Nop Object (R6 Class)

A Nop object defines a Numerical Optimization Problem.

Value

A Nop object, which is an R6 class that specifies the numerical optimization problem, stores optimization results, and provides methods for analyzing the results, see the details.

Getting Started

Step 1: Create a new `Nop` object:

Call object <- Nop$new(f, npar, ...) where

f is the function to be optimized over its first argument,
npar is the length of the first argument of f,
and ... are additional arguments for f.

Step 2: Specify numerical optimizer:

Call object$set_optimizer(<optimizer object>), where <optimizer object> is an object of class optimizer, which can be created with the define_optimizer function from the {optimizeR} package. Two optimizer objects are already available:

optimizer_nlm
optimizer_optim

Step 3: Test the configuration

Call object$test() to validate your configuration. An overview of the configuration yields the $print() method.

Optimization

Call object$evaluate() to evaluate the target function at some point. Call object$optimize() for optimization. See also the methods object$standardize(), object$reduce(), and object$continue() for initialization strategies.

Analysis of the results

The following are methods for the analysis of optimization results, with different filter options for optimization runs, optimizers, and elements:

$results() returns all saved optimization results,
$summary() summarizes the results,
$optima() returns a frequency table of identified optima,
$plot() visualizes the optimization time or value,
$best_parameter() returns the parameter vector at which the optimum value is obtained,
$best_value() returns the found optimum value of f,
$closest_parameter() returns parameter closest to a specified value.

Active bindings

f: The function to be optimized.
f_name: The name of the function to be optimized.
f_target: The name of the target argument, i.e., the argument over which f is optimized.
npar: The length of the target argument, i.e., the argument over which f is optimized.
arguments: A list of specified additional arguments for f.
true_value: The true numeric optimum value of f (if available).
true_parameter: The true optimum numeric parameter vector of length npar (if available), i.e., the point where f obtains its optimum.
minimized: A logical, set to TRUE (default) to show best minimum in $best_value(), $best_parameter(), and $optima().
optimizer: A list of specified optimizers.
new_label: A character, a new optimization label that has not been used yet.

Methods

Method `new()`

Creates a new Nop object.

Usage

Nop$new(f, npar, ...)

Arguments

f: The function to be optimized. It is optimized over its first argument, which should be a numeric vector of length npar.
npar: An integer, the length of the first argument of f (the argument over which f is optimized).
...: Optionally additional and named arguments for f.

Returns

A new Nop object.

Method `print()`

Prints details of the numerical optimization problem.

Usage

Nop$print(digits = getOption("ino_digits", default = 2), ...)

Arguments

digits: An integer, the number of shown decimal places. The default is 2.
...: Currently not used.

Returns

Invisibly the Nop object.

Method `set_argument()`

Sets additional arguments for f.

Usage

Nop$set_argument(...)

Arguments

...: Optionally additional named arguments for f.

Returns

Invisibly the Nop object.

Method `get_argument()`

Gets an argument value for f.

Usage

Nop$get_argument(argument_name)

Arguments

argument_name: A character, the argument to extract.

Returns

The argument.

Method `remove_argument()`

Removes an additional argument for f.

Usage

Nop$remove_argument(argument_name)

Arguments

argument_name: A character, the argument to remove.

Returns

Invisibly the Nop object.

Method `set_optimizer()`

Sets a numerical optimizer.

Usage

Nop$set_optimizer(optimizer, label = NULL)

Arguments

optimizer: An object of class optimizer, which can be created with the define_optimizer function from the {optimizeR} package.
label: A character, a unique label for the optimizer. By default label = NULL, in which case the default label saved inside optimizer is used.

Returns

Invisibly the Nop object.

Method `remove_optimizer()`

Removes numerical optimizer.

Usage

Nop$remove_optimizer(
  which_optimizer,
  verbose = getOption("ino_verbose", default = TRUE)
)

Arguments

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

verbose

A logical, which indicates whether progress/details should be printed. Set to TRUE (FALSE) to print (hide) such messages. The default is TRUE.

