### Table 2: Comparison of the registration results between CCRE and other MI algorithms for a xed synthetic motion. The true motion is (10 ; 5; 5)

"... In PAGE 4: ... The numerical schemes we used to implement these regis- trations are all based on sequential quadratic programming (SQP) technique. Table2 show the registration results for the three schemes. From the table, we observe that the MI... ..."

### Table 2:

"... In PAGE 22: ... The cost function is the norm of the state vector at the endpoint of the simulation result. In Table2 multistart local optimization is compared with the FP machine. For local optimization SQP (Sequential Quadratic Programming for constrained optimization) is ap- plied as it is described in Section 6.... In PAGE 32: ... The cost function is the norm of the state vector at the end of the simulation of the closed loop system dynamics. Table2 : Comparison between multistart local optimization using SQP (sequential quadratic programming for constrained optimization) and the FP machine (corresponding to Fig.8).... ..."

### Table 1: Existing continuousoptimizationmethodsand exam- ples of their implementations.

2001

"... In PAGE 2: ... In this sec- tion, we review some popular methods, including sequential quadratic programming methods, interior point methods, di- rect search methods, and simulated annealing. Table1 sum- marizes the methods and examples of their implementations. 2.... ..."

Cited by 2

### Table 1: Existing continuous optimization methods and exam- ples of their implementations.

2001

"... In PAGE 2: ... In this sec- tion, we review some popular methods, including sequential quadratic programming methods, interior point methods, di- rect search methods, and simulated annealing. Table1 sum- marizes the methods and examples of their implementations. 2.... ..."

Cited by 2

### Table 2. Bandwidth allocations for example 1 for the sum of the objective function using.classical optimisatron techniques in comparison to genetic based optimisation techniques.

"... In PAGE 3: ... For ease of comparison the results are tabulated below. Table2 considers the sum of the objective functions and Table 3 the product of the objective function #1 Using MATLAB function constr.m for constrained non linear optimisation; uses a Sequential Quadratic Programming method.... ..."

### Table 1. Results of Example 1 Method g(x*) x*1 x*2 NFE NSE Total HMV 2.2293 -0.096 3.161 17 17 51 HMV+ 2.2294 -0.104 3.156 8 8 24

2005

"... In PAGE 3: ... The modified MPPIR search method is compared to HMV, HMV+ and several general optimization algorithms: a modified feasible direction (MFD), a sequential quadratic programming (SQP), and a sequential linear programming (SLP). Table1 shows the inverse MPP * x and limit state function value ( ) * g x in the original space for the example 1. For numerical efficiency of each method, there is the number of function evaluations (NFE) and the number of sensitivity evaluations (NSE).... ..."

### Table 1: Results for Local Search

2005

"... In PAGE 13: ... The local search algorithm starts at a random binary vector and reaches a local maximum in the binary neighborhood by successively moving to the flrst improving neighbor found. Table1 presents the results that were obtained, where average and the maximum objective function value obtained starting from ten random binary vectors are shown for formulations (2) and (6). Matlabr function fmincon uses a sequential quadratic programming ap- proach for solving medium-scale constrained optimization problems.... ..."

### Table 1: Design variable bounds for the ACS problem.

in Convergence of trust region augmented Lagrangian methods using variable fidelity approximation data

"... In PAGE 12: ... The optimization of this problem was carried out using the CSSO data base generation approach in Step 9 of the algorithm. The starting point for this problem is given in the third column of Table1 . The algorithm converged to the global optimum given in Table 2 in four approximate minimizations as observed in the convergence plot of Fig- ure 3.... In PAGE 12: ... The constr function is an implementation of the sequential quadratic programming (SQP) algorithm (see Grace (1992)). The starting point given in Table1 was supplied to the MAT- LAB function constr, which required 52 high delity anal-... ..."

### Table 5: Robust optimization and deterministic optimization results.

"... In PAGE 16: ...4.2 Results Table5 shows the results obtained using the proposed methodology, compared with the results of a standard deterministic optimization based on sequential quadratic programming (SQP) and RSM. The deterministic solution was also used as initial solution for IHRSM.... In PAGE 16: ... (1997), 25 times slower). [Insert Table5 about here] 5 Conclusions This work proposes an economic methodology for performing robust design based on deterministic computer simulations. The combination of a novel iterative heuristic optimization method based on response surface models (IHRSM) with an uncertainty analysis technique (UA) was introduced.... ..."