1. Advanced Algorithms Piyush Kumar (Lecture 4: MaxFlow MinCut Applications) Welcome to COT5405

2. Announcements The project list is out, make sure you have a copy. Homework 1 is due NOW. Preliminary Programming assignment 1 is attached on the back of the homework solutions Project Partner / Top 5 choices Due –Before Wednesday morning (9:00am) –Send it to me by email. –Who doesn’t have a partner yet (and wants one)? –Email your choices to : ustymenk@cs.fsu.edu –Fill up the choice sheet now if you can.

3. Today Complete Preflow Push Discuss Programming assignment 1 –Preliminary Deadline: 26th Bipartite matching/Hall’s theorem Application of MaxFlow MinCut –Image segmentation –Edge Disjoint paths

4. Applications Max Flow Min Cut

5. Matching. –Input: undirected graph G = (V, E). –M  E is a matching if each node appears in at most edge in M. –Max matching: find a max cardinality matching. Bipartite Matching

6. Bipartite matching. –Input: undirected, bipartite graph G = (L  R, E). –M  E is a matching if each node appears in at most edge in M. –Max matching: find a max cardinality matching. 1 3 5 1' 3' 5' 2 4 2' 4' matching 1-2', 3-1', 4-5' RL

7. Bipartite Matching Bipartite matching. –Input: undirected, bipartite graph G = (L  R, E). –M  E is a matching if each node appears in at most edge in M. –Max matching: find a max cardinality matching. 1 3 5 1' 3' 5' 2 4 2' 4' RL max matching 1-1', 2-2', 3-3' 4-4'

8. Max flow formulation. –Create digraph G' = (L  R  {s, t}, E' ). –Direct all edges from L to R, and assign infinite (or unit) capacity. –Add source s, and unit capacity edges from s to each node in L. –Add sink t, and unit capacity edges from each node in R to t. Bipartite Matching s 1 3 5 1' 3' 5' t 2 4 2' 4' 1 1  RL G'

9. Theorem. Max cardinality matching in G = value of max flow in G'. Pf.  –Given max matching M of cardinality k. –Consider flow f that sends 1 unit along each of k paths. –f is a flow, and has cardinality k. ▪ Bipartite Matching: Proof of Correctness s 1 3 5 1' 3' 5' t 2 4 2' 4' 11  1 3 5 1' 3' 5' 2 4 2' 4' G' G

10. Theorem. Max cardinality matching in G = value of max flow in G'. Pf.  –Let f be a max flow in G' of value k. –Integrality theorem  k is integral and can assume f is 0-1. –Consider M = set of edges from L to R with f(e) = 1. each node in L and R participates in at most one edge in M |M| = k: consider cut (L  s, R  t) ▪ Bipartite Matching: Proof of Correctness 1 3 5 1' 3' 5' 2 4 2' 4' G s 1 3 5 1' 3' 5' t 2 4 2' 4' 11  G'

11. Def. A matching M  E is perfect if each node appears in exactly one edge in M. Q. When does a bipartite graph have a perfect matching? Structure of bipartite graphs with perfect matchings. –Clearly we must have |L| = |R|. –What other conditions are necessary? –What conditions are sufficient? Perfect Matching

12. Notation. Let S be a subset of nodes, and let N(S) be the set of nodes adjacent to nodes in S. Lemma. If a bipartite graph G = (L  R, E), has a perfect matching, then |N(S)|  |S| for all subsets S  L. Perfect Matching No perfect matching: S = { 2, 4, 5 } N(S) = { 2', 5' }. 1 3 5 1' 3' 5' 2 4 2' 4' L R Pf. Each node in S has to be matched to a different node in N(S).

