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--- courses:cs211:winter2014:journals:maggie:home [2014/03/31 17:32] – [7.2 Maximum Flows and Minimum Cuts in a Network] weatherlym
+++ courses:cs211:winter2014:journals:maggie:home [2014/04/01 23:11] (current) – [7.6 Disjoint Paths in Directed and Undirected Graphs] weatherlym
@@ Line 463: / Line 463: @@
 ====== 7.5 A First Application: The Bipartite Matching Problem ======
+  * Beginning with the graph G in an instance of the Bipartite Matching, we construct a flow network G', first we direct al edges in G from X to Y, we then add a node s and an edge (s,x) from s to each node in X.  We add a node t, and an edge (y, t) from each node in Y to t.  Finally, we give each edge in G' a capacity of 1, we now compute a maximum s-t flow in this network G'
+  * The size of the maximum matching in G is equal to the value of the maximum flow in G'; and the edges in such a matching in G are the edges that carry flow from X to Y in G'
+  * The Ford-Fulkerson Algorithm can be used to find a maximum matching in a bipartite graph in O(mn) time, where m is the number of edges
+  * We can decide if the graph G has a perfect matching by checking if the maximum flow in a related graph G' has value at least n, by the Max-Flow Min-Cut theorem there will be an s-t cut of capacity less than n if the maximum -flow value in G' has value less than n
+  * If there are nodes x1 and x2 in X that have only one incident edge each, and the other end of each edge is the same node y, then clearly the graph has no perfect matching
+  * Assume that the bipartite graph G = (V, E) has two sides X and Y st the size of X is equal to the size of Y.  Then the graph G either has a perfect matching or there is a subset A of X st the size of Gamm(A) < the size of A where Gamm(A) is a subset of Y and denotes the set of all nodes that ar adjacent to nodes in A.  A perfect matching can be found in O(mn) time
 ====== 7.6 Disjoint Paths in Directed and Undirected Graphs ======
+  * We say that a set of paths is edge-disjoint if no two paths share an edge,
+  * Given a directed graph G = (V, E) with two distinguished nodes s, t in V, the Directed Edge-Disjoint Paths Problem is to find the maximum number of edge-disjoint s-t paths in G
+  * The Undirected Edge-Disjoint Paths Problem is to find the maximum number of edge-disjoint s-t paths in an undirected graph G
+  * Both directed and the undirected versions of the problem can be solved using flows, starting with the directed problem, given the graph G = (V, E) we define a flow network in which s and t are the source and sink, and with capacity of 1 on each edge, suppose there are k edge-disjoint s-t paths, we can make each of these carry one unit of flow.  If there are k edge-disjoint paths in a directed graph G from s to t, then the value of the maximum s-t flow in G is at least k
+  * If f is a 0-1 valued flow of value v, then the set of edges with flow value f(e) = 1 contains a set of v edge-disjoint paths
+  * There are k edge-disjoint paths in a directed graph G from s to t if and only if het value of the maximum value of an s-t flow in G is at least k
+  * The Ford-Fulkerson Algorithm can be used to find a maximum set of edge-disjoint s-t paths in a directed graph G in O(mn) time
+  * In every directed graph with nodes s and t, the maximum number of edge-disjoint s-t paths is equal to the number of edges whose removal separates s from t
+  * There are k edge-disjoint paths in an undirected graph G from s to to if and only if the maximum value of an s-t flow in the directed version G' of G is at least k.  The Ford-Fulkerson Algorithm can be used to find a maximum set of disjoint s-t paths in an undirected graph G in O(mn) time
+  * In every undirected graph with nodes s and t, the maximum umber of edge-disjoint s-t paths is equal to the minimum number of edges whose removal separates s from t.