### Table 1: Object mapping for Gauss-Elimination. 4.4 Optimizing dynamic object placement for tree-structured programs Dynamic load balancing is an extensively researched area, and there are several general-purpose as well as application- speci c strategies. If we know the problem structure, we can sometimes use better-tuned strategies. Recognizing this, Paradise uses a special load balancing strategy for tree-structured computations where there is some amount of uniformity in the tree. In tree-structured programs,

1996

"... In PAGE 6: ... Paradise now generates a hint (consisting of the values of the constants in the mapping expression) to the object-mapping module in the run-time through the optimization hints le. Table1 presents times in seconds for running the Gauss-Elimination program on 32 proces- sors of the CM-5 for a 1000x1000 matrix, with a random object mapping strategy and the automatically optimized... ..."

Cited by 3

### Table 1: Object mapping for Gauss-Elimination. 4.4 Optimizing dynamic object placement for tree-structured programs Dynamic load balancing is an extensively researched area, and there are several general-purpose as well as application- speci c strategies. If we know the problem structure, we can sometimes use better-tuned strategies. Recognizing this, Paradise uses a special load balancing strategy for tree-structured computations where there is some amount of uniformity in the tree. In tree-structured programs,

1996

"... In PAGE 6: ... Paradise now generates a hint (consisting of the values of the constants in the mapping expression) to the object-mapping module in the run-time through the optimization hints le. Table1 presents times in seconds for running the Gauss-Elimination program on 32 proces- sors of the CM-5 for a 1000x1000 matrix, with a random object mapping strategy and the automatically optimized... ..."

Cited by 3

### Table 4 Classification results with different reducts 1: Number of rules; 2: Classification accuracy POSAR CEAR DISMAR GAAR PSORSAR

"... In PAGE 25: ... So, all the particles have a powerful search capability, which can help the swarm avoid dead ends. The comparison of the number of decision rules and the classification accuracy with different reducts are shown in Table4... ..."

### Table 1: Results of the Constrained Optimization Problem

"... In PAGE 9: ...961.81381. The first two constraints are active at the optimum. Table1 provides the results obtained using a swarm size of 200 flying for 100 time steps and also with a swarm size of 300 flying for 100 time steps. Five successive trials have been conducted to compute the best, worst and the average values of the objective using the present algorithm.... In PAGE 9: ...he worst, average and the best values are [-6729.7983, -6837.4347, -6883.7124]. It can be clearly observed from Table 2 that our algorithm reports consistent values with much less evaluations when compared with Koziel and Michalewicz (1999). Table1 . Results of the Constrained Optimization Problem Table 2.... ..."

### Table 3b. Solution Statistics for Model 2 (Minimization)

1999

"... In PAGE 4: ...6 Table 2. Problem Statistics Model 1 Model 2 Pt Rows Cols 0/1 Vars Rows Cols 0/1 Vars 1 4398 4568 4568 4398 4568 170 2 4546 4738 4738 4546 4738 192 3 3030 3128 3128 3030 3128 98 4 2774 2921 2921 2774 2921 147 5 5732 5957 5957 5732 5957 225 6 5728 5978 5978 5728 5978 250 7 2538 2658 2658 2538 2658 120 8 3506 3695 3695 3506 3695 189 9 2616 2777 2777 2616 2777 161 10 1680 1758 1758 1680 1758 78 11 5628 5848 5848 5628 5848 220 12 3484 3644 3644 3484 3644 160 13 3700 3833 3833 3700 3833 133 14 4220 4436 4436 4220 4436 216 15 2234 2330 2330 2234 2330 96 16 3823 3949 3949 3823 3949 126 17 4222 4362 4362 4222 4362 140 18 2612 2747 2747 2612 2747 135 19 2400 2484 2484 2400 2484 84 20 2298 2406 2406 2298 2406 108 Table3 a. Solution Statistics for Model 1 (Maximization) Pt Initial First Heuristic Best Best LP Obj.... In PAGE 5: ...) list the elapsed time when the heuristic procedure is first called and the objective value corresponding to the feasible integer solution returned by the heuristic. For Table3 a, the columns Best LP Obj. and Best IP Obj.... In PAGE 5: ... report, respectively, the LP objective bound corresponding to the best node in the remaining branch-and-bound tree and the incumbent objective value corresponding to the best integer feasible solution upon termination of the solution process (10,000 CPU seconds). In Table3 b, the columns Optimal IP Obj., bb nodes, and Elapsed Time report, respectively, the optimal IP objective value, the total number of branch-and-bound tree nodes solved, and the total elapsed time for the solution process.... ..."

### Table 1: Results of 30 independent runs on 8 benchmark tests using Augmented Lagrange Particle Swarm Optimization. Column 2 shows the number of particles np and column 3 the number of function calls nf. Details about the test functions can be found in the Appendix.

2005

"... In PAGE 6: ...(18), we maintain the magnitude of the penalty factors such that an e ective change in Lagrange multipliers is possible. This lower bound is formulated by rp;i 1 2 s j ij g;h : (20) Table1 summarizes the experimental results using ALPSO for solving eight constrained benchmark problems. All results show the average values of 30 independent runs on each test function.... In PAGE 7: ...with [13] the results from ALPSO are comparable or superior with less function evaluations required. The number of function evaluations listed in Table1 represents an upper limit where we stopped the optimization process. However, the best solution of each run was usually found much earlier.... ..."

### Table 1: List of positions of the loudspeakers and whether or not there was a dynamic object associated.

### Table 1. Comparison of results for various approaches.

"... In PAGE 8: ... 4. Numerical Results Table1 compares the balance and uniformity (t,s) of (n,2) de Bruijn sequences... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. ------------------------- Table1 goes here ------------------------- In Table 1, we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... In PAGE 9: ... In the case of Algorithm II, the characteristics of the sequences obtained by the optimal mappings with respect to both balance and uniformity criteria are shown. -------------------------Table 1 goes here ------------------------- In Table1 , we observe that: 1. Although Algorithm I generates sequences with optimal uniformity (minimum s), the corresponding balance criterion t is rather large.... ..."

### Table 1. Performance Characteristics of Different AM Implementations

1997

"... In PAGE 7: ...ficient, buffered writes in the SCI DSM only. Performance measurements on the UCSB SCI cluster show competitive performance behavior of the SCI AM system ( Table1 ). Our own implementation, depicted in the first row of Table 1, adds little over- head to the raw latency of 9.... ..."

Cited by 13