2. Minimum weight spanning trees 1 5 3 6 4 2 4 2 7 3 6

3. 1 5 3 6 4 2 4 2 7 3 6

4. 1 5 3 6 4 2 4 2 7 3 6

5. 1 5 3 6 4 2 4 2 7 3 6

6. 1 5 3 6 4 2 4 2 7 3 6

7. 1 5 3 6 4 2 4 2 7 3 6

8. Prim’s algorithm 1) S  {1} 2) add the cheapest edge {u,v} such that u  S and v  S C S  S  {v} (until S=V)

9. Minimum weight spanning trees 1) S  {1} 2) add the cheapest edge {u,v} such that u  S and v  S C S  S  {v} (until S=V) P = MST output by Prim T = optimal MST Is P = T ? assume all the edgeweights different

10. Minimum weight spanning trees P = MST output by Prim T = optimal MST P = T assuming all the edgeweights different v 1,v 2,...,v n order added to S by Prim smallest i such that the edge e  E used by Prim to connect v i+1 is not in T.

11. Minimum weight spanning trees S smallest i such that the edge e  E used by Prim to connect v i+1 is not in T. S={v 1,...,v i } look at T v i+1 f

12. Minimum weight spanning trees S smallest i such that the edge e  E used by Prim to connect v i+1 is not in T. S={v 1,...,v i } look at T+e e f w(f) > w(e) v i+1

13. Minimum weight spanning trees S smallest i such that the edge e  E used by Prim to connect v i+1 is not in T. S={v 1,...,v i } look at T+e-f e f w(f) > w(e) v i+1

14. Prim’s algorithm for i  1 to n do C[i]   C[0]  0 S  {} while S  V do j  with minimal C[j] over j  S C S  S  { j } for u neighbors of j do if w[{j,u}] < C[u] then C[u]  w[{j,u}]; P[u]  j

15. O(E+V log V) for i  1 to n do C[i]   C[0]  0 S  {} while S  V do j  with minimal C[j] over j  S C S  S  { j } for u neighbors of j do if w[{j,u}] < C[u] then C[u]  w[{j,u}]; P[u]  j Extract-Min O(log n) time Decrease-Key O(1) time (amortized) Prim’s algorithm

16. Shortest path problem 1 2 3 8 4 2 4 2 7 1 6 A B

17. 1 2 3 8 4 2 4 2 7 1 6 A B

18. Longest path problem ? 1 2 3 8 4 2 4 2 7 1 6 A B

19. path from u to v = sequence v 1,...,v n such that v 1 = u, v 2 = v, {v i,v i+1 }  E no repeated vertices tour from u to v = sequence v1,...,vn such that v1 = u, v2 = v, {vi,vi+1}  E no repeated edges walk from u to v = sequence v 1,...,v n such that v 1 = u, v 2 = v, {v i,v i+1 }  E  walk from u to v  exists path from u to v shortest path = shortest walk longest path  longest walk

20. Shortest path problem 1 2 3 8 4 2 4 2 7 1 6 A B 0

21. 1 2 3 8 4 2 4 2 7 1 6 A B 01

22. 1 2 3 8 4 2 4 2 7 1 6 A B 01 2

23. 1 2 3 8 4 2 4 2 7 1 6 A B 01 23

24. 1 2 3 8 4 2 4 2 7 1 6 A B 01 23 4

25. 1 2 3 8 4 2 4 2 7 1 6 A 01 23 4 B 6

26. Dijkstra’s algorithm for i  1 to n do T[i]   T[0]  0 S  {} while S  V do j  with minimal T[j] over j  S C S  S  { j } for u neighbors of j do if T[j]+w[{j,u}] < T[u] then T[u]  T[j]+w[{j,u}]; P[u]  j running time ?

27. Dijkstra’s algorithm (for s.p.) T[0]  0; S  {}; for i  1 to n do T[i]   while S  V do j  with minimal T[j] over j  S C S  S  { j } for u neighbors of j do if T[j]+w[{j,u}] < T[u] then T[u]  T[j]+w[{j,u}]; P[u]  j Prim’s algorithm (for MST) C[0]  0; S  {}; for i  1 to n do C[i]   while S  V do j  with minimal C[j] over j  S C S  S  { j } for u neighbors of j do if w[{j,u}] < C[u] then C[u]  w[{j,u}]; P[u]  j

28. Dijkstra’s algorithm - correctness T[0]  0; S  {}; for i  1 to n do T[i]   while S  V do j  with minimal T[j] over j  S C S  S  { j } for u neighbors of j do if T[j]+w[{j,u}] < T[u] then T[u]  T[j]+w[{j,u}]; P[u]  j Claim: After t steps we have 1. d(0,u) = T[u] for all u  S 2. T[u] = d S (0,u) for all u, where d S (0,u) is the length of shortest path from 0 to u, such that all vertices (except possibly u) of the path are in S Proof: induction on t.

