Kamath, A. P., Karmarkar, N. K., Ramakrishnan, K. G., and Resende, M. G. C. (1991. Home/Search Document Not in Database Summary Related Articles Check |
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Phase Transitions and Stochastic Local Search in k-Term.. - Rueckert, Kramer, De.. (2002) (Correct)
....randomized steps from time to time (the so called noise ) An easy way to apply SLS algorithms to k term DNF learning is to reduce a given k term DNF learning problem to a satisfiability (SAT) problem and apply one of the many published SLS algorithms to the resulting SAT prob lem. In [13] Kamath et al. introduced a reduction of k term DNF learning to SAT. They generated a test set of 47 k term DNF learning problem instances ranging from problems with eight variables up to 32 variables. The reduction of this test set to SAT has been widely used as a benchmark for SAT SLS algo ....
....contains one hundred soluble (for k 3) problem instances. The first test set was generated with [Pos[ 10, New[ 10, n 10, the second one with [Pos[ 20, New[ 20, 42 and the third one with IPosl 30, INew[ 30, 180. We reduced the test sets to SAT using the reduction from [13]. The resulting SAT problems describe the desired solution F using 2. IVarl variables. They use another IPosl t auxiliary variables to express, which positive example is covered by which term. The constraints put on those variables by the positive and negative examples are encoded in ]c. IVarl ....
Kamath, A. P., Karmarkar, N. K., Ramakrishnan, K. G., and Resende, M. G. C. (1991.
Generating Hard Satisfiability Problems - Selman, Mitchell, Levesque (1996) (60 citations) (Correct)
....of easy instances. That is, from the space of all problem instances, they sampled in a way that produced almost no hard cases. Nevertheless, papers continue to appear purporting to empirically demonstrate 2 the efficacy of some new procedure, but using just this distribution (e.g. Hooker, 1988; Kamath et al. 1990), or presenting data suggesting that very large satisfiability problems with thousands of propositional variables can be solved. How are we to evaluate these empirical results, given the danger of biasing the sample to suit the procedure in question, or of simply using easy problems (even ....
Kamath, A.P., Karmarker, N.K., Ramakrishnan, K.G., and Resende, M.G.C. (1990).
Hard and Easy Distributions of SAT Problems - Mitchell, Selman, Levesque (1992) (298 citations) (Correct)
....of easy instances. That is, from the space of all problem instances, they sampled in a way that produced almost no hard cases. Nevertheless, papers continue to appear purporting to empirically demonstrate the efficacy of some new procedure, but using just this distribution (e.g. Hooker, 1988; Kamath et al. 1990), or presenting data suggesting that very large satisfiability problems with thousands of propositional variables can be solved. In fact, we are presenting one of the latter kind our PROCEDURE DP Given a set of clauses Sigma defined over a set of variables V : ffl If Sigma is empty, ....
Kamath, A.P., Karmarker, N.K., Ramakrishnan, K.G., and Resende, M.G.C. (1990).
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