eldo_user

Optimization
Types of Design Objective
These statements, where two design objectives are minimized, define a bi-criterion problem or
a vector-valued objective function (f m (1) , f m (2) ). The technique used, known as scalarization, is
a standard approach for finding the solution to a vector optimization problem. Applied to this
simple problem, it leads to the minimization of the global objective function:
F = μ m (1) f m
(1)
+ μ m (2) f m
(2)
The optimization method considers a unique problem that is the aggregation of two concurrent
problems.
In practice you need to experiment with the different choices of weights with successive
adjustments. This technique is explained in “Role of the Weight Values on Minimization
Objectives” on page 611.
Effect of Multiple Sweeps on Minimization Objectives
When multiple sweeps or multiple step increments on circuit parameters are present in the
netlist, an extracted measure qualified as GOAL=MINIMIZE (or MAXIMIZE) must be handled
as a multi-dimensional object. Consider the following statements where P parameters have been
specified:
* Design parameters specification
.STEP PARAM OMEGA_J ! for all j in {1, ..., P}
* Minimize or Maximize statements
.OBJECTIVE EXTRACT_INFO LABEL=f_m
+ {$MACRO|FUNCTION}
+ GOAL=MINIMIZE
+ WEIGHT=MU_M
As above, the technique of scalarization is used, replacing the minimization of the
vector-valued function by the minimization of the sum of the components of f m . The optimizer
then forms and minimizes the function:
The extracted measure f m can be considered as a group of functions to be minimized. In these
cases the weight value applies to all functions in the group implying that all functions have the
same relative importance.
Role of the Weight Values on Minimization Objectives
The values μ
(i)
m are positive weight values attached to each design objective. Increase the
weight value μ
(i)
m if you want the objective f (i) m (x) to be a lower value.
Eldo® User's Manual, 15.3 611