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)
Createrandom
start values or based oncenter
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
$optimize()
supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
Super class
bbotk::Optimizer
-> OptimizerNLoptr
Examples
# \donttest{
if (requireNamespace("nloptr")) {
search_space = domain = ps(x = p_dbl(lower = -1, upper = 1))
codomain = ps(y = p_dbl(tags = "minimize"))
objective_function = function(xs) {
list(y = as.numeric(xs)^2)
}
objective = ObjectiveRFun$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)
}
#> x y timestamp batch_nr x_domain_x
#> <num> <num> <POSc> <int> <num>
#> 1: -0.1597428 0.02551775 2024-02-29 15:30:07 1 -0.1597428
#> 2: -0.1597428 0.02551775 2024-02-29 15:30:07 2 -0.1597428
#> 3: -0.1597428 0.02551775 2024-02-29 15:30:07 3 -0.1597428
#> 4: 0.3402572 0.11577499 2024-02-29 15:30:07 4 0.3402572
#> 5: -0.6597428 0.43526050 2024-02-29 15:30:07 5 -0.6597428
#> 6: 0.0000000 0.00000000 2024-02-29 15:30:07 6 0.0000000
#> 7: -0.0500000 0.00250000 2024-02-29 15:30:07 7 -0.0500000
#> 8: 0.0050000 0.00002500 2024-02-29 15:30:07 8 0.0050000
#> 9: -0.0005000 0.00000025 2024-02-29 15:30:07 9 -0.0005000
#> 10: 0.0005000 0.00000025 2024-02-29 15:30:07 10 0.0005000
#> 11: 0.0000000 0.00000000 2024-02-29 15:30:07 11 0.0000000
# }