User Guide

18 Understandin

Optimization Principles and Options

show discontinuous behavior for small parameter changes. This

can be caused by accumulation of errors in iterative simulation

algorithms.

Figure 4-4 demonstrates a typical case. The effect of the glitch

is serious—the optimizer can get stuck in the spurious local

minimum represented by the glitch.

The optimizer’s threshold mechanism limits the effect of

unreliable data.

To control parameter perturbation between

iterations

In the Threshold text box, enter a value that defines a

fraction of the current parameter value.

Example: A Threshold value of 0.01 means that when the

PSpice Optimizer changes a parameter value, the value will

change by at least 1% of its current value.

By default, Threshold is set to 0 so that small changes in

parameter values are not arbitrarily rejected. To obtain good

results, however, you may need to adjust the Threshold

values. When making adjustments, consider the following:

• If data quality is good, and Threshold is greater than

zero, reduce the Threshold value to find more accurate

parameter values.

• If data quality is suspect (has potential for spurious

peaks or glitches), increase the Threshold value to

ensure that the optimizer will not get stuck during the

run.

ure 4-4

Hypothetical Data

Glitch

Parameter

Goal

Function