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While the previous chapter gave you several algorithms for a single problem, this chapter describes a single algorithm with many variations and applications The core problem is that of finding maximum flow in a network, and the main solution strategy I ll be using is the augmenting path method of Ford and Fulkerson Before tackling the full problem, I ll guide you through two simpler problems, which are basically special cases (they re easily reduced to maximum flow) These problems, bipartite matching and disjoint paths, have many applications themselves and can be solved by more specialized algorithms You ll also see that the max-flow problem has a dual, the min-cut problem, which means that you ll automatically solve both problems at the same time The min-cut problem has several interesting applications that seem very different from those of max-flow, even if they are really closely related.

The results of running the test harness show no unit tests were run, because no unit tests have been added to the test/unit directory yet. They also show that two integration tests were run one that was successful, and one that failed. Because the Todo class does not have any implementation yet, you expected both tests to fail. So, you should evaluate the testPersist test to understand why it passed and to improve the test to make sure it fails before writing the Todo implementation. Listing 4-4 shows the original implementation of the testPersist test.

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Execution begins in routine A at t1 Instrumentation settings of routine A are in effect at this point At time t2, routine A calls routine B To preserve the instrumentation settings of routine A, routine B pushes them onto a stack and then puts its own settings into effect At t3, routine B calls routine C, which in turn saves instrumentation settings from routine B on the stack before overwriting them At t4, the application exits routine C, which restores the instrumentation settings of routine B by popping them off the stack, just prior to exiting The process goes on until the program exits from routine A at some point after t7 The axis on the right indicates which routine is executing at a certain point in time.

Finally, I ll give you some pointers on one way of extending the max-flow problem, by adding costs, and looking for the cheapest of the maximum flows, paving the way for applications such as mincost bipartite matching The max-flow problem and its variations have almost endless applications Douglas B West, in his book Graph Theory (see References, 2), gives some rather obvious ones, such as determining the total capacities of road and communication networks, or even working with currents in electrical circuits Kleinberg and Tardos (see References in 1) explain how to apply the formalism to survey design, airline scheduling, image segmentation, project selection, baseball elimination, and assigning doctors to holidays Ahuja, Magnanti, and Orlin have written one of the most thorough books on the subject and cover well over a hundred applications in such diverse areas as engineering, manufacturing, scheduling, management, medicine, defense, communication, public policy, mathematics, and transportation.

void testPersist() { new Todo(name: "1", new Todo(name: "2", new Todo(name: "3", new Todo(name: "4", new Todo(name: "5",

If this approach is adhered to throughout the code path of an application, it is possible to find out how much time is spent in each module by using DBMS MONITOR and V$SERV MOD ACT STATS Statistics provided by the view V$SERV MOD ACT STATS include DB time , DB CPU , physical reads , physical writes , cluster wait time , concurrency wait time , application wait time , and user I/O wait time ..

Although the algorithms apply to graphs, these application need not be all that graph-like at all For example, who d think of image segmentation as a graph problem I ll walk you through some of these applications in the unsurprisingly named section Some Applications later in the chapter If you re curious about how the techniques can be used, you might want to take a quick glance at that section before reading on The general idea that runs through this chapter is that we re trying to get the most out of a network, moving from one side to the other, pushing through as much of we can of some kind of substance be it edges of a bipartite matching, edge-disjoint paths, or units of flow This is a bit different from the cautious graph exploration in the previous chapter The basic approach of incremental improvement is still here, though.

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7. The Hotsos ILO library is free software and may be downloaded from http://sourceforge.net/ projects/hotsos ilo.

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