 Greedy Algorithms
B. Find a min weight edge – if it forms a cycle with edges already taken, throw it out, otherwise keep it 1 14 2 3 72
Kruskal's algorithm
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

45. 3 Greedy Algorithms
B. Find a min weight edge – if it forms a cycle with edges already taken, throw it out, otherwise keep it 1 14 2 3 72
Kruskal's algorithm
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

46. 3 Greedy Algorithms
C. Start with any vertex, add min weight edge extending that connected component that does not form a cycle 1 14 2 3 72
Prim's algorithm (reminiscent of Dijkstra's algorithm)
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

47. 3 Greedy Algorithms
C. Start with any vertex, add min weight edge extending that connected component that does not form a cycle 1 14 2 3 72
Prim's algorithm (reminiscent of Dijkstra's algorithm)
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

48. 3 Greedy Algorithms
C. Start with any vertex, add min weight edge extending that connected component that does not form a cycle 1 14 2 3 72
Prim's algorithm (reminiscent of Dijkstra's algorithm)
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

49. 3 Greedy Algorithms
C. Start with any vertex, add min weight edge extending that connected component that does not form a cycle 1 14 2 3 72
Prim's algorithm (reminiscent of Dijkstra's algorithm)
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

50. 3 Greedy Algorithms
C. Start with any vertex, add min weight edge extending that connected component that does not form a cycle 1 14 2 3 72
Prim's algorithm (reminiscent of Dijkstra's algorithm)
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

51. 3 Greedy Algorithms
C. Start with any vertex, add min weight edge extending that connected component that does not form a cycle 1 14 2 3 72
Prim's algorithm (reminiscent of Dijkstra's algorithm)
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

52. 3 Greedy Algorithms
C. Start with any vertex, add min weight edge extending that connected component that does not form a cycle 1 14 2 3 72
Prim's algorithm (reminiscent of Dijkstra's algorithm)
100 7 33 34 9 28 64 22 24 54 4 15
101 21 11 49 51 6 13 62 32 12 8 16 27 10 25 5 40 66

53. 3 Greedy Algorithms
When edge weights are all distinct, or if there is exactly one minimum spanning tree, the 3 algorithms all find the identical tree 1 14 2 7 9 54 4 11 6 12 8 16 10 25 5

54. Prim’s Algorithm O(n2) for adj matrix O(m + n log n) for adj list
D[s] = 0; mark s; //start vertex
while (some vertices are unmarked) {
v = unmarked vertex with smallest D;
mark v;
for (each w adj to v) {
D[w] = min(D[w], c(v,w)); }
O(n2) for adj matrix
While-loop is executed n times
For-loop takes O(n) time
O(m + n log n) for adj list
Use a PQ
Regular PQ produces time O(n + m log m)
Can improve to O(m + n log n) using a fancier heap

55. Greedy Algorithms These are examples of Greedy Algorithms
The Greedy Strategy is an algorithm design technique
Like Divide & Conquer
Greedy algorithms are used to solve optimization problems
The goal is to find the best solution
Works when the problem has the greedy-choice property
A global optimum can be reached by making locally optimum choices
Example: the Change Making Problem: Given an amount of money, find the smallest number of coins to make that amount
Solution: Use a Greedy Algorithm
Give as many large coins as you can
This greedy strategy produces the optimum number of coins for the US coin system
Different money system ⇒ greedy strategy may fail
Example: old UK system

56. Similar Code Structures
Breadth-first-search (bfs)
best: next in queue
update: D[w] = D[v]+1
Dijkstra’s algorithm
best: next in PQ
update: D[w] = min(D[w], D[v]+c(v,w))
Prim’s algorithm
update: D[w] = min(D[w], c(v,w))
while (some vertices are unmarked) { v = best of unmarked vertices; mark v; for (each w adj to v) update w; }