Evaluate cost function for samples

```
[y,info]
= sdo.evaluate(fcn,params)
```

```
[y,info]
= sdo.evaluate(fcn,params,param_samples)
```

```
[y,info]
= sdo.evaluate(___,opts)
```

`[`

evaluates
the cost function, `y`

,`info`

]
= sdo.evaluate(`fcn`

,`params`

)`fcn`

, for samples of the parameter
space specified by `params`

(`sdo.ParameterSpace`

object).
The software generates a table of samples with 2*Np*+1
rows and *Np* columns. These samples are generated
based on the parameter space specifications in `params`

,
per its `ParameterDistributions`

, `RankCorrelation`

,
and `Options`

properties. *Np* is
the number of parameters specified in `params`

. `fcn`

takes
sample values and computes model goal values. A model goal could be
a cost (objective), constraint, or assessment of difference between
experimental data and model simulation. `sdo.evaluate`

applies `fcn`

to
each row of the table of samples. `y`

is a table
with one column for each model goal output returned by `fcn`

and
2*Np*+1 rows. Additional evaluation information is
returned in `info`

.

`[`

evaluates
the cost function for the specified parameter samples table, `y`

,`info`

]
= sdo.evaluate(`fcn`

,`params`

,`param_samples`

)`param_samples`

.
For this syntax, you can specify `params`

as an `sdo.ParameterSpace`

object
or a vector of `param.Continuous`

objects. `y`

is
a table with one column for each model goal (cost or constraint) output
returned by `fcn`

. `y`

contains
as many rows as `param_samples`

.

`sdo.EvaluateOptions`

| `sdo.ParameterSpace`

| `sdo.analyze`

| `sdo.optimize`

| `sdo.sample`

Was this topic helpful?