eldo_user

Optimization
Types of Design Objective
• A constraint of the form f > 0 is difficult to handle in an active set method such as the
Eldo optimizer SQP algorithm. This is because the feasible region is not closed and the
constraint cannot be active at a solution.
Constraints of this type can be included via the transformation f(x) ≥ ε > 0. If the
constraints are active, you can solve a sequence of problems in which ε → 0. This
prevents f(x) from being evaluated at an infeasible point where it is not defined.
Multiple Specifications
Multiple range constraints can be specified. For example, if the netlist consists of two
statements such as:
* Aggregation of two range constraints
.OBJECTIVE EXTRACT_INFO_1 LABEL=F_I_1
+ {$MACRO|FUNCTION}
+ LBOUND=L_1 UBOUND=U_1
.OBJECTIVE EXTRACT_INFO_2 LABEL=F_I_2
+ {$MACRO|FUNCTION}
+ LBOUND=L_2 UBOUND=U_2
These objectives will be optimized jointly, meaning that a vector of design variables x has to
satisfy the system of inequalities:
This approach consists of defining a unique optimization problem that is the aggregation of all
the constraint statements.
Effect of Multiple Sweeps and Step Increments
Optimizations can be specified where range constraints are combined with multiple .STEP
commands. 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
* Range cosntraint statement
.OBJECTIVE EXTRACT_INFO LABEL=f_i
+ {$MACRO|FUNCTION}
+ LBOUND=L
+ UBOUND=U
The optimizer forms a group (or system) of inequalities as shown below:
Eldo® User's Manual, 15.3 615