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

Inherited methods

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")) { library(paradox) domain = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1))) search_space = ParamSet$new(list(ParamDbl$new("x", lower = -1, upper = 1))) codomain = ParamSet$new(list(ParamDbl$new("y", 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) }
#> Loading required namespace: nloptr
#> x y timestamp batch_nr x_domain_x #> 1: 6.503988e-01 4.230187e-01 2021-03-21 04:13:52 1 6.503988e-01 #> 2: 6.503988e-01 4.230187e-01 2021-03-21 04:13:52 2 6.503988e-01 #> 3: 6.503988e-01 4.230187e-01 2021-03-21 04:13:52 3 6.503988e-01 #> 4: 9.125997e-01 8.328382e-01 2021-03-21 04:13:52 4 9.125997e-01 #> 5: 3.881980e-01 1.506977e-01 2021-03-21 04:13:52 5 3.881980e-01 #> 6: 1.259971e-01 1.587527e-02 2021-03-21 04:13:52 6 1.259971e-01 #> 7: 0.000000e+00 0.000000e+00 2021-03-21 04:13:52 7 0.000000e+00 #> 8: -2.622009e-02 6.874930e-04 2021-03-21 04:13:52 8 -2.622009e-02 #> 9: 2.622009e-03 6.874930e-06 2021-03-21 04:13:52 9 2.622009e-03 #> 10: -2.622009e-04 6.874930e-08 2021-03-21 04:13:52 10 -2.622009e-04 #> 11: 2.622009e-05 6.874930e-10 2021-03-21 04:13:52 11 2.622009e-05 #> 12: 0.000000e+00 0.000000e+00 2021-03-21 04:13:52 12 0.000000e+00
# }