User Guide

4 Understandin

Optimization Principles and Options

general, constraints are given much greater weight than the

goals.

This approach has a number of pitfalls. In particular:

• If a very large value is used for the weight of the constraints,

numerical problems occur.

• If a more reasonable value is used, the result is not a true

solution of the original problem.

• Using a sequence of weights, and performing a series of

minimizations can lead to the true solution, but at the

expense of a large increase in optimization time (because of

all of the extra evaluations required to solve the intermediate

problems).

The PSpice Optimizer implements both constrained and

unconstrained minimization algorithms. This means that the

optimizer:

• Tackles constrained problems directly and efficiently.

• Calculates Lagrange multipliers for the solution, which

provide valuable insight to design tradeoffs.

pes of Constraints

Constraints are restrictions placed on potential solutions to

optimization problems. The simplest constraints are bound

constraints—simple limits on the ranges of the parameters (e.g.,

a resistor whose value has to be at least 100 Ω).

More challenging constraints that frequently arise in analog

circuit optimization have dependencies on other characteristics

of the design.

Example: Consider optimizing a MOS amplifier cell which must

satisfy these specifications:

• Reduce power consumption.

• Make sure gain-bandwidth product of the cell is greater than

or equal to some minimum value.