Objective: Specify objective function object
In optimizeR: Unified Framework for Numerical Optimizers
Objective R Documentation
Specify objective function object
Description
The Objective object specifies the framework for an objective
function for numerical optimization.
Value
An Objective object.
Active bindings
objective_name[character(1)]
The label for the objective function.
fixed_arguments[character(), read-only]
The name(s) of the fixed argument(s) (if any).
seconds[numeric(1)]
A time limit in seconds. Computations are interrupted
prematurely if seconds is exceeded.
No time limit if seconds = Inf (the default).
Note the limitations documented in setTimeLimit.
hide_warnings[logical(1)]
Hide warnings when evaluating the objective function?
verbose[logical(1)]
Print status messages?
npar[integer(), read-only]
The length of each target argument.
target[character(), read-only]
The argument name(s) to optimize.
gradient_specified[logical(1), read-only]
Whether a gradient function has been specified via $set_gradient().
hessian_specified[logical(1), read-only]
Whether a Hessian function has been specified via $set_hessian().
Methods
Public methods
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Method new()
Creates a new Objective object.
Usage
Objective$new(f, target = NULL, npar, ...)
Arguments
f[function]
A function to be optimized.
It is expected that f has at least one numeric argument.
Also, the return value of f is expected to have the
structure numeric(1), i.e. a single numeric value.
target[character()]
The argument name(s) to optimize.
All target arguments must be numeric.
If NULL (default), the first function argument is selected.
npar[integer()]
The length of each target argument, i.e., the length(s) of the
numeric vector argument(s) specified by target.
...Optionally additional function arguments that are fixed during the
optimization.
Method set_argument()
Set a function argument that remains fixed during optimization.
Usage
Objective$set_argument(..., .overwrite = TRUE, .verbose = self$verbose)
Arguments
...Optionally additional function arguments that are fixed during the
optimization.
.overwrite[logical(1)]
Overwrite existing values?
.verbose[logical(1)]
Print status messages?
Method get_argument()
Get a fixed function argument.
Usage
Objective$get_argument(argument_name, .verbose = self$verbose)
Arguments
argument_name[character(1)]
A function argument name.
.verbose[logical(1)]
Print status messages?
Method remove_argument()
Remove a fixed function argument.
Usage
Objective$remove_argument(argument_name, .verbose = self$verbose)
Arguments
argument_name[character(1)]
A function argument name.
.verbose[logical(1)]
Print status messages?
Method set_gradient()
Set a gradient function.
Usage
Objective$set_gradient(
gradient,
target = self$target,
npar = self$npar,
...,
.verbose = self$verbose
)
Arguments
gradient[function]
A function that computes the gradient of the objective function
f.
It is expected that gradient has the same call as f, and
that gradient returns a numeric vector of length
sum(self$npar).
target[character()]
The argument name(s) to optimize.
All target arguments must be numeric.
If NULL (default), the first function argument is selected.
npar[integer()]
The length of each target argument, i.e., the length(s) of the
numeric vector argument(s) specified by target.
...Optionally additional function arguments that are fixed during the
optimization.
.verbose[logical(1)]
Print status messages?
Method set_hessian()
Set a Hessian function.
Usage
Objective$set_hessian(
hessian,
target = self$target,
npar = self$npar,
...,
.verbose = self$verbose
)
Arguments
hessian[function]
A function that computes the Hessian of the objective function
f.
It is expected that hessian has the same call as f, and
that hessian returns a numeric matrix of dimension
sum(self$npar) times sum(self$npar).
target[character()]
The argument name(s) to optimize.
All target arguments must be numeric.
If NULL (default), the first function argument is selected.
npar[integer()]
The length of each target argument, i.e., the length(s) of the
numeric vector argument(s) specified by target.
...Optionally additional function arguments that are fixed during the
optimization.
.verbose[logical(1)]
Print status messages?
Method evaluate()
Evaluate the objective function.
Usage
Objective$evaluate(
.at,
.negate = FALSE,
.gradient_as_attribute = FALSE,
.gradient_attribute_name = "gradient",
.gradient_numeric = FALSE,
.hessian_as_attribute = FALSE,
.hessian_attribute_name = "hessian",
.hessian_numeric = FALSE,
...
