1. CS200: Algorithm Analysis

3. ALL PAIRS SHORTEST PATHS
Problem
To find the shortest path between all vertices v ∈ V for a weighted graph G = (V , E ).

4. ALL PAIRS SHORTEST PATHS
Interested in a directed graph G = (V,E) with weight function, w:E–>R, |V| = n.
Goal of algorithm is to create an n X n matrix of shortest path distances, z(u,v). The matrix holds the shortest path between each vertex in the graph.
How could we solve the problem using one of the single source shortest-path algorithms we have looked at?

5. ALL PAIRS SHORTEST PATHS
Bellman-Ford could be run once from each vertex (that is, each vertex could be considered as a source vertex).
Runtime of this solution is _________?
which on a dense graph is _________?
Runtime of Dijkstra’s on each vertex is O(V2 logE) with fancy data structure.
Perhaps we could do better with a different technique (neg-weight edges are allowed but not used in following examples).

6. ALL PAIRS SHORTEST PATHS
Method assumes the following :
1. Adjacency matrix representation of graph where the n X n matrix W = (wij) of edge weights.
2. Algorithm produces a table of all pairs shortest paths – n X n matrix D = (dij).
Where dij = weight of shortest path from i to j.

7. ALL PAIRS SHORTEST PATHS
1. Adjacency matrix representation of graph where the n X n matrix W = (wij) of edge weights.
Initial Matrix

8. ALL PAIRS SHORTEST PATHS
The first algorithm we will look at is a Dynamic Programming algorithm that is recursive in nature.
Discussion: consider a shortest path p from vertex i to vertex j and suppose that p contains at most m edges (assume no neg-weight edges => m is finite).
In the following l replaces D.

9. ALL PAIRS SHORTEST PATHS

10. ALL PAIRS SHORTEST PATHS
If i = j then p has weight = 0 and no edges. If i ≠ j then decompose path p into
where p' now contains at most m-1 edges. By lemma 25.1, p' is a shortest path from i to k and this => z(i,j) = z(i,k) + w(k,j).

11. Recursive description of algorithm:
Let dijm = weight of shortest path from i to j that uses at most m edges.
thus dij0 = 0 if i = j
= infinity if i ≠ j
and dijm = min (dikm-1 + wkj) correct since ≤ k ≤ n and for k= j, wjj = 0.

12. The figure shows all shortest paths with ≤ m-1 edges from i to j and from i to k that could precede j on a path.
ExtendShortestPaths(D,W) {D is nXn matrix, W is edge weight}
for i = 1 to n do //n = |V|
for j = 1 to n do
D'ij = infinity {initialize table}
for k = 1 to n do
if D'ij > Dik + wkj then {relax vertices}
D'ij = min(D'ij, Dik + wkj)
return D’
Runtime ___________?
O(n3)

13. SLOW-APSP O(n4) // Compute each D(m) SLOW-APSP(W, n) //n = |V|
D(1) = W
for m = 2 to n - 1
D(m) = ExtendShortestPaths(D(m-1) ,W)
return D(n-1)
Runtime ____________?
O(n4)

14. Slow-APSP Algorithm
Idea:
Find all vertices reachable in two hops, D(2) , save the matrix, and use it to find all vertices reachable in three hops, D(3), save the matrix, and use it to find all vertices reachable in four hops, D(4), and so on until we find D(n-1) . This matrix will contain the shortest path between every pair of vertices in the graph.

16. Ignore diagonal entries for our trace, but algorithm considers them.
For row 1, col 2: consider the following paths thru 1 interm. vertex,
1-3-2, 1-4-2, 1-5-2,
For row 1, col 3: consider the following paths thru 1 interm. vertex,
1-2-3, 1-4-3, 1-5-3,
For row 1, col 4: consider the following paths thru 1 interm. vertex,
1-2-4, 1-3-4, 1-5-4,
For row 1, col 5: consider the following paths thru 1 interm. vertex,
1-2-5, 1-3-5, 1-4-5,
1,4,5
2,5,1 2,5,3
3,2,4 3,2,5
4,5,1 4,5,3
5,3,2 5,1,4

17. 1,4,5,3
2,5,1,4
3,2,5,1
4,5,3,2 NC

19. Slow-APSP Algorithm
The runtime of ESP pseudo code is O(n3) because of the 3 nested for loops.
The ESP algorithm must be executed n-1 times (Slow-APSP), - it requires n-1 passes for each d'ij to converge to z(i,j) just as in Bellman-Ford.
Idea: Start with matrix D1 , initial values for dij. After 1 pass of ExtendShortestPaths we have D2 which is fed back into ExtendShortestPaths to produce D3... and so on until we compute the shortest paths, z(i,j) = dijn-1 = Dn-1.

20. Instead of viewing the computation of shortest paths through a relaxation step (as in pseudo- code) we can perform the computation via a set of matrix multiplications.

21. Example : C = A*B, for nXn matrices
where
cij=S(aik*bkj) which requires Q(N3) ops.
1<=k<=n
We can replace + (summation) with minimum and we can replace * (multiplication) with addition, giving:
cij = min (aik+bkj)

22. Observation: Extend Shortest Paths is like matrix multiplication:
D0 = A W = B
Dn-1 = C min = +
+ = *  = 0
create C, an n X n matrix
for i = 1 to n
for j = 1 to n
cij = 0
for k = 1 to n
cij = cij + aik · bkj

23. So, we can view Extend Shortest Paths as matrix multiplication!
Why do we care?
Because our goal is to compute D(n-1) as fast as we can. We don’t need to compute
all the intermediate D(1) , D(2) , D(3) , , D(n-1) .
Why?

24. If we further substitute Cm-1 for A and W for B and Cm for C we have
Cm = Cm-1 ”*" W.
The identity matrix for this new "multiplication" is
I = 0 ∞ ∞ ∞ ∞......
∞ 0 ∞ ∞ ∞
∞ ∞ 0 ∞ ∞

25. This new multiplication with min as + and + as
This new multiplication with min as + and + as * is associative and the algebraic structure is referred to as a closed semi-ring.
Thus C1 = C0 "*" W = W, where C0 = I
C2 = C1 "*" W = W2
C3 = C2 "*" W = W3
Cn-1 = Cn-2 "*" W = Wn-1
where Cn-1 = z(i,j).

26. Runtime for this MATRIX MULTIPLICATION is also Q(n4) which is no better than Bellman-Ford.
In order to improve the performance we don't have to compute all Cm matrices since we are only interested in Cn-1. We can compute Cn-1 with log (n-1) matrix products by computing the sequence:
C1 = W
C2 = W2
C4 = W4
(log(n-1)) (log(n-1))
C = W2
since 2log(n-1) ≥ n-1 we need to perform Q(logn) squarings.

27. It is ok to overshoot Cn-1 since once the shortest path values converge
Cn-1 = Cn = Cn+1 =
The runtime for this modified algorithm is
____________?
We can do better – Floyd Warshall algorithm next lecture.
O(n3lgn)