Optimization via Non-linear Optimization

Source: R/OptimizerNLoptr.R

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