13. Marriage Theorem. [Frobenius 1917, Hall 1935] Let G = (L  R, E) be a bipartite graph with |L| = |R|. Then, G has a perfect matching or there is exists S  L such that |N(S)| < |S|. Marriage Theorem Pf: When there is no perfect matching, Use max-flow min-cut theorem on a bipartite graph, to find the cut(A’,B’) < n. Let A = L ∩ A’ Claim 1: N(A)  A’ Claim 2: |R ∩ A’| >= N(A) Claim 3: c(A’,B’) = |L ∩ B’ | + |R ∩ A’| < n

14. Which max flow algorithm to use for bipartite matching? –Generic augmenting path: O(m val(f*) ) = O(mn). –Capacity scaling: O(m 2 log C ) = O(m 2 ). –Shortest augmenting path: O(m n 1/2 ). Non-bipartite matching. –Structure of non-bipartite graphs is more complicated, but well-understood. [Tutte-Berge, Edmonds-Galai] –Blossom algorithm: O(n 4 ). [Edmonds 1965] –Best known: O(m n 1/2 ). [Micali-Vazirani 1980] Bipartite Matching: Running Time

15. 15 7.10 Image Segmentation

16. Image segmentation

17. Image Segmentation Image segmentation. –Central problem in image processing. –Divide image into coherent regions. Ex: To recognize a hand (or face/iris/fingerprint) from the background. (And find out if the person is known to the system)

18. Image Segmentation Foreground / background segmentation. –Label each pixel in picture as belonging to foreground or background. –V = set of pixels, E = pairs of neighboring pixels. –a i  0 is likelihood pixel i in foreground. –b i  0 is likelihood pixel i in background. –p ij  0 is separation penalty for labeling one of i and j as foreground, and the other as background. Goals. –Accuracy: if a i > b i in isolation, prefer to label i in foreground. –Smoothness: if many neighbors of i are labeled foreground, we should be inclined to label i as foreground. –Find partition (A, B) that maximizes: foreground background

19. Image Segmentation Formulate as min cut problem. –Maximization. –No source or sink. –Undirected graph. Turn into minimization problem. –Maximizing is equivalent to minimizing –or alternatively

20. Image Segmentation Formulate as min cut problem. –G' = (V', E'). –Add source to correspond to foreground; add sink to correspond to background –Use two anti-parallel edges instead of undirected edge. st p ij ij ajaj G' bibi

21. Image Segmentation Consider min cut (A, B) in G'. – A = foreground. –Precisely the quantity we want to minimize. G' st ij A if i and j on different sides, p ij counted exactly once p ij bibi ajaj

22. Edge Disjoint paths

23. Disjoint path problem. Given a digraph G = (V, E) and two nodes s and t, find the max number of edge-disjoint s-t paths. Def. Two paths are edge-disjoint if they have no edge in common. Ex: communication networks. s 2 3 4 Edge Disjoint Paths 5 6 7 t

24. Disjoint path problem. Given a digraph G = (V, E) and two nodes s and t, find the max number of edge-disjoint s-t paths. Def. Two paths are edge-disjoint if they have no edge in common. Ex: communication networks. s 2 3 4 Edge Disjoint Paths 5 6 7 t

25. Max flow formulation: assign unit capacity to every edge. Theorem. Max number edge-disjoint s-t paths equals max flow value. Pf.  –Suppose there are k edge-disjoint paths P 1,..., P k. –Set f(e) = 1 if e participates in some path P i ; else set f(e) = 0. –Since paths are edge-disjoint, f is a flow of value k. ▪ Edge Disjoint Paths st 1 1 1 1 1 1 1 1 1 1 1 1 1 1

26. Max flow formulation: assign unit capacity to every edge. Theorem. Max number edge-disjoint s-t paths equals max flow value. Pf.  –Suppose max flow value is k. –Integrality theorem  there exists 0-1 flow f of value k. –Consider edge (s, u) with f(s, u) = 1. by conservation, there exists an edge (u, v) with f(u, v) = 1 continue until reach t, always choosing a new edge –Produces k (not necessarily simple) edge-disjoint paths. ▪ Edge Disjoint Paths st 1 1 1 1 1 1 1 1 1 1 1 1 1 1 can eliminate cycles to get simple paths if desired

27. Menger’s theorem In every directed graph with nodes s and t, the maximum number of edge-disjoint s- t paths is equal to the minimum number of edges whose removal separates s from t. Special case of MaxFlow MinCut theorem. We cud use this theorem instead of the MaxFlow MinCut theorem in the proof of Hall’s theorem.

28. References R.K. Ahuja, T.L. Magnanti, and J.B. Orlin. Network Flows. Prentice Hall, 1993. (Reserved in Dirac) K. Mehlhorn and S. Naeher. The LEDA Platform for Combinatorial and Geometric Computing. Cambridge University Press, 1999. 1018 pagesThe LEDA Platform for Combinatorial and Geometric Computing