### TABLE 6. Factors associated wttfa an iterative Emergency Room use (*) daring the 3 months preceding the index visit Logistic regression (n - 280f)

### TABLE 5. Frequency (%) of an iterative Emergency Room use during tbe 3 months preceding the index visit according to the type of the reported source of care

### Table 1. Iteration History for Example 1 Now we apply the adaptive procedure described in the preceding section to obtain the sequence of triangulations T1; : : : T6 from T0. Of course, we used the approximation ~ uj?1 2 Sj?1 Sj from the preceding level as an initial guess for the iterative solution. Together with the stopping criterion (22) and the rapid convergence of the preconditioned cg{method this leads to a very moderate number of iterations. A detailed iteration history is given in Table 1. Here tp denotes the elapsed CPU { time for the iteration process 18

1993

Cited by 33

### Table 3{1. Arithmetic and logical operators, in order of decreasing precedence.

"... In PAGE 18: ... Finally, logical expressions can be combined and extended by logic op- erators like or and not. Table3 {1 provides a summary of operators and operations, listed... In PAGE 19: ...Underlying operator binary operator sum + prod * min max exists or forall and Table3 {2. Iterated operators.... In PAGE 19: ... Other associative, commutative operators can be iterated just like sum. Table3 {2 shows those available in AMPL for arithmetic and logical operations. An example of forall,an iterated operator that returns a logical result, is found in the egypt model: forall {u in unit: util[u,pr] gt; 0} u in m_pos[pl] Given a process pr and a plant pl, this expression is true if and only if, for every member u of unit such that util[u,pr] is positive, u is also a member of the set m_pos[pl].... ..."

Cited by 2

### Table II shows the improvements on the discrepancies of the ps?1 rst points in dimension s for the three following choices of initial permutation pi: identity, Braaten and Weller apos;s, and multi-dimensionally permuted of type MC1 and MC2 de ned in the precedent sections with the number of iterations speci ed in Table 1.

1996

Cited by 3

### Table 2. Computational results for minimum cost and minimum makespan problems on two facilities with precedence constraints, using the Benders method. Computation time and number of iterations are shown for individual problem instances. Computa- tion was cut off after 600 seconds. Minimum makespans are also given, except when computation is terminated prematurely, in which case lower and upper bounds are shown. In such cases a feasible solution with makespan equal to the upper bound is obtained.

2004

"... In PAGE 9: ... Easier subproblems could allow the Benders method to deal with larger numbers of tasks. This hypothesis was tested by adding precedence constraints to the problem instances described above; see Table2 . This resulted in fast solution of the scheduling subproblems, except in... ..."

Cited by 15

### Table II. Computational results for minimum cost and minimum makespan problems on two facilities with precedence constraints, using the Benders method. Computation time and number of iterations are shown for individual problem instances. Compu- tation was cut off after 600 seconds. Minimum makespans are also given, except when computation is terminated prematurely, in which case lower and upper bounds are shown. In such cases a feasible solution with makespan equal to the upper bound is obtained.

2004

Cited by 15

### Table III. Computational results for minimum cost and min- imum makespan problems on two facilities with precedence constraints, using the Benders method. Computation time and number of iterations are shown for individual problem instances. Computation was cut off after 600 seconds. Minimum makespans are also given, except when computation is terminated prema- turely, in which case lower and upper bounds are shown. In such cases a feasible solution with makespan equal to the upper bound is obtained.

2004

Cited by 15

### Table 1: A sample of NP heads, preceding verbs, and following prepositions derived from the parsed corpus.

1993

"... In PAGE 6: ... phrase), and the preceding verb if the noun phrase was the object of that verb. The entries in Table1 are those generated from the text above. Each noun phrase in (3) is associated with an entry in the Noun column of the table.... In PAGE 8: ... No Preposition - if there is no preposition, the noun or verb is simply entered with a special symbol NULL, conceived of as the null preposition. (Cases b, f, g, and j-l in Table1 are assigned). 2.... In PAGE 9: ... The instances of by following a passive verb were left unassigned. (Item c in Table1 is assigned). 4.... In PAGE 9: ... Iterate until this step produces no new attachments. (Item d in Table1 may be assigned.) 6.... In PAGE 9: .... (Item d in Table1 is assigned, if not assigned in the previous step.) 7.... In PAGE 9: ... Unsure Attach - assign remaining pairs to the noun. (Items e, h and i in Table1 are assigned.) This procedure gives us bigram counts representing the frequency with which a given noun occurs associated with an immediately following preposition (or no preposition), or a given verb occurs in a transitive use and is associated with a preposition immediately following the object of the verb.... ..."

Cited by 241

### Table 9 shows the performance of our local search heuristic for different numbers of iterations within phase (II). DEV denotes the percentage deviation from the optimal objective function value (precedence-based lower bound) for the 10-activity (30-activity) instances. CPU denotes the computation time in CPU-seconds. avg, stddev, and max denote the average, the standard deviation, and the maximum value of DEV and CPU, respectively.

1997

"... In PAGE 18: ... Table9 : Effect of the Number of Iterations on the 10-Activity Instances iterations 10 50 100 500 avg 27.92 24.... ..."

Cited by 10