Optimization via Design Points

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():

mlr_optimizers$get("design_points")
opt("design_points")

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

Methods

Public methods

• OptimizerBatchDesignPoints$new()

• OptimizerBatchDesignPoints$clone()

Inherited methods

• bbotk::Optimizer$format()
• bbotk::Optimizer$help()
• bbotk::Optimizer$print()
• bbotk::OptimizerBatch$optimize()

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Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchDesignPoints$new()

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Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchDesignPoints$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# define the objective function
fun = function(xs) {
  list(y = - (xs[[1]] - 2)^2 - (xs[[2]] + 3)^2 + 10)
}

# set domain
domain = ps(
  x1 = p_dbl(-10, 10),
  x2 = p_dbl(-5, 5)
)

# set codomain
codomain = ps(
  y = p_dbl(tags = "maximize")
)

# create objective
objective = ObjectiveRFun$new(
  fun = fun,
  domain = domain,
  codomain = codomain,
  properties = "deterministic"
)

# initialize instance
instance = oi(
  objective = objective,
  terminator = trm("evals", n_evals = 20)
)

# load optimizer
design = data.table::data.table(x1 = c(0, 1), x2 = c(0, 1))
optimizer = opt("design_points", design = design)

# trigger optimization
optimizer$optimize(instance)
#>       x1    x2  x_domain     y
#>    <num> <num>    <list> <num>
#> 1:     0     0 <list[2]>    -3

# all evaluated configurations
instance$archive
#> 
#> ── <ArchiveBatch> - Data Table Storage ─────────────────────────────────────────
#>       x1    x2     y           timestamp batch_nr x_domain_x1 x_domain_x2
#>    <num> <num> <num>              <POSc>    <int>       <num>       <num>
#> 1:     0     0    -3 2025-10-24 13:29:02        1           0           0
#> 2:     1     1    -7 2025-10-24 13:29:02        2           1           1

# best performing configuration
instance$result
#>       x1    x2  x_domain     y
#>    <num> <num>    <list> <num>
#> 1:     0     0 <list[2]>    -3