TunerNLoptr 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.

## Details

The termination conditions stopval, maxtime and maxeval of nloptr::nloptr() are deactivated and replaced by the bbotk::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.

## Dictionary

This Tuner can be instantiated via the dictionary mlr_tuners or with the associated sugar function tnr():

TunerNLoptr$new() mlr_tuners$get("nloptr")
tnr("nloptr")

## Logging

All Tuners use a logger (as implemented in lgr) from package bbotk. Use lgr::get_logger("bbotk") to access and control the logger.

## Optimizer

This Tuner is based on bbotk::OptimizerNLoptr which can be applied on any black box optimization problem. See also the documentation of bbotk.

## 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

### Method clone()

The objects of this class are cloneable with this method.

TunerNLoptr$clone(deep = FALSE) #### Arguments deep Whether to make a deep clone. ## Examples if (FALSE) { # retrieve task task = tsk("pima") # load learner and set search space learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)) # hyperparameter tuning on the pima indians diabetes data set instance = tune( method = "nloptr", task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), algorithm = "NLOPT_LN_BOBYQA" ) # best performing hyperparameter configuration instance$result

# all evaluated hyperparameter configuration
as.data.table(instance$archive) # fit final model on complete data set learner$param_set$values = instance$result_learner_param_vals