29. Negative edge-weights? 2 2 3 8 4 2 4 -2 7 1 6 A B

30. Negative edge-weights? 2 2 3 8 4 2 4 -2 7 1 6 A 2 B

31. -2 -3 -8 -4 -2 -4 -2 -7 -6 A B Shortest path problem

32. -2 -3 -8 -4 -2 -4 -2 -7 -6 A B Shortest path problem 1 2 3 8 4 2 4 2 7 1 6 A B Longest path problem

33. -2 -3 -8 -4 -2 -4 -2 -7 -6 A B Shortest path problem 1 2 3 8 4 2 4 2 7 1 6 A B Longest path problem shortest path  shortest walk (if negative)

34. Negative edge weights allowed but no negative cycles shortest path  shortest walk (if negative)

35. The Bellman-Ford algorithm D[v] = estimate on the distance from 0 Initially: D[0] = 0 D[i] =  for all other vertices Relaxation of an edge: if D[u] + w(u,v) < D[v] then D[v]  D[u] + w(u,v)

36. The Bellman-Ford algorithm D[v] = estimate on the distance from 0 Initially: D[0] = 0 D[i] =  for all other vertices Repeat |V| times relax every edge e running time = O( V.E )

37. The Bellman-Ford algorithm Let S[u] be the number of edges in the shortest path from 0 to u Claim: After t steps of the algorithm S[u]  t  D[u] = d(0,u)

38. All-pairs shortest path problem Dijkstra  O( V 2 log V + V.E ) (negative edges not allowed)

39. The Floyd-Warshall algorithm dynamic programming algorithm negative edges allowed, no negative cycles d ij k = length of the shortest path from i to j which only uses vertices 1...k d ij k = min {d ij k-1,d ik k-1 + d kj k-1 }

40. The Floyd-Warshall algorithm d ij 0  w ij d ij k = min {d ij k-1,d ik k-1 + d kj k-1 } for k from 1 to n do for i from 1 to n do for j from 1 to n do for all i,j  V Time = ? Space = ?

41. The Floyd-Warshall algorithm d ij 0  w ij d ij k = min {d ij k-1,d ik k-1 + d kj k-1 } for k from 1 to n do for i from 1 to n do for j from 1 to n do for all i,j  V Time = O(n 3 ) Space = O(n 3 )

42. The Floyd-Warshall algorithm d ij  w ij d ij = min {d ij,d ik + d kj } for k from 1 to n do for i from 1 to n do for j from 1 to n do for all i,j  V Time = O(n 3 ) Space = O(n 2 )

43. Single source shortest paths Dijkstra = O(E+V log V) Bellman-Ford = O(E.V) allows negative edge-weights (but not negative cycles) All-pairs shortest paths Dijkstra = O(EV+V 2 log V) Floyd-Warshall = O(V 3 ) allows negative edge-weights (but not negative cycles) Johnson = O(EV+V 2 log V) allows negative edge-weights (but not negative cycles)

44. Johnson’s algorithm Dijkstra = O(E+V log V) Bellman-Ford = O(E.V) allows negative edge-weights (but not negative cycles) w’(u,v) = w(u,v) + h(u) – h(v) doesn’t change the shortest paths use Bellman-Ford to compute h such that w’ is non-negative

46.  M[i-1,j]  d-1 M[i,j]  d  M[i,j-1]  d-1  M[i-1,j-1]  d-1 M[i-1,j]  d-1  and M[i,j-1]  d-1 and M[i-1,j-1]  d-1 and A[i,j]=1  M[i,j]  d

47. if A[i,j] = 1 then M[i,j]  1+min(M[i-1,j],M[i,j-1],M[i-1,j-1]) else M[i,j]  0

48. If there exists a subset of size k then the k largest numbers have average at least k

49. Find(A[l..r], n, s, B) if r  l+1 then try all possibilities else m  median(A[l..r]); p  partition(A[l..r],m) s’  sum in A[p+1..r]; n’  (r-p) if (s+s’)/(n+n’) < B then Find(A[p+1..r], n, s,B) else Find(A[l..p],n+n’,s+s’,B) T(n)=T(n/2)+O(n)

51. b[k] = the largest subset of a 1,...,a k no three consecutive selected b[k-1] b[k-2] + a k b[k-3] + a k + a k-1 last not included in OPT last included in OPT, second to last not last 2 in OPT

52. b[0]  0; b[1]  a 1 ; b[2]  a 1 +a 2 ; c[0]  1; c[1]  2; c[2]  3; for k from 3 to n do b[k]  b[k-1]; c[k]  1; if a k +b[k-2] > b[k] then b[k]  a k +b[k-1]; c[k]  2; if a k +a k-1 +b[k-3] > b[k] then b[k]  a k +a k-1 +b[k-3]; c[k]  3;\ k  n while (k  0) do if c[k]=3 then print(a k,a k-1 ); k  k-3; if c[k]=2 then print(a k ); k  k-2; if c[k]=1 then k  k-1;

53. P[0...M,0..k] P[S,i] = can obtain sum S using a 1,...,a i P[0,0]  true; for i from to M do P[i,0]  false; for i from 1 to n do for S from 0 to n do P[S,i]  P[S,i-1] if a i  S and P[S-a i,i-1] then P[S,i]  true return P[M,n]

56. P[0...M,0..k] P[S,i] = k means can obtain sum S using k numbers from a 1,...,a i P[0,0]  0; for i from to M do P[i,0]   ; for i from 1 to n do for S from 0 to n do P[S,i]  P[S,i-1] if a i  S then P[S,i]  min(P[S,i-1],P[S-a i,i-1]+1) return P[M,n]

58. rev(G) H = empty graph on |V| vertices for v  V do for each out-neighbor u of v do append(H,u,v) return H sort(G) = rev(rev(G)) undirected = compare lists rev(rev(rev(G))) and rev(rev(G))

59. M[0]  0 for i from 1 to n do for k from 1 to  i 1/2  do M[i]=min(M[i],M[i-k 2 ]) Lagrange’s theorem: for all n we have M[n]  4 Legendre: if n  4 k (8m + 7)  M[n]  3

60. M[i] = sum of the largest increasing subsequence which ends with the i-th element for i from 1 to n do M[i]  A[i] for j from 0 to n do if A[j]<A[i] then M[i]  max(M[i],M[j]+A[i]) Output(n,u) if n>0 then i  index with maximum M[i] subject to A[i]<u print(A[i]); Output(i-1,A[i])