# Optimization via Design Points

Source:`R/OptimizerBatchDesignPoints.R`

`mlr_optimizers_design_points.Rd`

`OptimizerBatchDesignPoints`

class that implements optimization w.r.t. fixed
design points. We simply search over a set of points fully specified by the
user. The points in the design are evaluated in order as given.

In order to support general termination criteria and parallelization, we
evaluate points in a batch-fashion of size `batch_size`

. Larger batches mean
we can parallelize more, smaller batches imply a more fine-grained checking
of termination criteria.

## Dictionary

This Optimizer can be instantiated via the dictionary
mlr_optimizers or with the associated sugar function `opt()`

:

## Parameters

`batch_size`

`integer(1)`

Maximum number of configurations to try in a batch.`design`

data.table::data.table

Design points to try in search, one per row.

## Progress Bars

`$optimize()`

supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
`progressr::with_progress()`

to enable them. We recommend to use package
progress as backend; enable with `progressr::handlers("progress")`

.

## Super classes

`bbotk::Optimizer`

-> `bbotk::OptimizerBatch`

-> `OptimizerBatchDesignPoints`

## Examples

```
library(data.table)
search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))
codomain = ps(y = p_dbl(tags = "minimize"))
objective_function = function(xs) {
list(y = as.numeric(xs)^2)
}
objective = ObjectiveRFun$new(
fun = objective_function,
domain = domain,
codomain = codomain)
instance = OptimInstanceBatchSingleCrit$new(
objective = objective,
search_space = search_space,
terminator = trm("evals", n_evals = 10))
design = data.table(x = c(0, 1))
optimizer = opt("design_points", design = design)
# Modifies the instance by reference
optimizer$optimize(instance)
#> x x_domain y
#> <num> <list> <num>
#> 1: 0 <list[1]> 0
# Returns best scoring evaluation
instance$result
#> x x_domain y
#> <num> <list> <num>
#> 1: 0 <list[1]> 0
# Allows access of data.table of full path of all evaluations
as.data.table(instance$archive)
#> x y timestamp batch_nr x_domain_x
#> <num> <num> <POSc> <int> <num>
#> 1: 0 0 2024-06-28 15:41:32 1 0
#> 2: 1 1 2024-06-28 15:41:32 2 1
```