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Chapter 25: All-Pairs Shortest-Paths

Chapter 25: All-Pairs Shortest-Paths. Some Algorithms. When no negative edges Using Dijkstra’s algorithm: O(V 3 ) Using Binary heap implementation: O(VE lg V ) Using Fibonacci heap: O( VE + V 2 log V ) When no negative cycles Floyd- Warshall [1962] : O(V 3 ) time

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Chapter 25: All-Pairs Shortest-Paths

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  1. Chapter 25: All-Pairs Shortest-Paths

  2. Some Algorithms • When no negative edges • Using Dijkstra’salgorithm: O(V3) • Using Binary heap implementation: O(VE lg V) • Using Fibonacci heap: O(VE + V2logV) • When no negative cycles • Floyd-Warshall[1962]: O(V3) time • When negativecycles • Using Bellman-Ford algorithm: O(V2 E) = O(V4 ) • Johnson [1977]: O(VE + V2log V) time based on a clever combination of Bellman-Ford and Dijkstra

  3. A dynamic programming approach 1. characterize the structure of an optimal solution, 2. recursively define the value of an optimal solution, 3. compute the value of an optimal solution in a bottom-up fashion.

  4. The structure of an optimal solution • Consider a shortest path p from vertex i to vertex j, and suppose that p contains at most m edges. Assume that there are no negative-weight cycles. Hence m ≤ n-1 is finite.

  5. The structure of an optimal solution (cont.) • If i= j, then p has weight 0 and no edge • If i≠j, we decompose p into i ~ k -> j where p’ contains at most m-1 edges. • Moreover, p’is a shortest path from i to k and δ(i,j) = δ(i,k) + wkj , where δ(i,j) dente the shortest weight path from i to j

  6. Recursive solution to the all-pairs shortest-path problem • Define: lij(m) = minimum weight of any path from i to j that contains at most m edges. 0 if i = j • lij(0) = ∞ if i ≠ j • Then lij(m) = min{ lij(m-1), min1≤k≤n {lik(m-1) + wkj}} = min1≤k≤n {lik(m-1) + wkj} (why?)

  7. Recursive solution to the all-pairs shortest-path problem • Since shortest path from i to j contains at most n-1 edges, δ(i,j) = lij(n-1) = lij(n) = lij(n+1) = … • Computing the shortest-path weight bottom up: • Compute L(1) ,L(2) ,…., L(n-1)where L(m)=(lij(m)) for all i and j • Note that L(1) = W.

  8. Example: W = 2 4 3 8 1 3 7 1 -5 -4 2 5 4 6 L(2) = L(1) x W

  9. L(3) = L(2) x W L(4)= L(3)x W

  10. EXTENDED-SHORTEST-PATHS(L, W) • Given matrices L(m-1) and W return L(m) • n <- L.row • Let L’= (l’ij) be a new n x n matrix • for i = 1 to n • for j = 1 to n • l’ij=∞ • for k = 1 to n • l’ij= min(l’ij, lik + wkj) • return L’

  11. Matrix Multiplication Cij = k=1 to naik‧bkj lij(m) = min1≤k≤n {lik(m-1) + wkj} Let l(m-1) -> a w -> b l(m) ->c min -> + + -> ‧ time complexity : O(n3)

  12. SLOW-ALL-PAIRS-SHORTEST-PATHS(W) L(1) = L(0) ‧W = W L(2) = L(1) ‧W = W2 L(3) = L(2) ‧W = W3 ‧ ‧ ‧ L(n-1) = L(n-2) ‧W = Wn-1

  13. SLOW-ALL-PAIRS-SHORTEST-PATHS(W) 1 n = W.rows 2 L(1) = W • for m = 2 to n-1 • let L(m) be a new n x n matrix • L(m) = EXTENDED-SHORTEST- PATHS( L(m-1), W ) • return L(n-1) Time complexity : O(n4)

  14. Improving the running time L(1) = W L(2) = W2 =W．W L(4) =W4 = W2．W2 . . . i.e., using repeating squaring! Time complexity: O(n3lg n)

  15. FASTER-ALL-PAIRS-SHORTEST-PATHS FASTER-ALL-PAIRS-SHORTEST-PATHS(W) 1. n =W.row 2. L(1) =W 3. m =1 4. whilem < n-1 5. let L(2m) be a new n x n matrix 6. L(2m) = Extend-Shorest-Path(L(m), L(m)) 7. m=2m 8. return L(m)

  16. The Floyd-Warshall algorithm • A different dynamic programming formulation ‧The structure of a shortest path: Let V(G)={1,2,…,n}. For any pair ofvertices i, j єV, consider all paths from i to j whose intermediatevertices are drawn from {1, 2,…,k}, andlet p be aminimum weight path among them.

