Index

A

advanced tasks

interpreting results

methodology

tips and tricks

algorithm

caaoptimizationinterfaces.edu

conjugate gradient (cg)

simulated annealing

types of algorithm

algorithm and objective function

algorithms use

C

commands

Constraint Satisfaction

Design of Experiments

Optimize

constraint

constraint satisfaction

editor

using

using measured parameters in a constraint satisfaction computation

constraint satisfaction editor

constraint with weight

constraints

constraints tab

creating a constraint

D

defining an optimization

design of experiments

design of experiments tool

design of experiments window

prediction tab

results tab

settings tab

E

ENOVIA LCA

F

free parameter

free parameters, selecting

G

getting started

searching for a target value with the gradient algorithm

searching for a target value with the simulated annealing algorithm

global search

gradient based algorithm

non satisfied constraints

I

interoperability

storing optimizations at the Product level in ENOVIA LCA

interpreting results

optimization curves

result file

warnings and errors

L

local search

M

maximization optimization type

maximum value, searching

measured parameters

minimization optimization type

minimum value, searching

O

optimal CATIA plm usability

optimization dialog

constraints tab

problem tab

the computations results tab

optimization, defining

P

problem tab

algorithm

free parameters

optimization data

optimization type

parameter to be optimized

termination criteria

update mode

R

ranges and steps

result, interpreting

running a constrained optimization with weights

S

searching for a maximum value

searching for a minimum value

T

Tools Options - Product Engineering Optimizer

Parameters and Measure Tab

Part Infrastructure Tab

U

using constraints

using the constraint satisfaction function

using the design of experiments tool

W

weight

workbench description