Optimization via Non-linear Optimization
OptimizerNLoptr class that implements non-linear optimization. Calls
nloptr::nloptr() from package nloptr.
algorithmcharacter(1)
eval_g_ineqfunction()
xtol_relnumeric(1)
xtol_absnumeric(1)
ftol_relnumeric(1)
ftol_absnumeric(1)
start_valuescharacter(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.
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress").
bbotk::Optimizer -> OptimizerNLoptr
new()
Creates a new instance of this R6 class.
OptimizerNLoptr$new()
clone()
The objects of this class are cloneable with this method.
OptimizerNLoptr$clone(deep = FALSE)
deepWhether to make a deep clone.
Johnson, G S (2020). “The NLopt nonlinear-optimization package.” https://github.com/stevengj/nlopt.
if(requireNamespace("nloptr")) {
library(paradox)
domain = ParamSet$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)
}Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.