)
Arguments
.at[numeric()]
The values for the target argument(s), written in a single vector.
Must be of length sum(self$npar).
.negate[logical(1)]
Negate the function return value?
.gradient_as_attribute[logical(1)]
Add the value of the gradient function as an attribute to the output?
The attribute name is defined via the .gradient_attribute_name
argument.
Ignored if $gradient_specified and .gradient_numeric are FALSE.
.gradient_attribute_name[character(1)]
Only relevant if .gradient_as_attribute = TRUE.
In that case, the attribute name for the gradient (if available).
.gradient_numeric[logical(1)]
Calculate the gradient via the numerical approximation
grad?
.hessian_as_attribute[logical(1)]
Add the value of the Hessian function as an attribute to the output?
The attribute name is defined via the .hessian_attribute_name
argument.
Ignored if $hessian_specified and hessian_numeric are FALSE.
.hessian_attribute_name[character(1)]
Only relevant if .hessian_as_attribute = TRUE.
In that case, the attribute name for the Hessian (if available).
.hessian_numeric[logical(1)]
Calculate the Hessian via the numerical approximation
hessian?
...Optionally additional function arguments that are fixed during the
optimization.
Method evaluate_gradient()
Evaluate the gradient function.
Returns a numeric vector of NA_real_ values if the
evaluation fails or returns an unexpected result.
Usage
Objective$evaluate_gradient(.at, .negate = FALSE, ...)
Arguments
.at[numeric()]
The values for the target argument(s), written in a single vector.
Must be of length sum(self$npar).
.negate[logical(1)]
Negate the function return value?
...Optionally additional function arguments that are fixed during the
optimization.
Method evaluate_gradient_numeric()
Evaluate the numerical gradient grad.
Returns a numeric vector of NA_real_ values if the
calculation fails or returns an unexpected result.
Usage
Objective$evaluate_gradient_numeric(.at, .negate = FALSE, ...)
Arguments
.at[numeric()]
The values for the target argument(s), written in a single vector.
Must be of length sum(self$npar).
.negate[logical(1)]
Negate the function return value?
...Optionally additional function arguments that are fixed during the
optimization.
Method evaluate_hessian()
Evaluate the Hessian function.
Returns an NA_real_ value or matrix if the evaluation fails or
returns an unexpected result.
Usage
Objective$evaluate_hessian(.at, .negate = FALSE, ...)
Arguments
.at[numeric()]
The values for the target argument(s), written in a single vector.
Must be of length sum(self$npar).
.negate[logical(1)]
Negate the function return value?
...Optionally additional function arguments that are fixed during the
optimization.
Method evaluate_hessian_numeric()
Evaluate the numerical Hessian hessian.
Returns an NA_real_ matrix if the calculation fails or returns an
unexpected result.
Usage
Objective$evaluate_hessian_numeric(.at, .negate = FALSE, ...)
Arguments
.at[numeric()]
The values for the target argument(s), written in a single vector.
Must be of length sum(self$npar).
.negate[logical(1)]
Negate the function return value?
...Optionally additional function arguments that are fixed during the
optimization.
Method print()
Print details of the Objective object.
Usage
Objective$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
Objective$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
### define log-likelihood function of Gaussian mixture model
llk <- function(mu, sd, lambda, data) {
sd <- exp(sd)
lambda <- plogis(lambda)
cluster_1 <- lambda * dnorm(data, mu[1], sd[1])
cluster_2 <- (1 - lambda) * dnorm(data, mu[2], sd[2])
sum(log(cluster_1 + cluster_2))
}
### optimize over the first three arguments, the 'data' argument is constant
objective <- Objective$new(
f = llk, target = c("mu", "sd", "lambda"), npar = c(2, 2, 1),
data = faithful$eruptions
)
### evaluate at 1:5 (1:2 is passed to mu, 3:4 to sd, and 5 to lambda)
objective$evaluate(1:5)
optimizeR documentation built on May 9, 2026, 5:08 p.m.
