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.

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:  0.2103154 0.04423257 2025-02-11 15:24:19        1  0.2103154
#>  2:  0.2103154 0.04423257 2025-02-11 15:24:20        2  0.2103154
#>  3:  0.2103154 0.04423257 2025-02-11 15:24:20        3  0.2103154
#>  4:  0.7103154 0.50454798 2025-02-11 15:24:20        4  0.7103154
#>  5: -0.2896846 0.08391716 2025-02-11 15:24:20        5 -0.2896846
#>  6:  0.0000000 0.00000000 2025-02-11 15:24:20        6  0.0000000
#>  7:  0.0500000 0.00250000 2025-02-11 15:24:20        7  0.0500000
#>  8: -0.0050000 0.00002500 2025-02-11 15:24:20        8 -0.0050000
#>  9:  0.0005000 0.00000025 2025-02-11 15:24:20        9  0.0005000
#> 10: -0.0005000 0.00000025 2025-02-11 15:24:20       10 -0.0005000
#> 11:  0.0000000 0.00000000 2025-02-11 15:24:20       11  0.0000000
# }