Optimization via Non-linear Optimization

OptimizerBatchNLoptr class that implements non-linear optimization. Calls nloptr::nloptr() from package nloptr.

Source

Johnson, G S (2020). “The NLopt nonlinear-optimization package.” https://github.com/stevengj/nlopt.

Parameters

algorithm

character(1)

eval_g_ineq

function()

xtol_rel

numeric(1)

xtol_abs

numeric(1)

ftol_rel

numeric(1)

ftol_abs

numeric(1)

start_values

character(1)
Create "random" start values or based on "center" of search space? In the latter case, it is the center of the parameters before a trafo is applied. If set to "custom", the start values can be passed via the start parameter.

start

numeric()
Custom start values. Only applicable if start_values parameter is set to "custom".

approximate_eval_grad_f

logical(1)
Should gradients be numerically approximated via finite differences (nloptr::nl.grad). Only required for certain algorithms. Note that function evaluations required for the numerical gradient approximation will be logged as usual and are not treated differently than regular function evaluations by, e.g., Terminators.

For the meaning of the control parameters, see nloptr::nloptr() and nloptr::nloptr.print.options().

The termination conditions stopval, maxtime and maxeval of nloptr::nloptr() are deactivated and replaced by the Terminator subclasses. The x and function value tolerance termination conditions (xtol_rel = 10^-4, xtol_abs = rep(0.0, length(x0)), ftol_rel = 0.0 and ftol_abs = 0.0) are still available and implemented with their package defaults. To deactivate these conditions, set them to -1.

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 -> OptimizerBatchNLoptr

Methods

Public methods

• OptimizerBatchNLoptr$new()

• OptimizerBatchNLoptr$clone()

Inherited methods

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

---

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerBatchNLoptr$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerBatchNLoptr$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

# \donttest{
if (requireNamespace("nloptr")) {

  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)


  # We use the internal termination criterion xtol_rel
  terminator = trm("none")
  instance = OptimInstanceBatchSingleCrit$new(
    objective = objective,
    search_space = search_space,
    terminator = terminator)


  optimizer = opt("nloptr", algorithm = "NLOPT_LN_BOBYQA")

  # Modifies the instance by reference
  optimizer$optimize(instance)

  # Returns best scoring evaluation
  instance$result

  # 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:  0.04727466 0.002234893 2025-05-30 08:00:34        1  0.04727466
#>  2:  0.04727466 0.002234893 2025-05-30 08:00:34        2  0.04727466
#>  3:  0.04727466 0.002234893 2025-05-30 08:00:34        3  0.04727466
#>  4:  0.54727466 0.299509549 2025-05-30 08:00:34        4  0.54727466
#>  5: -0.45272534 0.204960237 2025-05-30 08:00:34        5 -0.45272534
#>  6:  0.00000000 0.000000000 2025-05-30 08:00:34        6  0.00000000
#>  7:  0.05000000 0.002500000 2025-05-30 08:00:34        7  0.05000000
#>  8: -0.00500000 0.000025000 2025-05-30 08:00:34        8 -0.00500000
#>  9:  0.00050000 0.000000250 2025-05-30 08:00:34        9  0.00050000
#> 10: -0.00050000 0.000000250 2025-05-30 08:00:34       10 -0.00050000
#> 11:  0.00000000 0.000000000 2025-05-30 08:00:34       11  0.00000000
# }