Returns

Invisibly the Nop object.

Method `evaluate()`

Evaluates the function.

Usage

Nop$evaluate(at = rnorm(self$npar), time_limit = NULL, hide_warnings = FALSE)

Arguments

at: A numeric vector of length npar, the point where the function is evaluated. Per default, at = rnorm(self$npar), i.e., random values drawn from a standard normal distribution.
time_limit: An integer, the time limit in seconds for computations. No time limit if time_limit = NULL (the default). This currently only works reliably under Windows.
hide_warnings: A logical. Set to TRUE (FALSE) to hide (show) warning messages.

Returns

Either:

a numeric value, the function value at at,
"time limit reached" if the time limit was reached,
the error message if the evaluation failed.

Method `optimize()`

Optimizes the function.

Usage

Nop$optimize(
  initial = "random",
  runs = 1,
  which_optimizer = "all",
  seed = NULL,
  return_results = FALSE,
  save_results = TRUE,
  label = self$new_label,
  ncores = getOption("ino_ncores", default = 1),
  verbose = getOption("ino_verbose", default = TRUE),
  simplify = TRUE,
  time_limit = NULL,
  hide_warnings = TRUE,
  fail_bad_initial = TRUE
)

Arguments

initial

Specify the initial point where the optimizer should start. Either:

the character "random" (the default) for random initial values drawn from a standard normal distribution,
a numeric vector of length npar, the starting point for optimization,
a list of such vectors (in this case, runs is set to the length of the list),
or a function without any arguments that returns a numeric vector of length npar. In all these cases, the same initial values are used for each optimizer. For more flexibility, a funtion for initial can have two arguments, where the first argument specifies the optimization run, and the second argument specifies the optimizer.

runs

An integer, the number of optimization runs. By default, runs = 1.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

seed

An integer, passed to set.seed for reproducibility. Can be NULL for no seed, which is the default.

return_results

A logical, which indicates whether the optimization results should be returned as a list. By default, return_results = FALSE.

save_results

A logical, which indicates whether the optimization results should be saved inside the Nop object. By default, save_results = TRUE.

label

Only relevant if save_results = TRUE. In this case, an optional character to specify a custom label of the optimization. By default, label = self$new_label creates a new label. Labels can be useful to distinguish optimization runs later.

ncores

An integer, the number of CPU cores for parallel computation. The default is 1. You can use parallel::detectCores() to detect the number of available CPU cores.

verbose

A logical, which indicates whether progress/details should be printed. Set to TRUE (FALSE) to print (hide) such messages. The default is TRUE.

simplify

Only relevant if return_results = TRUE and runs = 1 and/or only one optimizer is specified. In this case, if simplify = TRUE, the nested list output of optimization results is flattened if possible.

time_limit

An integer, the time limit in seconds for computations. No time limit if time_limit = NULL (the default). This currently only works reliably under Windows.

hide_warnings

A logical. Set to TRUE (FALSE) to hide (show) warning messages.

fail_bad_initial

Either TRUE to immediately fail if initial contains any misspecifications (default), or FALSE to include the failed runs in the results.

Returns

The return value depends on the value of return_results:

if return_results = FALSE, invisibly the Nop object,
if return_results = TRUE, a nested list of optimization results. Each element corresponds to one optimization run and is a list of results for each optimizer. The results for each optimizer is a list, the output of apply_optimizer. If simplify = TRUE, the output is flattened if possible.

Method `test()`

Validates the configuration of a Nop object.

