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")) {
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
# }