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")) { 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) }# }
#> Loading required namespace: nloptr
#> x y timestamp batch_nr x_domain_x #> 1: -0.494359142 2.443910e-01 2021-09-17 04:11:57 1 -0.494359142 #> 2: -0.494359142 2.443910e-01 2021-09-17 04:11:57 2 -0.494359142 #> 3: -0.494359142 2.443910e-01 2021-09-17 04:11:57 3 -0.494359142 #> 4: 0.005640858 3.181928e-05 2021-09-17 04:11:57 4 0.005640858 #> 5: -0.994359142 9.887501e-01 2021-09-17 04:11:57 5 -0.994359142 #> 6: 0.055640858 3.095905e-03 2021-09-17 04:11:57 6 0.055640858 #> 7: 0.000000000 0.000000e+00 2021-09-17 04:11:57 7 0.000000000 #> 8: -0.005000000 2.500000e-05 2021-09-17 04:11:57 8 -0.005000000 #> 9: 0.000500000 2.500000e-07 2021-09-17 04:11:57 9 0.000500000 #> 10: -0.000500000 2.500000e-07 2021-09-17 04:11:57 10 -0.000500000 #> 11: -0.000050000 2.500000e-09 2021-09-17 04:11:57 11 -0.000050000 #> 12: 0.000050000 2.500000e-09 2021-09-17 04:11:57 12 0.000050000 #> 13: 0.000000000 0.000000e+00 2021-09-17 04:11:57 13 0.000000000