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
#> 1: -7.412702e-01 5.494815e-01 2022-06-30 04:24:11 1 -7.412702e-01
#> 2: -7.412702e-01 5.494815e-01 2022-06-30 04:24:11 2 -7.412702e-01
#> 3: -7.412702e-01 5.494815e-01 2022-06-30 04:24:11 3 -7.412702e-01
#> 4: -5.472229e-01 2.994529e-01 2022-06-30 04:24:11 4 -5.472229e-01
#> 5: -9.353176e-01 8.748189e-01 2022-06-30 04:24:11 5 -9.353176e-01
#> 6: -3.531755e-01 1.247329e-01 2022-06-30 04:24:11 6 -3.531755e-01
#> 7: 2.220446e-16 4.930381e-32 2022-06-30 04:24:11 7 2.220446e-16
#> 8: 1.940473e-02 3.765437e-04 2022-06-30 04:24:11 8 1.940473e-02
#> 9: -1.940473e-02 3.765437e-04 2022-06-30 04:24:11 9 -1.940473e-02
#> 10: -1.940473e-04 3.765437e-08 2022-06-30 04:24:11 10 -1.940473e-04
#> 11: 1.940473e-04 3.765437e-08 2022-06-30 04:24:11 11 1.940473e-04
#> 12: -1.297789e-21 1.684255e-42 2022-06-30 04:24:11 12 -1.297789e-21
# }