  17. The structure of a shortest path • If k is not in p, then all intermediate vertices are in {1, 2,…,k-1}. • If k is an intermediate vertex of p, then p can be decomposed into i~ k ~ j where p1 is a shortest path from i to k with all the intermediate vertices in {1,2,…,k-1} and p2 is a shortest path from k to j with all the intermediate vertices in {1,2,…,k-1}.

  18. A recursive solution to the all-pairs shortest path • Let dij(k)=the weight of a shortest path from vertex i to vertex j with all intermediate vertices in the set {1,2,…,k}. dij(k) = wij if k = 0 = min(dij(k-1), dik(k-1) + dkj(k-1)) if k ≥ 1 D(n) = (dij(n)) if the final solution!

  19. FLOYD-WARSHALL(W) 1. n =W.rows 2. D(0)= W 3. fork=1to n 4. Let D(k) = (dij(k)) be a new n x n matrix 5. for i= 1 to n 6. for j=1to n 7. dij(k)=min(dij(k-1), dik(k-1)+dkj(k-1)) 8. return D(n) Complexity:O(n3)

  20. Constructing a shortest path • π(0), π(1),…, π(n) • πij(k) : is the predecessor of the vertex j on a shortest path from vertex i with all intermediate vertices in the set {1,2,…,k}. πij(0) = NIL if i=j or wij = ∞ = i if i ≠j and wij < ∞ πij(k) = πij(k-1) if dij(k-1)≤ dik(k-1) + dkj(k-1) =πkj(k-1) if dij(k-1) >dik(k-1) + dkj(k-1)

  21. Example

  22. Transitive closure of a directed graph • Given adirected graph G = (V, E) with V = {1,2,…, n} • The transitive closure of G is G*= (V, E*) where E*={(i, j)| there is a path from i to j in G}. Modify FLOYD-WARSHALL algorithm: tij(0) = 0 if i≠j and (i,j) ∉ E 1 if i=j or (i,j) єE for k ≥ 1 tij(k)= tij(k-1)(tik(k-1)tkj(k-1))

  23. TRANSITIVE-CLOSURE(G) • n=|G.V| • Let T(0) = (tij(0)) be a new n x n matrix 3 for i =1to n 4 for j=1 to n 5 ifi==j or(i, j) ∈ G.E 6tij(0)= 1 7 else tij(0)= 0 • for k =1to n • Let T(k) = (tij(k)) be a new n x n matrix • for i=1to n 11 for j=1 to n 12tij(k)= tij(k-1)(tik(k-1) tkj(k-1)) 123 return T(n) Time complexity: O(n3)

  24. Example ① ② ④ ③

  25. Some Algorithms • When no negative edges • Using Dijkstra’salgorithm: O(V3) • Using Binary heap implementation: O(VE lg V) • Using Fibonacci heap: O(VE + V2logV) • When no negative cycles • Floyd-Warshall[1962]: O(V3) time • When negativecycles • Using Bellman-Ford algorithm: O(V2 E) = O(V4 ) • Johnson [1977]: O(VE + V2log V) time based on a clever combination of Bellman-Ford and Dijkstra

  26. Johnson’s algorithm for sparse graphs • If all edge weights in G are nonnegative, we can find all shortest paths in O(V2lg V+VE)by using Dijkstra’s algorithm with Fibonacci heap • Bellman-Ford algorithm takes O(VE) • Using reweighting technique

  27. Reweighting technique • If G has negative-weighted edge, we compute a new set of nonnegative weight that allows us to use the same method. The new set of edge weight ŵ satisfies: • 1. For all pairs of vertices u, v єV, a shortest path from u to v using weight function w is also a shortest path from u to v using the weight function ŵ • 2. ∀(u,v) єE(G), ŵ(u,v) is nonnegative

  28. Lemma: (Reweighting doesn’t change shortest paths) • Given a weighted directed graph G = (V, E) with weight function w:E→R , let h:V →R be any function mapping vertices to real numbers. For each edge (u,v) єE, ŵ(u,v) = w(u,v) + h(u) – h(v) • Let P=<v0,v1,…,vk> be a path from vertex v0 to vk Then w(p) = δ(v0,vk) if and only if ŵ(p) = (v0,vk) Also, G has a negative-weight cycle using weight function w iff G has a negative weight cycle using weight function ŵ. 