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| Objective | R Documentation |
Specify objective function object
Description
The Objective object specifies the framework for an objective
function for numerical optimization.
Value
An Objective object.
Active bindings
objective_name[
character(1)]
The label for the objective function.fixed_arguments[
character(), read-only]
The name(s) of the fixed argument(s) (if any).seconds[
numeric(1)]
A time limit in seconds. Computations are interrupted prematurely ifsecondsis exceeded.No time limit if
seconds = Inf(the default).Note the limitations documented in
setTimeLimit.hide_warnings[
logical(1)]
Hide warnings when evaluating the objective function?verbose[
logical(1)]
Print status messages?npar[
integer(), read-only]
The length of each target argument.target[
character(), read-only]
The argument name(s) to optimize.gradient_specified[
logical(1), read-only]
Whether a gradient function has been specified via$set_gradient().hessian_specified[
logical(1), read-only]
Whether a Hessian function has been specified via$set_hessian().
Methods
Public methods
Method new()
Creates a new Objective object.
Usage
Objective$new(f, target = NULL, npar, ...)
Arguments
f[
function]
Afunctionto be optimized.It is expected that
fhas at least onenumericargument.Also, the return value of
fis expected to have the structurenumeric(1), i.e. a singlenumericvalue.target[
character()]
The argument name(s) to optimize.All target arguments must be
numeric.If
NULL(default), the first function argument is selected.npar[
integer()]
The length of each target argument, i.e., the length(s) of thenumericvectorargument(s) specified bytarget....Optionally additional function arguments that are fixed during the optimization.
Method set_argument()
Set a function argument that remains fixed during optimization.
Usage
Objective$set_argument(..., .overwrite = TRUE, .verbose = self$verbose)
Arguments
...Optionally additional function arguments that are fixed during the optimization.
.overwrite[
logical(1)]
Overwrite existing values?.verbose[
logical(1)]
Print status messages?
Method get_argument()
Get a fixed function argument.
Usage
Objective$get_argument(argument_name, .verbose = self$verbose)
Arguments
argument_name[
character(1)]
A function argument name..verbose[
logical(1)]
Print status messages?
Method remove_argument()
Remove a fixed function argument.
Usage
Objective$remove_argument(argument_name, .verbose = self$verbose)
Arguments
argument_name[
character(1)]
A function argument name..verbose[
logical(1)]
Print status messages?
Method set_gradient()
Set a gradient function.
Usage
Objective$set_gradient( gradient, target = self$target, npar = self$npar, ..., .verbose = self$verbose )
Arguments
gradient[
function]
Afunctionthat computes the gradient of the objective functionf.It is expected that
gradienthas the same call asf, and thatgradientreturns anumericvectorof lengthsum(self$npar).target[
character()]
The argument name(s) to optimize.All target arguments must be
numeric.If
NULL(default), the first function argument is selected.npar[
integer()]
The length of each target argument, i.e., the length(s) of thenumericvectorargument(s) specified bytarget....Optionally additional function arguments that are fixed during the optimization.
.verbose[
logical(1)]
Print status messages?
Method set_hessian()
Set a Hessian function.
Usage
Objective$set_hessian( hessian, target = self$target, npar = self$npar, ..., .verbose = self$verbose )
Arguments
hessian[
function]
Afunctionthat computes the Hessian of the objective functionf.It is expected that
hessianhas the same call asf, and thathessianreturns anumericmatrixof dimensionsum(self$npar)timessum(self$npar).target[
character()]
The argument name(s) to optimize.All target arguments must be
numeric.If
NULL(default), the first function argument is selected.npar[
integer()]
The length of each target argument, i.e., the length(s) of thenumericvectorargument(s) specified bytarget....Optionally additional function arguments that are fixed during the optimization.
.verbose[
logical(1)]
Print status messages?
Method evaluate()
Evaluate the objective function.
