Optimizer | R Documentation |
The Optimizer
object defines a numerical optimizer based on any
optimization algorithm implemented in R. The main advantage of working with
an Optimizer
object instead of using the optimization function
directly lies in the standardized inputs and outputs.
Any R function that fulfills the following four constraints can be defined as
an Optimizer
object:
It must have an input for a function
, the objective function to be
optimized.
It must have an input for a numeric
vector, the initial values from
where the optimizer starts.
It must have a ...
argument for additional parameters passed on to
the objective function.
The output must be a named list
, including the optimal function
value and the optimal parameter vector.
label
[character(1)
]
The label for the optimizer.
algorithm
[function
]
The optimization algorithm.
arg_objective
[character(1)
]
The argument name for the objective function in algorithm
.
arg_initial
[character(1)
]
The argument name for the initial values in algorithm
.
arg_lower
[character(1)
| NA
]
Optionally the argument name for the lower parameter bound in
algorithm
.
Can be NA
if not available.
arg_upper
[character(1)
| NA
]
Optionally the argument name for the upper parameter bound in
algorithm
.
Can be NA
if not available.
arg_gradient
[character(1)
| NA
]
Optionally the argument name for the gradient function in
algorithm
.
Can be NA
if not available.
arg_hessian
[character(1)
| NA
]
Optionally the argument name for the Hessian function in
algorithm
.
Can be NA
if not available.
gradient_as_attribute
[logical(1)
]
Only relevant if arg_gradient
is not NA
.
In that case, does algorithm
expect that the gradient is an attribute
of the objective function output (as for example in
nlm
)? In that case, arg_gradient
defines the
attribute name.
hessian_as_attribute
[logical(1)
]
Only relevant if arg_hessian
is not NA
.
In that case, does algorithm
expect that the Hessian is an attribute
of the objective function output (as for example in
nlm
)? In that case, arg_hessian
defines the
attribute name.
out_value
[character(1)
]
The element name for the optimal function value in the output list
of algorithm
.
out_parameter
[character(1)
]
The element name for the optimal parameters in the output list
of
algorithm
.
direction
[character(1)
]
Either "min"
(if the optimizer minimizes) or "max"
(if the optimizer maximizes).
arguments
[list()
]
Custom arguments for algorithm
.
Defaults are used for arguments that are not specified.
seconds
[numeric(1)
]
A time limit in seconds.
Optimization is 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 during optimization?
output_ignore
[character()
]
Elements to ignore (not include) in the optimization output.
new()
Initializes a new Optimizer
object.
Optimizer$new(which, ..., .verbose = TRUE)
which
[character(1)
]
Either:
one of optimizer_dictionary$keys
or "custom"
, in which case $definition()
must be used to
define the optimizer details.
...
[any
]
Optionally additional named arguments to be passed to the optimizer
algorithm. Without specifications, default values of the optimizer are used.
.verbose
[logical(1)
]
Print status messages?
A new Optimizer
object.
definition()
Defines an optimizer.
Optimizer$definition( algorithm, arg_objective, arg_initial, arg_lower = NA, arg_upper = NA, arg_gradient = NA, arg_hessian = NA, gradient_as_attribute = FALSE, hessian_as_attribute = FALSE, out_value, out_parameter, direction )
algorithm
[function
]
The optimization algorithm.
arg_objective
[character(1)
]
The argument name for the objective function in algorithm
.
arg_initial
[character(1)
]
The argument name for the initial values in algorithm
.
arg_lower
[character(1)
| NA
]
Optionally the argument name for the lower parameter bound in
algorithm
.
Can be NA
if not available.
arg_upper
[character(1)
| NA
]
Optionally the argument name for the upper parameter bound in
algorithm
.
Can be NA
if not available.
arg_gradient
[character(1)
| NA
]
Optionally the argument name for the gradient function in
algorithm
.
Can be NA
if not available.
arg_hessian
[character(1)
| NA
]
Optionally the argument name for the Hessian function in
algorithm
.