  29. Proof • ŵ(p) = w(p) + h(v0) – h(vk) ŵ(p) = ŵ(vi-1 ,vi) = (w(vi-1 ,vi) + h(vi-1)-h(vi)) = w(vi-1 ,vi) + h(v0)-h(vk) = w(p) + h(v0) – h(vk)

  30. Proof • Because h(v0) and h(vk) do not depend on the path, if one path from v0 to vk is shorter than another using weight function w, then it is also shorter using ŵ. Thus, w(p) = δ(v0,vk) if and only if ŵ(p) = (v0,vk)

  31. Proof • G has a negative-weight cycle using w iff G has a negative-weight cycle using ŵ. • Consider any cycle C=<v0,v1,…,vk> with v0=vk . Then ŵ(C) = w(C) + h(v0) – h(vk) = w(C) . Question: how to setting the value of h(vi) for all i?

  32. Producing nonnegative weight by reweighting • Given a weighted directed graph G = (V, E) • We make a new graph G’= (V’,E’), V’ = V ∪ {s}, E’ = E ∪{(s,v): v є V} and w(s,v) = 0, for all v in V • Let h(v) = δ(s, v) for all v V’ • We have h(v) ≤ h(u) + w(u, v) (why?) • ŵ(u, v) = w(u, v) + h(u) – h(v) ≥ 0

  33. Example: 2 3 4 8 3 1 2 1 -5 -4 7 5 4 6

  34. 0 2 3 4 0 8 3 1 S 0 2 0 1 -5 0 7 -4 4 5 0 6

  35. 2 0 -1 3 4 0 1 3 8 -5 0 S 0 2 0 1 -5 0 7 -4 -4 0 0 6 5 4

  36. h(v) = δ(s, v) • ŵ(u, v) =w(u, v) + h(u) – h(v) 2 5 -1 0 4 1 1 3 13 0 -5 0 2 S 0 0 0 4 10 0 -4 0 0 2 4 5

  37. JOHNSON algorithm 1 Computing G’, where G’.V = G.V ∪ {s} and G’.E= G.E ∪{(s, v): vє G.V} and w(s, v) = 0. 2 if BELLMAN-FORD(G’, w, s)= FALSE 3 print “the input graph contains negative weight cycle” 4 else for each vertex v єG’.V 5 set h(v) to be the value of δ(s, v) computed by the BF algorithm 6 for each edge (u, v) єG’.E, ŵ(u, v) =w(u, v) + h(u) – h(v)

  38. JOHNSON algorithm 7 Let D = (duv) be a new n x n matrix 8 for each vertexu єG.V run DIJKSTRA (G,ŵ, u) to compute (u, v) for all v єV[G] . 10 for each vertex v єG.V 11 duv= (u, v) + h(v) – h(u) 12 return D Complexity: O(V2lgV + VE)

  39. 2 4 0 2 0/0 1 3 0 13 4 2/1 2/3 0/-4 3 2 13 1 0/0 0 2/-3 0 10 2 0 0 2/-1 0/1 0 10 0 2 5 4 2/2 0/-4 2 5 4 2 4 0/4 0 3 13 1 0/0 2/7 2 0 0 10 0 2/3 0/5 2 5 4

  40. 2 2 4 0 2/5 1 3 4 0/-1 0 1 3 13 4/8 2/1 13 2/2 2 0/-5 2 0 0 0 10 0 0 0 10 0/0 2/6 2 2/-2 0/0 2 4 5 4 5

  41. Homework • Practice at home: Exercises: 25.1-1, 25.1-3, 25.1-6 • Practice at home : 25.2-4, 25.2-6 • Exercises: 25.2-8 (Due: Jan. 4) • Practice at home : 25.3-2, 25.3-6 • Exercises: 25.3-5 (Due: Jan. 4) • Bonus: Write a program to find all pairs shortest path of a graph G. There may exist negative weight cycle on graph G.

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