Usage

Nop$test(
  at = rnorm(self$npar),
  which_optimizer = "active",
  time_limit = 10,
  verbose = getOption("ino_verbose", default = TRUE),
  digits = getOption("ino_digits", default = 2)
)

Arguments

at

A numeric of length npar, the point at which the function f and the specified optimizer are tested. Per default, at = rnorm(self$npar), i.e., random values drawn from a standard normal distribution.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

time_limit

An integer, the time limit in seconds for computations. No time limit if time_limit = NULL (the default). This currently only works reliably under Windows.

verbose

A logical, which indicates whether progress/details should be printed. Set to TRUE (FALSE) to print (hide) such messages. The default is TRUE.

digits

An integer, the number of shown decimal places. The default is 2.

Returns

Invisibly TRUE if the tests are successful.

Method `standardize()`

Standardizes the optimization problem.

Usage

Nop$standardize(
  argument_name,
  by_column = TRUE,
  center = TRUE,
  scale = TRUE,
  ignore = integer(),
  verbose = getOption("ino_verbose", default = TRUE)
)

Arguments

argument_name: A character, the name of the argument of f to be standardized. The argument must a numeric vector, matrix, or data.frame.
by_column: Only relevant if the argument argument_name is a matrix or a data.frame. In that case, either TRUE to standardize column-wise (default) or FALSE to standardize row-wise. Currently, only by_column = TRUE is implemented.
center: Passed to scale. Default is TRUE.
scale: Passed to scale. Default is TRUE.
ignore: A integer (vector) of column indices (or row indices if by_column = FALSE) to not standardize.
verbose: A logical, which indicates whether progress/details should be printed. Set to TRUE (FALSE) to print (hide) such messages. The default is TRUE.

Returns

Invisibly the Nop object.

Method `reduce()`

Reduces the optimization problem.

Usage

Nop$reduce(
  argument_name,
  by_row = TRUE,
  how = "random",
  proportion = 0.5,
  centers = 2,
  ignore = integer(),
  seed = NULL,
  verbose = getOption("ino_verbose", default = TRUE)
)

Arguments

argument_name

A character, the name of the argument of f to be reduced.

by_row

Only relevant if the argument argument_name is a matrix or a data.frame. In that case, either TRUE to reduce row-wise (default) or FALSE to reduce column-wise. Currently, only by_row = TRUE is implemented.

how

A character, specifying how to reduce. Can be one of:

"random" (default), reduce at random
"first", reduce to the first elements
"last", reduce to the last elements
"similar", reduce to similar elements
"dissimilar", reduce to dissimilar elements Note that "similar" and "dissimilar" are based on k-means clustering via kmeans. To apply these options, the argument argument_name must be numeric.

proportion

A numeric between 0 and 1, specifying the reduction proportion. By default, proportion = 0.5.

centers

Only relevant, if how = "(dis)similar". In that case, passed to kmeans. By default, centers = 2.

ignore

Only relevant, if how = "(dis)similar". In that case a integer (vector) of row indices (or column indices if by_row = FALSE) to ignore for clustering.

seed

An integer, passed to set.seed for reproducibility. Can be NULL for no seed, which is the default.

verbose

A logical, which indicates whether progress/details should be printed. Set to TRUE (FALSE) to print (hide) such messages. The default is TRUE.

Returns

Invisibly the Nop object.

Method `reset_argument()`

Resets an additional argument for f after transformation with $standardize() or $reduce().

Usage

Nop$reset_argument(
  argument_name,
  verbose = getOption("ino_verbose", default = TRUE)
)

Arguments

argument_name: A character, the name of the argument to reset.
verbose: A logical, which indicates whether progress/details should be printed. Set to TRUE (FALSE) to print (hide) such messages. The default is TRUE.

Returns

Invisibly the Nop object.

Method `continue()`

Continues optimization runs, e.g., with a transformed parameter.