Can be NA
if not available.
gradient_as_attribute
[logical(1)
]
Only relevant if arg_gradient
is not NA
.
In that case, does algorithm
expect that the gradient is an attribute
of the objective function output (as for example in
nlm
)? In that case, arg_gradient
defines the
attribute name.
hessian_as_attribute
[logical(1)
]
Only relevant if arg_hessian
is not NA
.
In that case, does algorithm
expect that the Hessian is an attribute
of the objective function output (as for example in
nlm
)? In that case, arg_hessian
defines the
attribute name.
out_value
[character(1)
]
The element name for the optimal function value in the output list
of algorithm
.
out_parameter
[character(1)
]
The element name for the optimal parameters in the output list
of
algorithm
.
direction
[character(1)
]
Either "min"
(if the optimizer minimizes) or "max"
(if the optimizer maximizes).
set_arguments()
Sets optimizer arguments.
Optimizer$set_arguments(...)
...
[any
]
Optionally additional named arguments to be passed to the optimizer
algorithm. Without specifications, default values of the optimizer are used.
The Optimizer
object.
minimize()
Performing minimization.
Optimizer$minimize(objective, initial, lower = NA, upper = NA, ...)
objective
[function
| Objective
]
A function
to be optimized that
has at least one argument that receives a numeric
vector
and returns a single numeric
value.
Alternatively, it can also be an Objective
object for more
flexibility.
initial
[numeric()
]
Starting parameter values for the optimization.
lower
[NA
| numeric()
| numeric(1)
]
Lower bounds on the parameters.
If a single number, this will be applied to all parameters.
Can be NA
to not define any bounds.
upper
[NA
| numeric()
| numeric(1)
]
Upper bounds on the parameters.
If a single number, this will be applied to all parameters.
Can be NA
to not define any bounds.
...
[any
]
Optionally additional named arguments to be passed to the optimizer
algorithm. Without specifications, default values of the optimizer are used.
A named list
, containing at least these five elements:
value
A numeric
, the minimum function value.
parameter
A numeric
vector, the parameter vector
where the minimum is obtained.
seconds
A numeric
, the optimization time in seconds.
initial
A numeric
, the initial parameter values.
error
Either TRUE
if an error occurred, or FALSE
, else.
Appended are additional output elements of the optimizer.
If an error occurred, then the error message is also appended as element
error_message
.
If the time limit was exceeded, this counts as an error. In addition,
the flag time_out = TRUE
is appended.
Optimizer$new("stats::nlm")$ minimize(objective = function(x) x^4 + 3*x - 5, initial = 2)
maximize()
Performing maximization.
Optimizer$maximize(objective, initial, lower = NA, upper = NA, ...)
objective
[function
| Objective
]
A function
to be optimized that
has at least one argument that receives a numeric
vector
and returns a single numeric
value.
Alternatively, it can also be an Objective
object for more
flexibility.
initial
[numeric()
]
Starting parameter values for the optimization.
lower
[NA
| numeric()
| numeric(1)
]
Lower bounds on the parameters.
If a single number, this will be applied to all parameters.
Can be NA
to not define any bounds.
upper
[NA
| numeric()
| numeric(1)
]
Upper bounds on the parameters.
If a single number, this will be applied to all parameters.
Can be NA
to not define any bounds.
...
[any
]
Optionally additional named arguments to be passed to the optimizer
algorithm. Without specifications, default values of the optimizer are used.
A named list
, containing at least these five elements:
value
A numeric
, the maximum function value.
parameter
A numeric
vector, the parameter vector
where the maximum is obtained.
seconds
A numeric
, the optimization time in seconds.
initial
A numeric
, the initial parameter values.
error
Either TRUE
if an error occurred, or FALSE
, else.
Appended are additional output elements of the optimizer.
If an error occurred, then the error message is also appended as element
error_message
.
If the time limit was exceeded, this also counts as an error. In addition,
the flag time_out = TRUE
is appended.
