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Greedy algorithms are characterized by how they make decisions. In building a solution, step by step, each added element is the one that looks best at the moment it s added, without concern for what went before or what will happen later. Such algorithms can often be quite simple to design and implement, but showing that they are correct (that is, optimal) is often challenging. In general, you need to show that making a greedy choice is safe that if the solution you had was promising, that is, it could be extended to an optimal one, then the one after the greedy choice is also promising. The general principles, as always, is that of induction, though there are a couple of more specialized ideas that can be useful. For example, if you can show that a hypothetical optimal solution can be modified to become the greedy solution without loss of quality, then the greedy solution is optimal. Or, if you can show that during the solution building process, the greedy partial solutions in some sense keep up with a hypothetical optimal sequence of solutions, all the way to the final solution, you ve can (with a little care) use that to show optimality. Important greedy problems and algorithms discussed in this chapter include the knapsack problem (selecting a weight-bounded subset of items with maximum value), where the fractional version can be solved greedily; Huffman trees, which can be used to create optimal prefix codes, and are built greedily by combining the smallest trees in the partial solution; and minimum spanning trees, which can be built using Kruskal s algorithm (keep adding the smallest valid edge) or Prim s algorithm (keep connecting the node that is closest to your tree).

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class User { String userName String firstName String lastName static constraints = { userName(blank:false,unique:true) firstName(blank:false) lastName(blank:false) } String toString () { "$lastName, $firstName" } }

Figure 10-2 An s-t network with one path found (heavy edges) and one augmenting path (highlighted) If you either add an incoming edge or cancel an outgoing one at some node u, that node will be overcrowded It will have more paths entering than leaving, which isn t allowed You can fix this either by adding an outgoing edge or canceling an incoming one All in all, this works out to finding a path from s, following unused edges in their direction and used ones against their direction Any time you can find such an augmenting path, you will also have discovered an additional disjoint path Code for implementing this algorithm can be found in Listing 10-2 Listing 10-2.

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In theory, the extended SQL trace file should contain the same figures, however small discrepancies are in order The trace file that results from this test is small enough to evaluate manually Note that the trace file which results the first time you run the test may be larger than the file reproduced below, since the dictionary cache and the library cached need to be loaded Except for some header information, the complete trace file (with line numbers added) is depicted here: 1 Oracle Database 10g Enterprise Edition Release 102030 - Production 2 With the Partitioning, Oracle Label Security, OLAP and Data Mining options 3 4 *** ACTION NAME:() 2007-11-20 15:39:38546 5 *** MODULE NAME:(insert customerpl) 2007-11-20 15:39:38546 6 *** SERVICE NAME:(TENoradbprocom) 2007-11-20 15:39:38546 7 *** SESSION ID:(44524) 2007-11-20 15:39:38.

In Listing 4-10, you see the User class contains userName, firstName, and lastName properties. It also contains constraints that make all properties required and forces the userName property to be unique in the database. Finally, the overridden toString() method returns the user s name in a last-name-first format.

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Counting Edge-Disjoint Paths Using Labeling Traversal to Find Augmenting Paths def paths(G, s, t): # Edge-disjoint path count H, M, count = tr(G), set(), 0 # Transpose, matching, result while True: # Until the function returns Q, P = {s}, {} # Traversal queue + tree while Q: # Discovered, unvisited u = Qpop() # Get one if u == t: # Augmenting path! count += 1 # That means one more path break # End the traversal forw = (v for v in G[u] if (u,v) not in M) # Possible new edges back = (v for v in H[u] if (v,u) in M) # Cancellations for v in chain(forw, back): # Along out- and in-edges if v in P: continue # Already visited Ignore P[v] = u # Traversal predecessor Q.

546 8 ===================== 9 PARSING IN CURSOR #2 len=68 dep=0 uid=61 oct=42 lid=61 tim=789991633616 hv=740818757 ad='6be3972c' 10 alter session set events '10046 trace name context forever, level 8' 11 END OF STMT 12 EXEC #2:c=0,e=98,p=0,cr=0,cu=0,mis=0,r=0,dep=0,og=1,tim=789991633607 13 WAIT #2: nam='SQL*Net message to client' ela= 5 driver id=1413697536 #bytes=1 p3=0 obj#=-1 tim=789991638001 14 WAIT #2: nam='SQL*Net message from client' ela= 569 driver id=1413697536 #bytes=1 p3=0 obj#=-1 tim=789991638751 15 ===================== 16 PARSING IN CURSOR #1 len=87 dep=0 uid=61 oct=2 lid=61 tim=789991639097 hv=2228079888 ad='6cad992c'.

Note If you re making changes to domain or controller classes while the application is running with the

add(v) # New node discovered else: # Didn't reach t return count # We're done while u != s: # Augment: Backtrack to s u, v = P[u], u # Shift one step if v in G[u]: # Forward edge Madd((u,v)) # New edge else: # Backward edge Mremove((v,u)) # Cancellation To make sure we ve solved the problem, we still need to prove the converse, though that there always will be an augmenting path as long as there is room for improvement The easiest way of showing this is by using the idea of connectivity: how many edges must we remove to separate s from t (so that no path goes from s to t) Any such set represents an s-t cut, a partitioning into two sets S and T, where S contains s and T contains t.

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