Optimization via Non-linear Optimization

OptimizerNLoptr 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 class

bbotk::Optimizer -> OptimizerNLoptr

Methods

Public methods

• OptimizerNLoptr$new()

• OptimizerNLoptr$clone()

Inherited methods

• bbotk::Optimizer$format()

• bbotk::Optimizer$help()

• bbotk::Optimizer$optimize()

• bbotk::Optimizer$print()

---

Method new()

Creates a new instance of this R6 class.

Usage

OptimizerNLoptr$new()

---

Method clone()

The objects of this class are cloneable with this method.

Usage

OptimizerNLoptr$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 = OptimInstanceSingleCrit$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
#>  1: -7.412702e-01 5.494815e-01 2022-06-30 04:24:11        1 -7.412702e-01
#>  2: -7.412702e-01 5.494815e-01 2022-06-30 04:24:11        2 -7.412702e-01
#>  3: -7.412702e-01 5.494815e-01 2022-06-30 04:24:11        3 -7.412702e-01
#>  4: -5.472229e-01 2.994529e-01 2022-06-30 04:24:11        4 -5.472229e-01
#>  5: -9.353176e-01 8.748189e-01 2022-06-30 04:24:11        5 -9.353176e-01
#>  6: -3.531755e-01 1.247329e-01 2022-06-30 04:24:11        6 -3.531755e-01
#>  7:  2.220446e-16 4.930381e-32 2022-06-30 04:24:11        7  2.220446e-16
#>  8:  1.940473e-02 3.765437e-04 2022-06-30 04:24:11        8  1.940473e-02
#>  9: -1.940473e-02 3.765437e-04 2022-06-30 04:24:11        9 -1.940473e-02
#> 10: -1.940473e-04 3.765437e-08 2022-06-30 04:24:11       10 -1.940473e-04
#> 11:  1.940473e-04 3.765437e-08 2022-06-30 04:24:11       11  1.940473e-04
#> 12: -1.297789e-21 1.684255e-42 2022-06-30 04:24:11       12 -1.297789e-21
# }