Optimizer$new("stats::nlm")$ maximize(objective = function(x) -x^4 + 3*x - 5, initial = 2)
optimize()
Performing minimization or maximization.
Optimizer$optimize( objective, initial, lower = NA, upper = NA, direction = "min", ... )
objective
[function
| Objective
]
A function
to be optimized that
has at least one argument that receives a numeric
vector
and returns a single numeric
value.
Alternatively, it can also be an Objective
object for more
flexibility.
initial
[numeric()
]
Starting parameter values for the optimization.
lower
[NA
| numeric()
| numeric(1)
]
Lower bounds on the parameters.
If a single number, this will be applied to all parameters.
Can be NA
to not define any bounds.
upper
[NA
| numeric()
| numeric(1)
]
Upper bounds on the parameters.
If a single number, this will be applied to all parameters.
Can be NA
to not define any bounds.
direction
[character(1)
]
Either "min"
for minimization or "max"
for maximization.
...
[any
]
Optionally additional named arguments to be passed to the optimizer
algorithm. Without specifications, default values of the optimizer are used.
A named list
, containing at least these five elements:
value
A numeric
, the maximum function value.
parameter
A numeric
vector, the parameter vector
where the maximum is obtained.
seconds
A numeric
, the optimization time in seconds.
initial
A numeric
, the initial parameter values.
error
Either TRUE
if an error occurred, or FALSE
, else.
Appended are additional output elements of the optimizer.
If an error occurred, then the error message is also appended as element
error_message
.
If the time limit was exceeded, this also counts as an error. In addition,
the flag time_out = TRUE
is appended.
objective <- function(x) -x^4 + 3*x - 5 optimizer <- Optimizer$new("stats::nlm") optimizer$optimize(objective = objective, initial = 2, direction = "min") optimizer$optimize(objective = objective, initial = 2, direction = "max")
print()
Prints the optimizer label.
Optimizer$print(...)
...
[any
]
Optionally additional named arguments to be passed to the optimizer
algorithm. Without specifications, default values of the optimizer are used.
Invisibly the Optimizer
object.
clone()
The objects of this class are cloneable with this method.
Optimizer$clone(deep = FALSE)
deep
Whether to make a deep clone.
### Task: compare minimization with 'stats::nlm' and 'pracma::nelder_mead'
# 1. define objective function and initial values
objective <- TestFunctions::TF_ackley
initial <- c(3, 3)
# 2. get overview of optimizers available in dictionary
optimizer_dictionary$keys
# 3. define 'nlm' optimizer
nlm <- Optimizer$new(which = "stats::nlm")
# 4. define the 'pracma::nelder_mead' optimizer (not contained in the dictionary)
nelder_mead <- Optimizer$new(which = "custom")
nelder_mead$definition(
algorithm = pracma::nelder_mead, # the optimization function
arg_objective = "fn", # the argument name for the objective function
arg_initial = "x0", # the argument name for the initial values
out_value = "fmin", # the element for the optimal function value in the output
out_parameter = "xmin", # the element for the optimal parameters in the output
direction = "min" # the optimizer minimizes
)
# 5. compare the minimization results
nlm$minimize(objective, initial)
nelder_mead$minimize(objective, initial)
## ------------------------------------------------
## Method `Optimizer$minimize`
## ------------------------------------------------
Optimizer$new("stats::nlm")$
minimize(objective = function(x) x^4 + 3*x - 5, initial = 2)
## ------------------------------------------------
## Method `Optimizer$maximize`
## ------------------------------------------------
Optimizer$new("stats::nlm")$
maximize(objective = function(x) -x^4 + 3*x - 5, initial = 2)
## ------------------------------------------------
## Method `Optimizer$optimize`
## ------------------------------------------------
objective <- function(x) -x^4 + 3*x - 5
optimizer <- Optimizer$new("stats::nlm")
optimizer$optimize(objective = objective, initial = 2, direction = "min")
optimizer$optimize(objective = objective, initial = 2, direction = "max")
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