Usage

Nop$continue(
  which_run = "last",
  which_optimizer = "active",
  seed = NULL,
  return_results = FALSE,
  save_results = TRUE,
  ncores = getOption("ino_ncores", default = 1),
  verbose = getOption("ino_verbose", default = TRUE),
  simplify = TRUE,
  time_limit = NULL,
  hide_warnings = TRUE
)

Arguments

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

seed

An integer, passed to set.seed for reproducibility. Can be NULL for no seed, which is the default.

return_results

A logical, which indicates whether the optimization results should be returned as a list. By default, return_results = FALSE.

save_results

A logical, which indicates whether the optimization results should be saved inside the Nop object. By default, save_results = TRUE.

ncores

An integer, the number of CPU cores for parallel computation. The default is 1. You can use parallel::detectCores() to detect the number of available CPU cores.

verbose

A logical, which indicates whether progress/details should be printed. Set to TRUE (FALSE) to print (hide) such messages. The default is TRUE.

simplify

time_limit

An integer, the time limit in seconds for computations. No time limit if time_limit = NULL (the default). This currently only works reliably under Windows.

hide_warnings

A logical. Set to TRUE (FALSE) to hide (show) warning messages.

Returns

The same as the return value of $optimize().

Method `results()`

Returns saved optimization results.

Usage

Nop$results(
  which_run = "all",
  which_optimizer = "all",
  which_element = "all",
  only_comparable = FALSE,
  simplify = TRUE
)

Arguments

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

which_element

Select elements of saved optimization results. See $elements_available() for the names of all available elements. Either:

"all", all elements,
"basic", the elements
- "value", the numeric value of the found optimum,
- "parameter", the parameter vector at which the optimum value is obtained,
"default", the elements that are saved for all optimization runs by default, i.e.
- "run", the run id,
- "optimizer", the label for the optimizer,
- "value" and "parameter" (see above),
- "seconds", the optimization time in seconds,
- "label", the label for the optimization run,
a character (vector), names of specific elements in the optimization output.

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

simplify

Returns

A nested list of optimization results. Each element corresponds to one optimization run and is a list of results for each optimizer. If simplify = TRUE, the output is flattened if possible.

Method `number_runs()`

Returns the number of saved optimization runs.

Usage

Nop$number_runs(
  which_run = "all",
  which_optimizer = "all",
  only_comparable = FALSE
)

Arguments

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

Returns

An integer.

Method `elements_available()`

Returns names of available elements per optimizer.

Usage

Nop$elements_available(which_optimizer = "all")

Arguments

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

Returns

A list.

Method `clear()`

Deletes optimization results.

Usage

Nop$clear(which_run, which_optimizer = "all", which_element = "all")

Arguments

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

which_element

Select elements of saved optimization results. See $elements_available() for the names of all available elements. Either:

"all", all elements,
"basic", the elements
- "value", the numeric value of the found optimum,
- "parameter", the parameter vector at which the optimum value is obtained,
"default", the elements that are saved for all optimization runs by default, i.e.
- "run", the run id,
- "optimizer", the label for the optimizer,
- "value" and "parameter" (see above),
- "seconds", the optimization time in seconds,
- "label", the label for the optimization run,
a character (vector), names of specific elements in the optimization output.

Returns

Invisibly the Nop object.

Method `summary()`

Provides an overview of the optimization runs.

Usage

Nop$summary(
  which_element = "basic",
  which_run = "all",
  which_optimizer = "all",
  digits = getOption("ino_digits", default = 2),
  only_comparable = FALSE,
  ...
)

Arguments

which_element

Select elements of saved optimization results. See $elements_available() for the names of all available elements. Either:

"all", all elements,
"basic", the elements
- "value", the numeric value of the found optimum,
- "parameter", the parameter vector at which the optimum value is obtained,
"default", the elements that are saved for all optimization runs by default, i.e.
- "run", the run id,
- "optimizer", the label for the optimizer,
- "value" and "parameter" (see above),
- "seconds", the optimization time in seconds,
- "label", the label for the optimization run,
a character (vector), names of specific elements in the optimization output.

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

digits

An integer, the number of shown decimal places. The default is 2.

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

...

