bbotk (version 0.3.1)

mlr_optimizers_nloptr: Optimization via Non-linear Optimization

Description

OptimizerNLoptr class that implements non-linear optimization. Calls nloptr::nloptr() from package nloptr.

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()

• bbotk::Optimizer$format()

• 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

# NOT RUN {
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)
}
# }