eldo_user

Optimization
Types of Design Objective
This leads to the minimization of the global objective function:
F = μ r (1) (f r
(1)
− r (1) ) 2 + μ r (2) (f r
(2)
− r (2) ) 2
In practice you need to experiment with different choices of weights by successive adjustments
(refer to “Role of the Weight Values on Minimization Objectives” on page 611).
Effect of Multiple Sweeps and Step Increments
The combination of optimization with goal values and multiple .STEP commands is supported.
Consider the following statements where P parameters have been specified:
* Design parameters specification
.STEP PARAM OMEGA_1
.STEP PARAM OMEGA_2
*...
.STEP PARAM OMEGA_P
* Goal statement
.OBJECTIVE EXTRACT_INFO LABEL=F_R
+ {$MACRO|FUNCTION}
+ GOAL=R
+ WEIGHT=MU_R
As above, the technique of scalarization is used, replacing the minimization of a vector-valued
function by the minimization of the sum of the components of f r . The optimizer then forms and
minimizes the function:
Note
The weight number applies to all functions in the group, however, it is possible to associate
a weight to each component of f r . This can done with the .DATA command. Refer to
“Design Objectives for Multi-Point Simulation” on page 618 for more information.
Related Topics
Minimization and Maximization Objectives
Range Constraints Objectives
Objectives for Operating Modes
Eldo® User's Manual, 15.3 613