Optionally named expressions of elements. See $elements_available() for the names of all available elements. In addition, "true_value", "true_parameter", "best_value", and "best_parameter" can be accessed As an example, you could add distance = "sqrt(sum((parameter - true_parameter) ^ 2))" for the euclidean distance between the estimated and true parameter vector.

Returns

A data.frame with optimization results.

Method `optima()`

Provides an overview of the identified optimum values.

Usage

Nop$optima(
  digits = getOption("ino_digits", default = 2),
  sort_by = "frequency",
  which_run = "all",
  which_optimizer = "all",
  only_comparable = TRUE
)

Arguments

digits

An integer, the number of shown decimal places. The default is 2.

sort_by

Either:

"frequency" (default) to sort rows by frequency,
"value" to sort by value.

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

Returns

A data.frame.

Method `plot()`

Visualizes the optimization time or value.

Usage

Nop$plot(
  which_element = "seconds",
  by = NULL,
  relative = FALSE,
  which_run = "all",
  which_optimizer = "all",
  only_comparable = FALSE,
  title = paste("Optimization of", x$f_name),
  xlim = c(NA, NA)
)

Arguments

which_element

Either:

"seconds" to plot the optimization times (default)
"value" to plot the optimization values

by

Either:

"label" to group by optimization label
"optimizer" to group by optimizer
NULL to not group (default)

relative

Only if which_element = "seconds". In this case, set to TRUE to plot relative time differences with respect to the overall median.

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

title

A character, the plot title.

xlim

Passed on to coord_cartesian.

Returns

A ggplot object.

Method `deviation()`

Visualizes deviation of parameters.

Usage

Nop$deviation(
  reference = self$true_parameter,
  which_element = "initial",
  which_run = "all",
  which_optimizer = "all",
  only_comparable = FALSE,
  title = "Parameter deviation",
  ylim = c(NA, NA),
  parameter_labels = paste0("theta", 1:self$npar)
)

Arguments

reference

A numeric of length self$npar, the reference parameters.

which_element

Either:

"initial" to compute deviations to the initial values (default)
"parameter" to compute deviations to the estimated parameters

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

title

A character, the plot title.

ylim

Passed on to coord_cartesian.

parameter_labels

A character of length length(reference) with labels for the parameters.

Returns

A ggplot object.

Method `trace()`

Capture trace of optimization with stats::nlm().

Usage

Nop$trace(
  initial = stats::rnorm(self$npar),
  iterations = 100,
  tolerance = 1e-06,
  which_element = c("value", "parameter", "gradient", "hessian", "seconds"),
  ...
)

Arguments

initial

A numeric vector of length npar, the starting point for the optimization. By default, initial = stats::rnorm(self$npar), i.e., random initial values drawn from a standard normal distribution.

iterations

A positive integer, the maximum number of iterations before termination. By default, interations = 100.

tolerance

A numeric, the minimum allowable absolute change in the function value before termination. By default, tolerance = 1e-6.

which_element

A character (vector) of elements to provide in the output, can be one or more of:

value (the current function value)
parameter (the current value of each parameter)
gradient (the current gradient value)
hessian (the current Hessian value)
seconds (the number of seconds for the current iteration)

...

Additional arguments passed on to nlm. The arguments iterlim and hessian cannot be specified.

Returns

A data.frame with iterations in rows, the columns depend on the specification of which_element.

Method `best_value()`

Returns the best found numeric value of f.

Usage

Nop$best_value(
  which_run = "all",
  which_optimizer = "all",
  only_comparable = TRUE,
  digits = getOption("ino_digits", default = 2)
)

Arguments

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

digits

An integer, the number of shown decimal places. The default is 2.

Returns

A numeric, the best found numeric value of f. The output has two attributes:

run, the run id that led to the best value,
optimizer, the optimizer that led to the best value. In the case that multiple optimization runs led to the best value, the first one of them is returned.

Method `best_parameter()`

Returns the best found numeric parameter vector.

