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This is the central problem of the chapter. It forms a generalization of both the bipartite matching and the disjoint paths, and it is the mirror image of the minimum cut problem (next section). The only difference from the disjoint path case is that instead of setting the capacity for each edge to one, we let it be an arbitrary positive number. If the capacity is a positive integer, you could think of it as the number of paths that can pass through it. More generally, the metaphor here is some form of substance flowing through the network, from the source to the sink, and the capacity represents the limit for how many units can flow through a given edge. (You can think of this as a generalization of the engagement rings that were passed back and forth in the matching.) In general, the flow itself is an assignment of a number of flow units to each unit (that is, a function or mapping from edges to numbers), while the size or



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Managing Transactions with JUnit 3.8 Legacy Support When using Spring s JUnit 3.8 legacy support to create tests, your test class can extend AbstractTransactionalSpringContextTests to have its test methods run within transactions: package com.apress.springrecipes.bank; import org.springframework.test.AbstractTransactionalSpringContextTests; public class AccountServiceJUnit38LegacyTests extends AbstractTransactionalSpringContextTests { protected void onSetUpInTransaction() throws Exception { accountService.createAccount(TEST_ACCOUNT_NO); accountService.deposit(TEST_ACCOUNT_NO, 100); } // Don't need onTearDown() any more ... }





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The main disadvantage of being stateful is: Stateful session beans don t scale up as well on an EJB server, because they require more system resources than stateless session beans do. A couple of reasons for this are that: Stateful session beans require memory to store the state. Stateful session beans can be swapped in and out of memory (activated and passivated) as the EJB container deems necessary to manage server resources. The state gets stored in a more permanent form whenever a session bean is passivated, and that state is loaded back in when the bean is activated.

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By default, each test method will run within a transaction that will be rolled back at the end of this method. So, you needn t perform database cleanup tasks in the onTearDown() method, and therefore you can simply delete it. Note that the data preparation tasks must be performed in the onSetUpInTransaction() method not onSetUp() to have them run within the same transactions as test methods, which will be rolled back at the end. However, if you would like a transaction to commit at the end of a test method, you can call the setComplete() method explicitly to cause it to commit instead of rolling back. Also, you can end a transaction during a test method by calling the endTransaction() method, which causes a transaction to roll back normally, or commit if you have called setComplete() before. This class requires a transaction manager to be configured in the bean configuration file. By default, it looks for a bean whose type is PlatformTransactionManager, and uses this bean to manage transactions for your test methods. <bean id="transactionManager" class="org.springframework.jdbc.datasource.DataSourceTransactionManager"> <property name="dataSource" ref="dataSource" /> </bean>

magnitude of the flow is the total amount pushed through the network. (This can be found by finding the net flow out of the source, for example.) Note that although flow networks are commonly defined as directed, you could find the maximum flow in an undirected network as well (Exercise 10-4). Let s see how we can solve this more general case. A na ve approach would be to simply split edges, just like the na ve extension of BFS in 9 (Figure 9-2). Now, though, we want to split them lengthwise, as shown in Figure 10-3. Just like BFS with serial dummy nodes gives you a good idea of how Dijkstra s algorithm works, our augmenting path algorithm with parallel dummy nodes is very close to how the full Ford-Fulkerson algorithm for finding maximum flow works. As in the Dijkstra case, though, the actual algorithm can take care of greater chunks of flow in one go, meaning that the dummy node approach (which only lets us saturate one unit of capacity at a time) is hopelessly inefficient. Let s walk through the technicalities. Just like in the zero-one case, we have two rules for how our flow interacts with edges and nodes. As you can see, they parallel the disjoint path rules closely: The amount of flow going into any node except s or t must equal the amount of flow going out of that node. At most c(e) units of flow can go through any given edge.

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