eldo_user

Optimization
Designing a Low Noise Amplifier (LNA)
Solving the restricted problem should require only a fraction of the effort needed to solve the
continuous problem, as shown in Table 13-9, because the number of variables is smaller and the
solution should be close to the solution of the continuous problem.
Table 13-9. Results of Restricted Problem
Run Fmerit Nb Iter Nb Simul
N1=20, LS=1.5x10 -9 4.9403493 16 130
N1=20, LS=1.75x10 -9 5.4893925 16 131
N1=15, LS=1.5x10 -9 5.4792729 57 467
N1=15, LS=1.75x10 -9 5.0082957 40 329
The value of the merit function Fmerit at the continuous solution is a lower bound on the
restricted solutions. This value was given above and is 4.8781332. The solution obtained for the
run with N1 = 20 and LS =1.5x10 −9 will be a satisfactory solution. The extra computation cost
in solving the additional restricted problems associated with the discrete variables is likely to be
a fraction of the cost to solve the original full continuous problem. If not, this implies that the
discrete solution differs substantially from the continuous solution.
Example of Solving a Pseudodiscrete Variable Problem
The following experiment can be considered a complete enumeration procedure where all the
possible combinations of the discrete values of N1 and LS were tested.
Note
This kind of process is very costly and inefficient and given only for illustrative purposes.
The following statements were used:
.PARAM LS=0.25n
.PARAM N1=30
.STEP PARAM LS 0 3n 0.25n
.STEP PARAM N1 10 50 5
.OPTIMIZE OUTER=LS,N1
This outer loop results shown at the end of the .otm file (some columns were removed to
simplify the results) were:
670
Eldo® User's Manual, 15.3