Usage

Nop$best_parameter(
  which_run = "all",
  which_optimizer = "all",
  only_comparable = TRUE,
  digits = getOption("ino_digits", default = 2)
)

Arguments

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

digits

An integer, the number of shown decimal places. The default is 2.

Returns

A numeric of length self$npar. The output has two attributes:

run, the run id that led to the best parameter vector,
optimizer, the optimizer that led to the best parameter vector. Note that this parameter vector is not necessarily unique.

Method `closest_parameter()`

Extracts the parameter vector that led to a function value closest (in absolute value) to value. Note that this parameter vector is not necessarily unique.

Usage

Nop$closest_parameter(
  value,
  which_run = "all",
  which_optimizer = "all",
  only_comparable = TRUE,
  digits = getOption("ino_digits", default = 2)
)

Arguments

value

A single numeric.

which_run

Select saved results of optimization runs. Either:

"all", all results,
"last", the results from the last optimization,
"failed", the results from failed optimization runs,
a character (vector) of labels specified in $optimize(),
a numeric (vector) of run ids.

which_optimizer

Select specified numerical optimizers. Either:

"all", all specified optimizers,
"removed", all removed optimizers,
"active", all active optimizers (i.e., not removed),
a character (vector) of specified optimizer labels,
a numeric (vector) of optimizer ids (see $print() output).

only_comparable

Either TRUE to show only comparable results (i.e., results obtained for the original optimization problem), or FALSE to include all optimization results.

digits

An integer, the number of shown decimal places. The default is 2.

Returns

A numeric vector of length self$npar. The output has two attributes:

run, the run id that led to this parameter vector,
optimizer, the optimizer that led to this parameter vector.

Method `clone()`

The objects of this class are cloneable with this method.

Usage

Nop$clone(deep = FALSE)

Arguments

deep: Whether to make a deep clone.

Examples

Nop$new(f = f_ackley, npar = 2)$
  set_optimizer(optimizer_nlm())$
  optimize(runs = 100, verbose = FALSE)$
  optima()
#>   value frequency
#> 1     0        39
#> 2  2.58        34
#> 3  3.57        12
#> 4  5.38         6
#> 5  4.88         5
#> 6  6.56         2
#> 7  6.88         2

Value

Getting Started

Step 1: Create a new Nop object:

Step 2: Specify numerical optimizer:

Step 3: Test the configuration

Optimization

Analysis of the results

Active bindings

Methods

Public methods

Method new()

Usage

Arguments

Returns

Method print()

Usage

Arguments

Returns

Method set_argument()

Usage

Arguments

Returns

Method get_argument()

Usage

Arguments

Returns

Method remove_argument()

Usage

Arguments

Returns

Method set_optimizer()

Usage

Arguments

Returns

Method remove_optimizer()

Usage

Arguments

Returns

Method evaluate()

Usage

Arguments

Returns

Method optimize()

Usage

Arguments

Returns

Method test()

Usage

Arguments

Returns

Method standardize()

Usage

Arguments

Returns

Method reduce()

Usage

Arguments

Returns

Method reset_argument()

Usage

Arguments

Returns

Method continue()

Usage

Arguments

Returns

Method results()

Usage

Arguments

Returns

Method number_runs()

Usage

Arguments

Returns

Method elements_available()

Usage

Arguments

Returns

Method clear()

Step 1: Create a new `Nop` object:

Method `new()`

Method `print()`

Method `set_argument()`

Method `get_argument()`

Method `remove_argument()`

Method `set_optimizer()`

Method `remove_optimizer()`

Method `evaluate()`

Method `optimize()`

Method `test()`

Method `standardize()`

Method `reduce()`

Method `reset_argument()`

Method `continue()`

Method `results()`

Method `number_runs()`

Method `elements_available()`

Method `clear()`

Method `summary()`

Method `optima()`

Method `plot()`

Method `deviation()`

Method `trace()`

Method `best_value()`

Method `best_parameter()`

Method `closest_parameter()`

Method `clone()`