Usage
Objective$evaluate( .at, .negate = FALSE, .gradient_as_attribute = FALSE, .gradient_attribute_name = "gradient", .gradient_numeric = FALSE, .hessian_as_attribute = FALSE, .hessian_attribute_name = "hessian", .hessian_numeric = FALSE, ... )
Arguments
.at[
numeric()]
The values for the target argument(s), written in a single vector.Must be of length
sum(self$npar)..negate[
logical(1)]
Negate the function return value?.gradient_as_attribute[
logical(1)]
Add the value of the gradient function as an attribute to the output?The attribute name is defined via the
.gradient_attribute_nameargument.Ignored if
$gradient_specifiedand.gradient_numericareFALSE..gradient_attribute_name[
character(1)]
Only relevant if.gradient_as_attribute = TRUE.In that case, the attribute name for the gradient (if available).
.gradient_numeric[
logical(1)]
Calculate the gradient via the numerical approximationgrad?.hessian_as_attribute[
logical(1)]
Add the value of the Hessian function as an attribute to the output?The attribute name is defined via the
.hessian_attribute_nameargument.Ignored if
$hessian_specifiedandhessian_numericareFALSE..hessian_attribute_name[
character(1)]
Only relevant if.hessian_as_attribute = TRUE.In that case, the attribute name for the Hessian (if available).
.hessian_numeric[
logical(1)]
Calculate the Hessian via the numerical approximationhessian?...Optionally additional function arguments that are fixed during the optimization.
Method evaluate_gradient()
Evaluate the gradient function.
Returns a numeric vector of NA_real_ values if the
evaluation fails or returns an unexpected result.
Usage
Objective$evaluate_gradient(.at, .negate = FALSE, ...)
Arguments
.at[
numeric()]
The values for the target argument(s), written in a single vector.Must be of length
sum(self$npar)..negate[
logical(1)]
Negate the function return value?...Optionally additional function arguments that are fixed during the optimization.
Method evaluate_gradient_numeric()
Evaluate the numerical gradient grad.
Returns a numeric vector of NA_real_ values if the
calculation fails or returns an unexpected result.
Usage
Objective$evaluate_gradient_numeric(.at, .negate = FALSE, ...)
Arguments
.at[
numeric()]
The values for the target argument(s), written in a single vector.Must be of length
sum(self$npar)..negate[
logical(1)]
Negate the function return value?...Optionally additional function arguments that are fixed during the optimization.
Method evaluate_hessian()
Evaluate the Hessian function.
Returns an NA_real_ value or matrix if the evaluation fails or
returns an unexpected result.
Usage
Objective$evaluate_hessian(.at, .negate = FALSE, ...)
Arguments
.at[
numeric()]
The values for the target argument(s), written in a single vector.Must be of length
sum(self$npar)..negate[
logical(1)]
Negate the function return value?...Optionally additional function arguments that are fixed during the optimization.
Method evaluate_hessian_numeric()
Evaluate the numerical Hessian hessian.
Returns an NA_real_ matrix if the calculation fails or returns an
unexpected result.
Usage
Objective$evaluate_hessian_numeric(.at, .negate = FALSE, ...)
Arguments
.at[
numeric()]
The values for the target argument(s), written in a single vector.Must be of length
sum(self$npar)..negate[
logical(1)]
Negate the function return value?...Optionally additional function arguments that are fixed during the optimization.
Method print()
Print details of the Objective object.
Usage
Objective$print()
Method clone()
The objects of this class are cloneable with this method.
Usage
Objective$clone(deep = FALSE)
Arguments
deepWhether to make a deep clone.
Examples
### define log-likelihood function of Gaussian mixture model
llk <- function(mu, sd, lambda, data) {
sd <- exp(sd)
lambda <- plogis(lambda)
cluster_1 <- lambda * dnorm(data, mu[1], sd[1])
cluster_2 <- (1 - lambda) * dnorm(data, mu[2], sd[2])
sum(log(cluster_1 + cluster_2))
}
### optimize over the first three arguments, the 'data' argument is constant
objective <- Objective$new(
f = llk, target = c("mu", "sd", "lambda"), npar = c(2, 2, 1),
data = faithful$eruptions
)
### evaluate at 1:5 (1:2 is passed to mu, 3:4 to sd, and 5 to lambda)
objective$evaluate(1:5)
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