Unformatted text preview: DP. We tested the algorithms on dierent orderings of variables: input ordering as used by the random problem generator, minwidth ordering, and mindiversity ordering. Given an interaction graph, minwidth ordering selects a variable with the smallest degree, and puts it last in the ordering; the node is eliminated from the graph and the ordering continues recursively. Mindiversity ordering was described earlier. In order to test the algorithms on problems with dierent structures several random generators were used. Uniform kcnfs were obtained using the generator proposed in [17]. It takes as an input the number of variables n, the number of clauses m, and the number of literals per clause k, and obtains each clause by choosing k variables randomly from the set of n variables and by determining the polarity of each literal with probability p = 0.5. Dierent values of p were also used. We did not check clause uniqueness since for
18 large number of variables it is unlikely that identical clauses will be generated. Our second generator, called mixed cnfs, produces theories containing clauses of length k1 or k2 . The third generator, called chains, obtains a sequence of n independent uniform k cnf theories (called subtheories), and connects them in a chain by (n 0 1) 2cnf clauses. Each 2cnf clause contains variables from two consecutive subtheories in the chain (see Figure 7). Similarly, we connected sequences of independent theories into a tree structure. Both chains and treestructures described above belong to a class of random embeddings in ktrees [1]. We implemented a generator, called (k; m)trees, which generalizes the idea of k trees. A (k; m)tree is a tree of cliques, each having (k + m) nodes, where k is the
size of intersection between each two neighbouring cliques. Therefore, conventional k trees are (k; 1)trees according to our denition. The (k; m)tree generators takes as an input k , m, the number of cliques in a (k; m)tree Ncliques, and the number of clauses to be added per each new clique, Nclauses. It generates the rst clique of size k + m with Nclauses clauses in it, then, until Ncliques are generated, it chooses randomly a previously generated clique, randomly selects k variables out of this clique, adds m new variables and generates Nclauses clauses on the new clique. We should notice that Nclauses is not the number of
clauses per each clique, because each new clique shares some variables with the previously (and, possible, future) generated cliques. The induced width of a (k; m)trees is bounded by k + m 0 1.
6.1 Results for Problems with Uniform Structure We recorded CPU time for all algorithms, and the number of deadends for DPbacktracking. We recorded also the number of new clauses generated by DR, the maximal size of the generated clauses, and the induced width. The number of experiments per each data point is reported in the gures. We compared DPbacktracking with DR on uniform k cnfs for k=3,4,5 and on mixed the...
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This note was uploaded on 09/17/2013 for the course PMATH 330 taught by Professor W. alabama during the Spring '09 term at Waterloo.
 Spring '09
 W. Alabama

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