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--- courses:cs211:winter2018:journals:mccaffreyk:7 [2018/04/01 18:59] – mccaffreyk
+++ courses:cs211:winter2018:journals:mccaffreyk:7 [2018/04/03 01:37] (current) – mccaffreyk
@@ Line 17: / Line 17: @@
 from a given edge cannot exceed the amount of flow into it(conservation condition). The maximum flow problem involves
 finding a flow of maximum possible value in some flow network. To solve this, we will focus on how some node's capacities put
-bounds on flow. A "cut" of a flow network is basically just a path that some quantity of flow follows between nodes from source
+bounds on flow. A "cut" of a flow network is a set of edges which cannot be directly connected all across two distinct groups of nodes that some quantity of flow follows between nodes from source to sink. Unfortunately, Dynamic Programming does not work here. It is important to note that we can divide flow distribution along multiple cuts in our network. For this reason, we cannot appear to use a greedy algorithm either. A graph representation made after the flow has been distributed is called a residual graph. Capacity values are placed by each edge(now represent actual expected flow values) and each node can have both backward and forward edges. A backward edge is made when flow is diverted from the current path back to go on a different cut. Flow can only be diverted when the current node's capacity is exceeded. Clearly, residual graphs will often have more edges than non residual graphs. New capacities of each edge are called residual capacities. Bottleneck capacity is the lowest capacity of any node in some path. We build a function augment to manage the flows of various edges in residual graphs. For each edge in some cut, if the edge is forward, its flow is increased by the bottleneck and if it is backward its flow is decreased by the bottleneck. Finally, we can use a simple algorithm to compute the residual graph with max flow. All that this algorithm does is apply the augment function to some s-t path in the graph while an s-t path exists. We call this the Ford-Fulkerson Algorithm. This section was a little complicated at times but I found the material interesting and intuitive. This gets an 8/10.
-to sink. Unfortunately, Dynamic Programming does not work here. It is important to note that we can divide flow distribution along
-multiple cuts in our network. For this reason, we cannot appear to use a greedy algorithm either. A graph representation made
-after the flow has been distributed is called a residual graph. Capacity values are placed by each edge(now represent actual expected flow values) and each node can have both backward and forward edges. A backward edge is made when flow is diverted from the current path back to go on a different cut. Flow can only be diverted when the current node's capacity is exceeded. Clearly, residual graphs will often have more edges than non residual graphs. New capacities of each edge are called residual capacities. Bottleneck capacity is the lowest capacity of any node in some path(cut?). We build a function augment to manage the flows of various edges in residual graphs. For each edge in some cut, if the edge is forward, its flow is increased by the bottleneck and if it is backward its flow is decreased by the bottleneck. Finally, we can use a simple algorithm to compute the residual graph with max flow. All that this algorithm does is apply the augment function to some s-t path in the graph while an s-t path exists. We call this the Ford-Fulkerson Algorithm. This section was a little complicated at times but I found the material interesting and intuitive. This gets an 8/10.
 === Section 7.2: Maximum Flows and Minimum Cuts in a Network ===
@@ Line 27: / Line 24: @@
 === Section 7.5: A First Application: The Bipartite Matching Algorithm ===
+We now apply what our knowledge of networks to the bipartite question itself. That is, **What is the size of the largest matching in a some bipartite graph G?** First, we show the equivalence between maximum flow and maximum matching. We discuss a version of the network problem in which all capacities of flow are 1. Clearly, here the residual graph contains the same number of edges as the original graph. It is also obvious that only the flow from a single edge can enter any given edge in our graph since max capacity is 1 and there can be no non-integer flows. Now, we make the leap that the size of the maximum flow with these conditions applied is equal to the size of the maximum possible matching. This would not be the case if all capacities were not 1, but 1 capacity creates the geometric conditions that we need for this relationship. Since these are equivalent, we can simply use the Ford Fulkerson algorithm on our graph to find maximum matching. This requires O(mn) time. Not all bipartite graphs have perfect matchings. We look for something tangible to help us identify rather some bipartite graph G has a perfect matching or not. Luckily, this is very simple. G has a perfect matching if the size of its two sets of nodes(two sides) are equal. I rate this section at 5/10. The concepts themselves are again somewhat intuitive. However, there is so much mathematical and proof based noise here that understanding the content proved difficult for me. I had to read this section three times before I really knew what was going on and even then I failed to grasp some of the denser material.
+=== Section 7.7: Extensions to the Maximum Flow Problem ===
+The significance of the maximum flow problem is not its relation to Networks. Rather, concepts from our solution can be used to model many other types of problems and solve them in polynomial time. One such problem is circulation with demands. Here, there can be more than one sink and more than one source generating/receiving flow. Sources have "supplies" and sinks have "demands". Some flows send out more flow than they receive and others take. We note that all flows still must be integers and the capacity principle still applies. We now have a demand condition which replaces our previous conservation condition. Flow must also adhere to the value of the demand:enters +- demand must leave each node. Unlike our original application, we wish to find out if all demands can possibly be satisfied in some graph rather than maximizing flow. This is a feasibility problem. Clearly, if the demand condition (and the capacity condition) is satisfied, meeting all demands is feasible. By creating a master source and master sink, we can again reduce this down to a situation very similar to that of the maximum flow problem, a tactic also used for matching. We can prove that some graph is feasible if and only if maximum flow is equal to total demand D. It is important to note that sometimes it may be advantageous to use lower bounds in addition to upper bound capacities for this problem. This would add additional constraints to our capacity condition and function by encouraging the use of certain edges. Our same strategy applies when lower bounds are added. This section gets a 6/10. Parts were confusing and I had to read over it twice to understand it. Still, I could not grasp some of the denser proofs and explanations. Generally, I attempt to make sense of the overarching concepts first and then work back, seeing how many of the small details I can grasp.