### Table 6 - Formulas for the Stepwise Regression Models Split Version Formula R2 adj.

in Nile Mosley

"... In PAGE 20: ... The final linear models for stepwise regression presented very high R2 (adj.) ( Table6 ), making it difficult not to choose stepwise regression as the best cost estimation technique for our dataset. Table 6 - Formulas for the Stepwise Regression Models Split Version Formula R2 adj.... ..."

### Table 1. Complexity results for satisfiability. The entries trivial denote cases in which a given formula is always satisfiable. Question marks stand for open questions.

"... In PAGE 2: ...emporal operator S and the boolean operator f (Theorem 3.3). The difficulty lies in simulating the quantifier tree of a Quantified Boolean Formula (QBF) in a linear structure. Our results are summarized in Table1 . The first column contains the propositional restrictions in terms of closed sets of Boolean functions (clones) whose terminology is introduced in the following section.... ..."

### Table 2. Hard Constraints

"... In PAGE 4: ... The combination of zeroth and first order derivative constraints enables attenuation in the nulling region on the order of 10 4 times the stopband attenuation, without causing the response artifacts due to setting cnull;0 = 10 4cstop directly. Signals with low fundamental frequencies, such as an A0 pi- ano tone, may contain hundreds of audible harmonics up to the Soft Constraints Type Constraint Region Passband jH(!g;j) 1j Passband General Stopband jH(!g;j)j cs General Stopband Nulling jH(!g;j)j cnull;0 Nulling Region jH(!g;j)j cnull;1 Table2 : Soft constraints, satisfied within tolerance c(j) . Nyquist frequency.... ..."

### Table 1: Results for GSAT and DP on hard random 3CNF formulas. variables and simplifying the formula. If no new vari- able can be assigned a value without producing an empty clause, it backtracks. The performance of the basic DP procedure is greatly improved by using unit propagation whenever unit clauses arise:3 variables oc- curring in unit clauses are immediately assigned the truth value that satis es the clause, and the formula is simpli ed, which may lead to new unit clauses, etc. This propagation process can be executed quite e - ciently (in time linear in the total number of literals). DP combined with unit propagation is one of the most widely used methods for propositional satis ability test- ing.

1992

"... In PAGE 3: ... In such cases, GSAT is tested on both satis able and unsatis able instances. Table1 summarizes our results: rst the number of variables and clauses in each formula, and then statis- tics for GSAT and DP. For formulas containing up to 120 variables, the statistics are based on averages over 100 satis able instances; for the larger formulas, the av- erage is based on 10 satis able formulas.... ..."

Cited by 576

### Table 1: Results for GSAT and DP on hard random 3CNF formulas. variables and simplifying the formula. If no new vari- able can be assigned a value without producing an empty clause, it backtracks. The performance of the basic DP procedure is greatly improved by using unit propagation whenever unit clauses arise:3 variables oc- curring in unit clauses are immediately assigned the truth value that satis es the clause, and the formula is simpli ed, which may lead to new unit clauses, etc. This propagation process can be executed quite e - ciently (in time linear in the total number of literals). DP combined with unit propagation is one of the most widely used methods for propositional satis ability test- ing.

1992

"... In PAGE 3: ... In such cases, GSAT is tested on both satis able and unsatis able instances. Table1 summarizes our results: rst the number of variables and clauses in each formula, and then statis- tics for GSAT and DP. For formulas containing up to 120 variables, the statistics are based on averages over 100 satis able instances; for the larger formulas, the av- erage is based on 10 satis able formulas.... ..."

Cited by 576

### Table 1: Results for GSAT and DP on hard random 3CNF formulas. variables and simplifying the formula. If no new vari- able can be assigned a value without producing an empty clause, it backtracks. The performance of the basic DP procedure is greatly improved by using unit propagation whenever unit clauses arise:3 variables oc- curring in unit clauses are immediately assigned the truth value that satis es the clause, and the formula is simpli ed, which may lead to new unit clauses, etc. This propagation process can be executed quite e - ciently (in time linear in the total number of literals). DP combined with unit propagation is one of the most widely used methods for propositional satis ability test- ing.

1992

"... In PAGE 3: ... In such cases, GSAT is tested on both satis able and unsatis able instances. Table1 summarizes our results: rst the number of variables and clauses in each formula, and then statis- tics for GSAT and DP. For formulas containing up to 120 variables, the statistics are based on averages over 100 satis able instances; for the larger formulas, the av- erage is based on 10 satis able formulas.... ..."

Cited by 576

### Table 1. Complexity results for satisfiability. The entries trivial denote cases in which a given formula is always satisfiable. The abbreviation c. stands for complete. Question marks stand for open questions.

"... In PAGE 3: ...em 3.3). The difficulty lies in simulating the quantifier tree of a Quantified Boolean Formula (QBF) in a linear structure. Our results are summarized in Table1 . The first column contains the propo- sitional restrictions in terms of closed sets of Boolean functions (clones) whose terminology is introduced in the following section.... ..."

### Table 1. Complexity results for satisfiability. The entries trivial denote cases in which a given formula is always satisfiable. The abbreviation c. stands for complete. Question marks stand for open questions.

"... In PAGE 3: ...em 3.3). The difficulty lies in simulating the quantifier tree of a Quantified Boolean Formula (QBF) in a linear structure. Our results are summarized in Table1 . The first column contains the propo- sitional restrictions in terms of closed sets of Boolean functions (clones) whose terminology is introduced in the following section.... ..."

### Table 1. SSPs of hard random CNF formulas

2002

"... In PAGE 15: ... These classes were shown to be exponentially hard for general resolution [2,6]. Table1 gives the results of computing SSPs for random CNF formulas from the quot;hard quot; domain [8] (the number of clauses is 4.25 times the number of variables).... In PAGE 15: ... We generated 10 random CNFs of each size (number of variables). Table1 gives the average values of the SSP size and the share (percent) of the Boolean space taken by an SSP. It is not hard to see that the SSP size grows very quickly.... ..."

Cited by 1