### Table 2: Technology Mapping results

"... In PAGE 8: ... The results show that the Boolean approach reduces the number of matching algorithm calls, nd smaller area circuits in better CPU time, and reduces the initial network graph because generic 2-input base function are used. Table2 presents a comparison between SIS and Land for the library 44-2.genlib, which is distributed with the SIS package.... ..."

### Table 3: Summary of the Experimental Results on Constrained Optimization.

"... In PAGE 34: ...Constrained Optimization We now turn to constrained optimization problems which are, in general, very di cult to solve. Table3 summarizes some of our computation results on some of the toughest problems from [10]. We give the number of variables in the initial statement (v), the number of constraints (c), the CPU time, and the number of splits.... ..."

### TABLE I Comparison of Results for the Two-Variable Constrained Optimization Problem.

1999

Cited by 2

### Table 8: Makespan results for large benchmark problem set

1997

Cited by 35

### Table 5: Results of the capacitated facility locations problems on the AP1000

### Table 3.1: Hierarchy discovery and policy optimization framed as a quarticly-constrained optimization problem

2006

Cited by 1

### Table 3. Bound Constrained Problems

1997

"... In PAGE 16: ... There are, in addition, compact representations for the symmetric rank-one (SR1) updating formula, which is particularly appealing in the constrained setting because it is not restricted by the positive de niteness requirement. The recently developed code L-BFGS-B [12], [65] uses a gradient projection ap- proach together with compact limited memory BFGS matrices to solve the bound constrained optimization problem min f(x) subject to l x u: Table3 illustrates the performance of L-BFGS-B on bound constrained problems from the CUTE collection. Once more we use the Newton code of LANCELOT as abenchmark [65].... ..."

Cited by 5

### Table 1 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. For com- parison, we have chosen problems P3 to P8 according to [13], where an Evolutionary Strategy for solving constrained problems is presented. The dimension of the search space, the number of particles and the maximum number of function evaluations are listed in columns 2 to 4. The known optimal solution fopt is given in column 5. We used cognitive and social scaling factor values of c1;2 = 0:8 for problem P1 to P5 and c1;2 = 0:4 for P6 to P8. For all benchmark tests the inertia factor was set to a constant value of w = 0:9 while the maximum number of basic PSO iterations was set to kmax = 3. The constraint toler- ances g;h = 10 4 are used for both equality and inequality constraints in all test runs. All experiments were performed in Matlab.

2005

"... In PAGE 6: ... All experiments were performed in Matlab. Table1 : 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.... 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: Primary activities of a dynamic object-system core 1. Retrieving the values of slots. 2. Storing values into slots.

1997

"... In PAGE 19: ... If we may oversimplify, there are eleven primary ac- tivities of a ProKappa-like core. Table1 lists them in order of frequency of occurrence in running applications (based on our experience with a variety of applications). Note that with the dynamic, instance-method model of object oriented programming, sending a message to an object is effectively doing a slot retrieval followed by an apply.... ..."